http://gcrinstitute.org

2020 Survey of Artificial General Intelligence Projects
for Ethics, Risk, and Policy

McKenna Fitzgerald, Aaron Boddy, & Seth D. Baum
Global Catastrophic Risk Institute

Cite as: McKenna Fitzgerald, Aaron Boddy, and Seth D. Baum, 2020. 2020 Survey of Artificial
General Intelligence Projects for Ethics, Risk, and Policy. Global Catastrophic Risk InstituteTechnical Report 20-1.

Technical reports are published to share ideas and promote discussion. They have not necessarilygone through peer review. The views therein are the authors’ and are not necessarily the views of the
Global Catastrophic Risk Institute.

Executive Summary
Artificial general intelligence (AGI) is artificial intelligence (AI) that can reason across a wide range ofdomains. While most AI research and development (R&D) deals with narrow AI, not AGI, there issome dedicated AGI R&D. If AGI is built, its impacts could be profound. Depending on how it isdesigned and used, it could either help solve the world’s problems or cause catastrophe, possibly evenhuman extinction.
This paper presents a survey of AGI R&D projects that are active in 2020 and updates a previous
survey of projects active in 2017. Both surveys attempt to identify every active AGI R&D project andcharacterize them in terms of relevance to ethics, risk, and policy, focusing on seven attributes:
 The type of institution in which the project is based Whether the project publishes open-source code Whether the project has military connections
 The nation(s) in which the project is based The project’s goals for its AGI
 The extent of the project’s engagement with AGI safety issues
 The overall size of the project
The surveys use openly published information as found in scholarly publications, project websites,
popular media articles, and other websites. The 2020 survey uses information from the 2017 survey aswell as the past three years of the Journal of Artificial General Intelligence, the past three years of AGI
conference proceedings (the 2017 survey covered prior content from the Journal of Artificial GeneralIntelligence and the AGI conference proceedings), keyword searches in Google web search, Google
Scholar, Crunchbase, GitHub, the authors’ prior knowledge, suggestions from readers of the 2017
survey, and additional literature and webpages identified via all of the above. The use of Crunchbase,
GitHub, and reader suggestions is new to the 2020 survey.
The 2020 survey has two main findings. First, the expanded search methodology has identified a large
number of new AGI R&D projects, bringing a more comprehensive picture of the field of AGI R&D.
Second, accounting for the expanded search methodology, there has been little change in the field of
AGI R&D between 2017 and 2020. Some 2017 projects are now inactive and some new projects haveemerged in 2020, but the overall picture is largely the same. Specific trends are as follows:

The 2020 survey identifies 72 active AGI R&D projects spread across 37 countries. The 2017
survey identified 45 projects in 30 countries. The 2020 survey updates the 2017 dataset, finding
70 projects that were active in 36 countries in 2017, 57 of which remain active in 2020.



Relative to the 2017 survey, the AGI R&D projects presented in the 2020 survey tend to besmaller, more geographically diverse, less open-source, less focused on intellectual goals, more
focused on humanitarian goals, and more concentrated in private corporations.



The 2020 survey presents some small changes between 2017 and 2020 projects: a decrease inacademic projects, an increase in private corporation projects, an increase in projects stating
humanitarian goals, a decrease in projects with military connections, and a decrease in projects
based in the United States. Other project attributes have been approximately constant.

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Looking at just the 72 projects active in 2020, the trends can be summarized as follows:

Many of the projects are interconnected via common personnel, common parent organizations,or project collaboration.



About half of the projects are in private corporations. Academic institutions are the secondmost common institution type.



About half of the projects publish open-source code and about half do not.



Only nine projects have identifiable military connections. The connections mostly involve basic
research and sometimes involve tactical military applications. No major strategic military AGI
R&D activities were identified.



Almost half of the projects are based in the US, and most are based in a country allied with the
US. Five projects are based in China. 20 projects are multinational, including six projects that
operate in both China and the US.

More than half of the projects state humanitarian goals. The second-most common type ofstated goal is intellectual. Many projects have an orientation toward commercializing AGI as a
product, though few of these explicitly state profit as a goal.



Most projects are not active on AGI safety issues, and some are openly dismissive of AGI
safety concerns, though some others have a significant emphasis on safety.



Most projects are in the small-to-medium size range. The four largest projects are BlueBrain(an academic project based in Lausanne, Switzerland), DeepMind (a Google project based in
London), the Human Brain Project (an academic project also based in Lausanne), and OpenAI(a nonprofit based in San Francisco). The largest projects are over 100 times larger than thesmallest projects as measured in terms of full-time project personnel.

Looking across multiple attributes, some additional trends are apparent:

There is a cluster of corporate projects are active on safety and state that their goals are tobenefit humanity (i.e., is humanitarian).



Another cluster is of academic projects that are not active on safety and state intellectual goals.
Many of these projects have funding from military research agencies such as DARPA.



Projects new to the 2020 survey are mostly small-to-medium-sized private corporations, manyof which articulate humanitarian goals and are oriented toward commercializing AGI as a
product without stating profit as a goal.

Additionally, the 2020 survey identifies, for the first time, a preponderance of projects that state afocus on AGI but demonstrate no other AGI activity. These projects are counted separately from the 72
projects described above.
Figure ES1 on the next page presents an overview of the data.
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Humanitarian

Both
LIDA

MIL

AGi3

AGI Laboratory

EngagedOn Safety

Corporate

OpenAI
Vicarious
Cyc
Core AIMIL

Unspecified
Animats

MIL

Corporate-HumanitarianEngaged On Safety Cluster

SusaroIntelligent Artifacts
New Sapience

Other

NARS
LeabraCommAI

Profit

FLOWERSAERA

AcademicIntellectualist

GoodAI
Apollo Programfor AGINNAISENSE

AGi3

Brain Simulator II
Cognitive
Science &MonadSolutions

AGI Laboratory
SusaroMauhn

Fairy Tale AGIS
Fairy Tale AGIS

DeepBrainz

DeepMind
Other

OpenAI
Whole Brain
Architecture Initiative

OpenCog

FLOWERS

Academic-Intellectualist-Not
Engaged On Safety Cluster

US academic military sub-cluster

BinaryNeurons
Network

Big MotherNot Engaged
On Safety

Academic

CorporateMostly Small- Human
Brain
Not Engaged
Project
On Safety
Sigma
MIL
ClusterBlue Brain ACT-R
CLARION
Human Sigma MIL
SOAR
Brain
Project

AIXI

NiHa

Corporate

Graphen

Nigel

ORBAI

Prome

Mindtrace

Cerenaut
Research
HTM

Olbrain

SingularityNet

Sanctuary AI
True Brain Computing

AstrumAIBrainMSR AI
SOAR

ANSNA

MIL

Curious AI
XephorSolutionsBrain2Bot

SingularityNet

M3-CLIC

Baidu Research
Tencent AI Lab
Mind Simulation
Omega
Optimizing Mind

Uber AI Labs

Other

Drayker

He4oSingularityNet

China
Brain Project MILWILLIAM

Research Center
for Brain-Inspired MILIntelligence

NDEYSS

SingularityNet

AGI Brain
Robot Brain Project

MARAGI

Figure ES1. Overview of the 72 identified AGI R&D projects characterized according to 7 attributes:
Institution type: academic (mainly universities), corporate (public and private corporations),
and other (government, nonprofit, and projects not affiliated with any institution).

Open-source code: blue background indicates code available open-source; red backgroundindicates code available upon request.

Military connections: projects labeled MIL have military connections.



Nationality: Flags indicate the country in which a project is based.
Australia,
Austria, Belgium,
Brazil,Canada,China,
Czech
Republic,
Finland,France,Iceland,
India,
Iran,
Ireland,Japan,
Netherlands,
Pakistan,
Russia,Sweden, Switzerland,
Turkey,Ukraine,UK,USA,
no project location identifiedStated goals: animal welfare, ecocentrism, humanitarian, intellectualism, profit,
transhumanism, and unspecified (when project goals could not be identified).



Engagement on safety: engaged projects either actively try to make their AGI safe or state thatthey support other AGI safety work; projects that are not engaged on safety either openly
dismiss concerns about AGI safety or have no publicly stated activity on safety.



Size: font size of project names indicates project size.
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The data suggest the following conclusions:Regarding ethics, the major trend is that projects are split between stated goals of benefiting humanity
and advancing knowledge, with the former largely from corporate projects and the latter largely fromacademic projects. While these are not the only goals that projects articulate, there appears to be aloose consensus among projects to aim for some combination of these two goals. The 2020 survey
finds a small increase in the number of projects with humanitarian goals and a small decrease in thenumber of projects with intellectual goals. The 2020 survey also finds a large increase in the number ofcorporate projects, which are often motivated by profit even if they do not explicitly say that their goal
is to generate profit.
Regarding risk, in particular the risk of AGI catastrophe, there is good news and bad news.
Unfortunately, most projects are not adequately addressing AGI safety issues. Academic projects inparticular have done relatively little to address safety issues, a trend that continues from 2017. The
proliferation of corporate projects since 2017 heightens the concern that these projects could put profit
ahead of safety and the public interest. Fortunately, however, there is much potential for projects tocooperate on safety issues, thanks to the partial consensus on goals, the concentration of projects in the
US and its allies, and the various interconnections between different projects. Additionally, the absenceof large government projects suggests that, at this time, no government is aggressively pursuing AGI
for its strategic advantage.
Regarding policy, several conclusions can be drawn. First, the concentration of projects in the US and
its allies could greatly facilitate the establishment of international public policy for AGI. Second, thelarge and growing number of corporate projects suggests an urgent need to attend to the corporate
governance and political economy of AGI R&D. Third, the smaller but still significant number ofacademic projects suggests that research policy institutions, such as review boards that evaluate risky
research, have an important role to play. Fourth, the large number of projects with open-source code
presents a policy challenge because it makes it AGI R&D accessible to anyone anywhere in the world.
Fifth, the large difference in size between the largest and smallest projects suggests that policymaking
may benefit from a focus on larger projects. Finally, the absence of large government projects suggeststhat the primary role of governments may be as regulators of private-sector AGI R&D rather than asdrivers of AGI R&D.
This study has some limitations, meaning that the actual state of AGI R&D may differ from what ispresented here. The survey is based exclusively on openly published information and projects weresought out primarily using the English language. It is possible that this survey missed some AGI R&D
projects. Data collection occurred primarily between June and September 2020 and may have missed
more recent project information. The 72 projects identified by this survey therefore represent a lower
bound on the number of existing projects. Furthermore, projects’ actual attributes may differ fromthose found in openly published descriptions of those projects. For example, profit may be a goal of
corporate projects even though most corporate projects did not state that profit was one of their goals.
Therefore, this study’s results should not be assumed to necessarily reflect the actual current state of
AGI R&D. That said, the study nonetheless improves upon the 2017 survey and provides what is now
the most thorough description yet of AGI R&D in terms of ethics, risk, and policy.
Finally, this document, presenting the 2020 survey, is largely based on the document that presented the2017 survey. Much of the content remains the same, including the overall report design and various
specific passages of text.
5

Acknowledgments
Kevin Chow, Maximilian Cornell, Fadi Al-Salti, and Daniel Johnston provided research assistance.
Ben Goertzel, Gordon Irlam, Luke Muehlhauser, Robert de Neufville, Olga Afanasjeva, Andrea Owe,
Tony Barrett, Brian Tse, Yawen Duan, Kwan Yee Ng, Justin Shovelain, and Marek Rosa provided
feedback on previous drafts. Lloyd Harry-Davis provided assistance with copy editing. Melissa
Thomas-Baum provided assistance with graphic design. Olga Afanasjeva, Colin Hales, and Marek
Rosa suggested AGI R&D projects upon reading the 2017 version of this survey. Any lingering errors
or other shortcomings are the authors’ alone.
This work was made possible by a grant from the Gordon R. Irlam Charitable Foundation. The viewsin this paper are the authors’ alone and do not necessarily reflect the views of the Global Catastrophic
Risk Institute or the Gordon R. Irlam Charitable Foundation.

6

Contents
Main text
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8

2. Prior Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3. Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5. Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Appendix 1. Active AGI Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Projects that are active in 2020.
Appendix 2. Inactive AGI Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Projects that were active in 2017, are inactive in 2020, and for which there is new coding information.
Appendix 3. Other Notable Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Projects with some notable relation to AGI R&D. Includes projects (1) that were identified in the 2017
survey, are inactive in 2020, and for which there is no new coding information, (2) projects that workon technical aspects of AGI but are not working towards building AGI, such as projects working on
hardware or safety mechanisms that can be used for AGI, and (3) select narrow AI projects, such as AI
groups at major technology companies.
Appendix 4. Other Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
Projects that state that they have a focus on AGI but demonstrate little identifiable activity on AGI.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

7

1. Introduction
Artificial general intelligence (AGI) is artificial intelligence (AI) that can reason across a wide range ofdomains, much like the human mind. Most AI is domain-specific and can only reason within aparticular field or set of tasks. For example, DeepBlue can beat Garry Kasparov at chess, and maybe ata few other basic tasks like multiplication, but it cannot beat him at anything else. This is an exampleof narrow AI, not AGI. AGI was initially a primary goal of AI research and has long been considered
its “grand dream” or “holy grail.”1 The technical difficulty of building AGI has led most of the field tofocus on narrower, more immediately practical forms of AI, but some dedicated AGI research anddevelopment (R&D) continues.
AGI is also a profound societal concern. Or, rather, it could be if it is built. AGI could complementhuman intellect and increase the world’s capacity to solve its problems. Or it could be used maliciously
in a power play by whoever controls it. Or humanity may fail to control it, possibly resulting incatastrophe. Any AI can outsmart humans in some domains (e.g., multiplication), but an AGI may beable to outsmart humans in all domains. In that case, the outcome could depend on the AGI’s goals:
whether it seeks to benefit humanity, the world, itself, or to pursue some other goal entirely. Indeed,scholars of AGI sometimes propose that it could cause human extinction or a similar catastrophe (seeliterature review below).
The high potential stakes of AGI raise questions of ethics, risk, and policy. Which AGI, if any,
should be built? What is the risk of catastrophe if an AGI is built? What policy options are available to
avoid AGI catastrophe and, if desired, to develop safe and beneficial AGI? These are all questions
under active investigation. However, the literature to date has tended to be theoretical and speculative,
and has also tended to have little basis in the actual state of affairs in AGI R&D. Since AGI may not bebuilt for many years, it is inevitable that the AGI literature will be somewhat speculative. Nevertheless,
AGI R&D is happening right now. Information about current R&D can guide decisions about today’s
ethics, risk, and policy, as well as provide insight into what future R&D might look like.
This paper presents an update to the 2017 survey of active AGI R&D projects in terms of theirethical, risk, and policy dimensions (Baum 2017a). There have been several prior surveys of R&D on
AGI and related technologies (Chong et al. 2007; Duch et al. 2008; Langley et al. 2008; de Garis et al.
2010; Goertzel et al. 2010; Samsonovich 2010; Taatgen and Anderson 2010; Thórisson and Helgasson
2012; Dong and Franklin 2014; Goertzel 2014; Kotseruba et al. 2016). However, these are all surveys
on the technical aspects of AGI, such as how the AGI itself is designed, what progress has been madeon it, and how promising the various approaches are for achieving AGI.
This paper presents and analyzes information of relevance to ethics, risk, and policy—for example,
which political jurisdictions projects are located in and how engaged they are with AGI safety issues.
Additionally, whereas prior surveys focus on select noteworthy examples of AGI R&D projects, thispaper attempts to document all currently existing projects. In order to answer many questions related to
ethics, risk, and policy, it is important to survey the entire range of projects—for example, to know
which political jurisdictions should be included in AGI public policy.
Section 1.1 explains terminology related to AGI. Section 2 reviews prior literature on AGI ethics,
risk, and policy. Section 3 presents the research questions pursued in this study. Section 4 summarizes
the methodology used for this paper’s survey. Section 5 presents the main results of the survey of AGI
R&D projects and other notable projects. Section 6 concludes. Appendix 1 presents a full survey of
AGI R&D projects that are active in 2020. Appendix 2 presents select inactive AGI R&D projects,
specifically those that were active in 2017, are no longer active, and for which there is new coding
information. Appendix 3 presents notable projects that were considered for the 2020 survey but
excluded for not meeting inclusion criteria, including inactive 2017 projects for which there is no new
1

See e.g. Legg (2008, p.125); https://montrealartificialintelligence.com

8

coding information. Appendix 4 presents other projects that have stated a focus on AGI but
demonstrate little further activity on AGI.

1.1 Terminology
AGI is one of several terms used for advanced, and potentially transformative, future AI. The terms
have slightly different meanings, and it is worth briefly distinguishing between them.









AGI is specifically AI with a wide range of intelligence capabilities, including “the ability toachieve a variety of goals, and carry out a variety of tasks, in a variety of different contexts andenvironments” (Goertzel 2014, p.2). AGI is not necessarily advanced—an AI can be general
without being highly sophisticated—though general intelligence requires a certain degree ofsophistication and AGI is often presumed to be highly capable. AGI is also a dedicated field ofstudy, with its own society (http://www.agi-society.org), journal (Journal of Artificial GeneralIntelligence), and conference series (http://agi-conf.org).
Cognitive architecture is the overall structure of an intelligent entity. One can speak of thecognitive architecture of the brains of humans or other animals. However, the term is used mainly
for theoretical and computational models of human and nonhuman animal cognition. Cognitive
architectures can be narrow, focusing on specific cognitive processes such as attention or emotion(Kotseruba et al. 2016). However, they are often general, and thus their study overlaps with thestudy of AGI. Biologically inspired cognitive architectures is a dedicated field of study with its
own society (http://bicasociety.org), journal (Biologically Inspired Cognitive Architectures), andconference series (http://bicasociety.org/meetings).
Brain emulations are computational instantiations of biological brains. Brain emulations are
sometimes classified as distinct from AGI (e.g., Barrett and Baum 2017a). However, they arecomputational entities with general intelligence, and thus this paper treats them as a type of AGI.
Human-level AI is AI with intelligence comparable to humans, or “human-level, reasonably
human-like AGI” (Goertzel 2014, p.6). An important subtlety is that an AGI could be as advanced
as humans, but with a rather different type of intelligence: it does not necessarily mimic humancognition. For example, AI chess programs will use brute force searches in some instances inwhich humans use intuition, yet the AI can still perform at or beyond the human level.
Superintelligence is AI that significantly exceeds human intelligence. The term ultraintelligence
has also been used in this context (Good 1965) but is less common. It is often proposed thatsuperintelligence will come from an initial seed AI that undergoes recursive self-improvement,
becoming successively smarter and smarter. The seed AI would not necessarily be AGI, but it isoften presumed to be.

This paper focuses on AGI because the term is used heavily in R&D contexts, and it is importantfor ethics, risk, and policy. Narrow cognitive architectures (and narrow AI) are less likely to havetransformative consequences for the world. Human-level AI and superintelligence are more likely tohave transformative consequences. However, these terms are not common in R&D. Note that not every
project included in this paper’s survey explicitly identifies as an AGI project, but they all have thepotential to be AGI or contribute to the development of AGI, and thus have potential fortransformative consequences. The survey does not exclude any projects that are explicitly trying tobuild human-level AI or superintelligence.

92. Prior Literature
2.1 Ethics
Perhaps the most fundamental question in AGI ethics is on whether to treat AGI as an intellectual
pursuit or as something that could impact society and the world at large. In other words, is AGI R&D
pursued in order to advance the forefront of knowledge or to benefit society? Often, these two goals
are closely connected, as evidenced by the central role of science and technology in improving living
conditions worldwide. However, these goals are not always connected and can sometimes be at odds.
In particular, for the present study, research into potentially dangerous new technologies can yieldsignificant scientific and intellectual insights, yet end up being harmful to society.
Researchers across all fields of research often have strong intellectual values and motivations, and
AGI is no exception. The question of whether to evaluate research in terms of intellectual merit orbroader societal/global impacts is a point of ongoing contention across academia (Schienke et al.
2009). As with most fields, AI has traditionally emphasized intellectual merits, though there are calls
for this to change (Baum 2018). The intellectual pull of AGI can be particularly strong, given its status
as a long-term “grand dream” or “holy grail.” However, the broader impacts can also have a strong
pull, given the enormous potential stakes of AGI. In practical terms, an AGI project with intellectual
motivations is, relative to a project motivated by broader impacts of AGI, more likely to view building
AGI as a worthy goal in itself and to pay little attention to any potential dangers or other broader
impacts.
The second area of AGI ethics concerns the goals that an AGI should be designed to pursue. This is
the main focus of prior literature on AGI ethics. One line of thinking proposes “indirect normativity”
or “coherent extrapolated volition,” in which the AGI is designed to use its intellect to figure out what
humanity wants it to do (Yudkowsky 2004; Muehlhauser and Helm 2012; Bostrom 2014). This
proposal is motivated in part by procedural justice concerns—everyone, and not just AGI designers,
should have a say in the AGI’s ethics—and in part by concerns about the technical difficulty of
programming the subtleties of human ethics directly into the AGI. The proposal raises questions about
whose values to include (for example, should the values of nonhumans be included?), how to assess
what each individual’s values are, and how to resolve disagreements among individuals (Baum 2020).
An alternative line of thinking proposes that the AGI should create new entities that are morally
superior to humans. This thinking falls in the realm of “transhumanism” or “posthumanism”; AGI
researchers de Garis (2005) and Goertzel (2010) use the term “cosmism.” This view holds that AGI
should benefit the cosmos as a whole, not just humanity, and proposes that morally superior beings
produced by AGI may advance the good of the cosmos. Whereas Goertzel (2010) stresses that “legacy
humans” should be able to decide for themselves whether to continue in this new world, de Garis(2005) suggests that this world may be worth forming even if legacy humans would be eliminated. In
contrast, Yampolskiy (2013) argues that AGI should only be built if they are expendable tools ofbenefit to their human creators.
Finally, there has also been some discussion of whether AGI should be built in the first place,although to date, less attention has been paid to this idea. Most discussions of AGI either support
building it or do not seriously consider whether to build it because they presume that it will inevitably
be built, as discussed by Totschnig (2019). Some arguments against building AGI are rooted inconcerns about catastrophe risk (e.g., Joy 2000); more on risk below. Others argue that even safe AGI
should not be built. These include the fringe anarchist views of Kaczynski (1995, para. 174) and themore sober discussion of Totschnig (2019). However, there has been much less outright opposition to
AGI than there has been to similarly transformative technologies like human enhancement.
102.2 Risk
The potential for AGI catastrophe is rooted in the notion that AGI could come to outsmart humanity,
take control of the planet, and pursue whatever goals it is programmed to pursue. Unless it isprogrammed with goals that are safe for humanity and everything else that matters, the result could becatastrophic. Likewise, in order to avoid catastrophe, AGI R&D projects must take sufficient safety
precautions.
Opinions vary on the size of this risk and the corresponding safety effort required. Some proposethat it is fundamentally challenging to design an AGI with safe goals—that even seemingly minor
mistakes could yield catastrophic results and that therefore AGI R&D projects should be very attentive
to safety (Yudkowsky 2004; Muehlhauser and Helm 2012; Bostrom 2014). Others argue that an AGI
can be trained to have safe goals and that this process is not exceptionally fragile, such that AGI R&D
projects need to attend to safety, but not to an unusual extent (e.g., Goertzel and Pitt 2012; Bieger et al.
2015; Goertzel 2015; 2016; Steunebrink et al. 2016). Finally, some dismiss the risk entirely, either
because AGI will never be able to outsmart humanity (e.g., Bringsjord 2012; McDermott 2012) or
because it is too unlikely or too distant in the future to merit attention (e.g., Etzioni 2016; Stilgoe andMaynard 2017). Analysis of other global risks provides reason to believe that AGI does, in fact, pose asubstantial risk to humanity (Baum et al. 2019)
One common concern is that competing projects will race to launch AGI first, with potentiallycatastrophic consequences (Joy 2000; Shulman 2009; Dewey 2015; Armstrong et al. 2016). Desire to
win the AGI race may be especially strong due to perceptions that AGI could be so powerful that it
would lock in an extreme first-mover advantage. This creates a collective action problem: it is in thegroup’s collective interest for each project to maintain a high safety standard, but it is each project’s
individual interest to skimp on safety in order to win the race. Armstrong et al. (2016) present gametheoretic analysis of the AGI race scenario, finding that the risk increases if (a) there are more R&D
projects; (b) the projects have a stronger preference for their own AGI relative to others’, making themless likely to invest in time-consuming safety measures; and (c) the projects have a similar capability tobuild AGI, so that the advantage they get from skimping on safety is relatively larger.
Barrett and Baum (2017a; 2017b) develop a risk model of catastrophe from AGI, looking
specifically at AGI that recursively self-improves to the point that it becomes superintelligent andgains control of the planet.2 For this catastrophe to occur, six conditions must all hold: (1)superintelligence must be possible; (2) the initial (“seed”) AI that starts the self-improvement process
must be created; (3) the self-improvement process and the resulting superintelligence must not be
successfully contained, thus allowing the superintelligence to gain control of the planet; (4) humans
must fail to make the AI’s goals safe, so that if it accomplished its goals it would cause a catastrophe;(5) the AI must not independently make its goals safe regardless of human efforts; and (6) the AI mustnot be deterred from pursuing its goals by humans, other AIs, or anything else. The total risk depends
on the probability of each of these conditions holding. Risk management can seek to reduce theprobability of conditions (2), (3), (4), and (6), and is one aspect of AGI policy.

2.3 Policy
AGI policy can be understood broadly as all efforts to influence AGI R&D, which can include theformal policies of governments and other institutions as well as the informal policies of peopleinterested in or concerned about AGI, including the researchers themselves. AGI policy can seek to,among other things, fund or otherwise support AGI R&D, encourage particular ethical views to be
2

The model speaks in terms of AI in general, of which AGI is just one type, alongside other types of AI that could also
recursively self-improve. This distinction is not crucial for the present paper.

11

built into AGI, or reduce AGI risk. Sotala and Yampolskiy (2015) review a wide range of AGI policy
ideas, focusing on risk management.
Much of the prior literature on AGI politics emphasizes the tension between (1) hypothetical AGI
developers who want to proceed with inadequate regard for safety or ethics and (2) a community that isconcerned about unsafe and unethical AGI and seeks ways to shift AGI R&D toward safer and more
ethical directions. Joy (2000) argues that the risk of catastrophe is too great and calls for a generalabandonment of AGI R&D. Hibbard (2002) and Hughes (2007) instead call for regulatory regimes toavoid dangerous AGI without altogether abandoning the technology. Yampolskiy and Fox (2013)
propose review boards at research institutions to restrict AGI research that would be too dangerous.
Baum (2017b) calls for attention to the social psychology of AGI R&D communities in order to ensurethat safety and ethics measures succeed, as well as to encourage AGI R&D communities to do more on
their own.
One policy challenge comes from the fact that AGI could be developed anywhere in the world that
can attract sufficient research talent and assemble modest computing resources. Therefore, Wilson(2013) outlines an international treaty that could ensure that dangerous AGI work does not shift tounregulated countries. Scherer (2016) analyzes the potential for AGI regulation by the US government,
noting the advantages of national regulation relative to sub-national regulation and suggesting that this
could be a prelude to an international treaty. Goertzel (2009) analyzes prospects that AGI will be
developed in China or in the West, finding that AGI could be developed either way depending on theimportance of certain factors. Bostrom (2014) calls for international control over AGI R&D, possibly
under the auspices of the United Nations. In order to identify rogue AGI R&D projects that may
operate in secret, Hughes (2007), Shulman (2009), and Dewey (2015) propose global surveillance
regimes; Goertzel (2012a) proposes that a limited AGI could conduct the surveillance.
Finally, prior literature has occasionally touched on the institutional context in which AGI R&D
occurs. The Yampolskiy and Fox (2013) proposal to establish review boards that are similar to thepreexisting review boards for research on human subjects predominantly focuses on universities.
Goertzel (2017a) expresses concern about AGI R&D at large corporations due to their tendency toconcentrate global wealth and bias government policy in their own favor; he argues instead for opensource AGI R&D. In contrast, Bostrom (2017) argues that open-source AGI R&D could be moredangerous because it would give everyone access to the same code and thereby tighten the race to build
AGI first. Shulman (2009) worries that nations will compete to build AGI in order to achieve“unchallenged economic and military dominance”, and that the pursuit of AGI could be geopoliticallydestabilizing. Baum et al. (2011) query AGI experts on the relative merits of AGI R&D in
corporations, open-source communities, and the US military, and find that experts have diverging
views, especially on the relative merits of open-source and military R&D.
It should also be noted that there has been some significant activity regarding AI from major
governments. For example, the Chinese government recently announced a major initiative to become a
global leader in AI within the next few decades (Webster et al. 2017). The Chinese initiative closely
resembles—and may be derivative of—a series of reports on AI published by the US under President
Obama (Allen and Kania 2017). Russian President Vladimir Putin has spoken about the importance ofAI, calling it “the future,” noting “colossal opportunities, but also threats that are difficult to predict”(RT 2017). However, these various initiatives and pronouncements are not specifically about AGI andappear to mainly refer to narrow AI. Some policy communities have even avoided associating with
AGI, such as a series of events sponsored by the Obama White House in association with the reports
mentioned above (Conn 2016). Thus, high-level government interest in AI does not necessarily imply
government involvement in AGI. One instance of high-level government interest in AGI is in theEuropean Commission’s large-scale support of the Human Brain Project, in hopes that a computer
brain simulation could revive the European economy (Theil 2015).
12

3. Research Questions
The prior literature suggests several questions that could be informed by a survey of active AGI R&D
projects:
How many AGI R&D projects are there? Armstrong et al. (2016) find that AGI risk increases if
there are more R&D projects, making them less likely to cooperate on safety. Similarly, literature oncollective action in other contexts often proposes that, under some circumstances, smaller groups may
be more successful at cooperating, though large groups may be more successful in other circumstances(e.g., Yang et al. 2013).3 Thus, it is worth simply knowing how many AGI R&D projects there are.
What types of institutions are the projects based in? Shulman (2009), Baum et al. (2011),
Yampolskiy and Fox (2013), and Goertzel (2017a) suggest that certain institutional contexts could be
more dangerous and that policy responses should be matched to projects’ institutions. While the exact
implications of institutional context are still under debate, it would be helpful to see which institution
types are hosting AGI R&D.
How much AGI R&D is open-source? Bostrom (2017) and Goertzel (2017a) offer contrasting
perspectives on the merits of open-source AGI R&D. This is another debate still to be resolved, which
meanwhile would benefit from data on the preponderance of open-source AGI R&D.
How much AGI R&D has military connections? Shulman (2009) proposes that nations may pursue
AGI for military dominance. If true, this could have substantial geopolitical implications. Whilemilitary R&D is often classified, it is worth seeing what military connections are present in publicly
available data.
Where are AGI R&D projects located? Wilson (2013) argues for an international treaty to regulate
global AGI R&D, while Scherer (2016) develops a regulatory proposal that is specific to the US. It isthus worth seeing which countries the R&D is located in.
What goals do projects have? Section 2.1 summarizes a range of ethical views corresponding to avariety of goals that AGI R&D projects could have. Additionally, Armstrong et al. (2016) finds that
AGI risk increases if projects have stronger preference for their own AGI relative to others’, which
may tend to happen more when projects disagree on goals. Thus, it is worth identifying and comparing
projects’ goals.
How engaged are projects on safety issues? Section 2.2 reviews a range of views on the size of
AGI risk and the difficulty of making AGI safe, and Section 2.3 summarizes policy literature intended
to ensure that AGI R&D projects may have inadequate safety procedures. Thus, data on how engaged
projects are on safety could inform discussion both on the size of AGI risk and on AGI risk policy.
How large are the projects? Larger projects may be more capable of building AGI. Additionally,
Armstrong et al. (2016) find that AGI risk increases if projects are similar in their capability to build
AGI. The Armstrong et al. (2016) analysis assumes that project capacity is distributed uniformly. It is
worth seeing what the distribution of project sizes actually is and which projects are the largest.
Project capacity for building AGI is arguably more important than project size. However, project
capacity is harder to assess with this paper’s methodology of analyzing openly published statements. In
addition to project size, project capacity could also depend on the talent of its personnel, theavailability of funding, computing power, or other resources, and on how well the project is managed.
These factors are often not publicly reported. Another important factor is the viability of the technicalapproach that a project pursues, but this is not well understood and is a matter of disagreement among
AGI experts. While it may be possible to assess project capacity with some degree of rigor, this3

The collective action literature specifically finds that smaller groups are often more successful at cooperating when closeinteractions reduce free-riding and the costs of transactions and compliance monitoring, while larger groups are often more
successful at cooperating when cooperation benefits from having more total resources available (Yang et al. 2013). Thus,
for example, one might want a small group for keeping a secret but a large group for fundraising for a fixed project.

13

paper’s methodology is not suited for such a task, and thus it is left for future work. Instead, projectsize may be used as at least a rough proxy for project capacity, though caution is warranted here
because it may be an imperfect or even misleading proxy.

4. Methodology
The paper’s method consists of identifying AGI R&D projects and then characterizing them along
several axes. The identification and description were based on openly published information as foundin scholarly publications, project websites, popular media articles, and other websites, with emphasis
placed on more authoritative publications. Identification and description were conducted primarily by
the present authors during the primary data collection stage between June and September 2020. Some
project data were revisited for minor miscellaneous revisions after September 2020.
In social science terminology, this methodology is known as the “coding” of qualitative data
(Coffey and Atkinson 1996; Auerbach and Silverstein 2003). The data is qualitative in that it consistsof text about AGI R&D projects; it is coded into quantitative form, such as “one academic project andthree government projects.” The coding scheme was initially developed based on prior literature andthe present authors’ understanding of the topics. It was updated during the coding process based on thepresent authors’ reading of the data (known as “in vivo” coding).
This methodology is fundamentally interpretive and is rooted in the researcher’s interpretation of
the data. Some aspects of the data are not a matter of interpretation—for example, the fact that the
University of Southern California is an academic institution in the US. Other aspects are more open tointerpretation. This includes which projects qualify as AGI R&D. Goertzel (2014, p.2) refers to the
AGI community as a “fuzzy set”; this is an apt description. Different researchers may interpret thesame data in different ways. Indeed, a small number of data points from the 2017 survey have beencoded differently in this 2020 version, due to the authors’ reflection on, and refinement of, their coding
technique. Furthermore, different authors may also find different data as they search through the vast
space of openly published information about AGI R&D. Thus, these results should be read as one takeon AGI R&D and not necessarily as a true or complete reflection of the topic. Interested readers areinvited to query the data for themselves and make their own interpretations. The appendices containfull descriptions and explanations of coding judgments and cites the corresponding data. (Not all thedata are cited, since much of what was found is redundant or of limited relevance.)
The methodology of the 2020 survey is similar but not identical to the methodology of the 2017
survey. There are two types of methodology changes: changes in the methodology for how projects are
identified and changes in the methodology for how the identified projects are coded. Of the two, thechanges in project identification methodology are more significant. As described in Section 4.1, the2020 survey uses an expanded suite of methods used to identify AGI R&D projects. Changes in thecoding of identified projects have been relatively minor.
Because the methods have changed, results of the 2017 survey are not directly comparable toresults of the 2020 survey. Therefore, the 2020 survey revisits the AGI R&D projects active in 2017 inaddition to covering projects active in 2020. The result is three datasets: (1) projects identified in the2017 survey, (2) projects active in 2017 as identified in the 2020 survey, and (3) projects active in
2020. Section 5 presents all three datasets coded using the same updated coding methodology.
Comparisons between these datasets show the changes in AGI R&D projects that are due to changes inproject identification methodology as well as the changes due to the passage of time between 2017 and2020. Changes in the projects due to changes in coding methodology are relatively minor and are notemphasized in Section 5.

14

4.1 Identification of AGI R&D Projects
AGI R&D candidate projects were identified via:









The 2017 AGI R&D projects survey.
The present authors’ prior knowledge.
Projects suggested by readers of the 2017 survey.
Keyword searches on the internet and in scholarship databases, mainly Google web search, Google
Scholar, Crunchbase, GitHub, and LinkedIn.
Other previous survey papers (Chong et al. 2007; Duch et al. 2008; Langley et al. 2008; de Garis etal. 2010; Goertzel et al. 2010; Samsonovich 2010; Taatgen and Anderson 2010; Thórisson andHelgasson 2012; Dong and Franklin 2014; Goertzel 2014; Kotseruba et al. 2016).
The entire contents of the issues of the Journal of Artificial General Intelligence published sincethe 2017 survey (2018 through 2020; earlier issues were covered in the 2017 survey).
The proceedings of the AGI conferences published since the 2017 survey (2018 to 2020; earlier
proceedings were covered in the 2017 survey).
Additional literature and webpages identified via all of the above.

The 2020 survey uses all of the project identification methods that were used in the 2017 survey,
plus some additional methods. The new methods are suggestions from readers of the 2017 survey andsearches of Crunchbase and GitHub. The expansion of methods was done to achieve a morecomprehensive compilation of AGI R&D projects. The 2020 survey has attempted to apply thesemethods to identify AGI projects active in both 2017 and 2020. Unfortunately, it is not possible in2020 to search the versions of Crunchbase and GitHub that existed in 2017. It is likewise not possible
to incorporate suggestions from all readers of the 2020 survey. Instead, a draft of the survey wascirculated to select colleagues to identify further projects and obtain general feedback.
A project is considered active if it has visible updates within the three previous years. Projects
coded as active in 2017 need to be active at some point during the three year period 2015-2017.
Projects coded as active in 2020 need to be active at some point during the three year period 20182020. Projects are coded as active if they are active at any time in the three year period, even if they are
known to be no longer active. For example, a project that publicly disbanded in 2019 is still coded as
active in 2020, even though it is known that the project is not active in the year 2020. The visible
updates needed to code a project as active can be in a variety of forms including research publications,
website updates, and blog posts. The updates do not need to show progress on AGI R&D inrecognition of the possibility that the R&D itself could be conducted privately.
Project chronology is inferred from both the timing and the substance of the visible updates. When
updates were released is inferred from listed publication dates or website update data on the Internet
Archive (https://archive.org). The substance of updates is obtained by reading the updates. For
example, the Internet Archive may show that a project website was publicly launched in 2018, while
the website itself states that the project itself was founded in 2016. This is taken to indicate that the
project was active in both 2016 and 2018. The project would likewise be coded in this survey as active
in both 2017 and 2020 because 2016 and 2018 are within the three year periods of 2017 and 2020,
respectively. An implication of this is that the 2020 survey can retrospectively identify some projects
that were active in 2017 but not publicly visible in 2017. Because they were not publicly visible, they
could not have been included in the 2017 survey. This creates an asymmetry between 2017 and 2020
projects: unlike for the 2017 data in the 2020 survey, projects that are active in 2020 but not publicly
visible in 2020 cannot be included in the 2020 survey. This asymmetry is another factor that should be
accounted for when comparing projects between 2017 and 2020.
15

Each identified project was put into one of five categories:









Active AGI R&D projects (Appendix 1). These are projects that are, or were at some point during
2018-2020, working toward building AGI. The included projects either identify as AGI or conduct
R&D to build something that is considered to be AGI, human-level intelligence, or
superintelligence.
Inactive AGI R&D projects (Appendix 2). These are projects that were working towards building
AGI at some point during 2015-2017, were inactive during the period 2018-2020, and for which
new information was identified in the 2020 survey that affects how the project is coded. This
includes (1) projects that were included in the 2017 survey whose coding is affected by changes incoding methodology between 2017 and 2020, and (2) projects that were active during 2015-2017,
inactive during 2018-2020, and identified for the first time in the 2020 survey. These projects arecoded in the same way as active AGI R&D projects.
Other notable projects (Appendix 3). These include (1) projects that were identified in the 2017
survey, were inactive during 2018-2020, and whose coding is not affected by changes in coding
methodology between 2017 and 2020; for these projects, the coding in the 2017 survey is used fordata analysis in the 2020 survey; (2) projects that work on technical aspects of AGI but are notworking towards building AGI, such as projects working on hardware or safety mechanisms thatcan be used for AGI; and (3) select narrow AI projects, such as AI groups at major technology
companies.
Other projects (Appendix 4). These projects describe themselves as working on AGI, butdemonstrate little further activity on AGI.
Other projects judged not to be worth including in this paper.

The Section 5 data analysis is based on AGI R&D projects as documented in Appendices 1-2 andthe 2017 survey. The projects in Appendices 3 and 4 are reported for the purposes of documenting
related work, clarifying the present authors’ thinking about where the boundaries of AGI R&D lie, andassisting in the identification of any AGI R&D projects that have been overlooked by the present
research.
Projects that only do R&D in deep learning and related techniques were excluded unless they
explicitly identify as trying to build AGI. Deep learning already shows some generality (LeCun et al.
2015), and some people argue that deep learning could be extended into AGI (e.g., Christiano 2016).
Others argue that deep learning, despite its remarkable ongoing successes, is fundamentally limited,
and AGI requires other types of algorithms (e.g., Strannegård and Nizamani 2016; Wang and Li 2016;
Marcus and Ernst 2019). The recent explosion of work using deep learning renders it too difficult tosurvey using this paper’s project-by-project methodology. Furthermore, if all of deep learning was
included, it would dominate the results, yielding the unremarkable finding that there is a lot of active
deep learning work. The deep learning projects that explicitly identify as trying to build AGI are muchsmaller in number, fitting comfortably with this paper’s methodology and yielding more noteworthy
insights.

4.2 Description of AGI R&D Projects
For each identified AGI R&D project, a general description was produced, along with classification interms of the following attributes:
Type of institution: The type of institution in which the project is based, such as academic orgovernment.
16








Open-source: Whether the project makes its source code openly available.
Military connections: Whether the project has connections to any military activity.
Nationality: The nation where the project is based. For multinational projects, the nation where theproject’s administrative and/or operational leadership is located is considered the lead nation, andadditional partner countries were tabulated separately.
Stated goal: The project’s stated goals for its AGI, defined as what the project aims to accomplish
with its AGI and/or what goals it intends to program the AGI to pursue.
Engagement on safety: The extent of the project’s engagement with AGI safety issues.
Size: The overall size of the project.

4.2.1 Type of Institution
The type of institution attribute has six categories:







Academic: Institution conducts secondary education (e.g., colleges and universities).
Government: Institution is situated within a local or national government (e.g., national
laboratories). This category excludes public colleges and universities.
Nonprofit: Institution is formally structured as a nonprofit and is not an academic institution (e.g.,
nonprofit research institutes).
Private corporation: Institution is for-profit and does not issue public stock.
Public corporation: Institution is for-profit and does issue public stock.
None: Project is not based within any formal institution.

Some projects had two institution types; none had more than two. For the two-type projects, bothtypes were recorded. Project participants were identified and evaluated to determine whether their
institutions merited inclusion. Institutions that have not been active within the last three years wereconsidered to be inactive and excluded. Additionally, institutions were only included if the partnership
was formally recognized on project websites or other key project documents. Some projects had
participation from many institutions that were more limited in nature, such as through co-authorship of
publications. This more limited participation was not counted because it would make the entire
exercise unwieldy due to the highly collaborative nature of many of the identified projects. This coding
policy was maintained across all of the attributes, not just institution type.
In the 2017 survey, projects were coded as having multiple institution types according to a morerelaxed standard for partner organizations than was used in the 2020 survey. Therefore, some projects
coded as having multiple institution types under the 2017 methodology may be coded as having only
fewer institution type(s) under the 2020 methodology.
4.2.2 Open-Source
The open-source attribute has three categories:

Yes: Project has source code available for download online.
Restricted: Project offers source code upon request.
No: Project does not offer source code.

Projects are coded as “yes” if some source code related to their AGI work is open. Projects arecoded as “restricted” if no AGI code is open and some AGI code is available upon request. Projects
that have no open or restricted code are coded as “no”; this includes projects that only have other, non17

AGI open or restricted code are coded as “no.” Whether code is related to AGI is a matter ofinterpretation, and different coders may produce somewhat different results. The three open-source
categories are mutually exclusive: each project is coded as belonging to one category.
4.2.3 Military Connections
The military connections attribute has three categories:

Yes: The project has identifiable military connections.
No: The project is found to have no military connections.
Unspecified: No determination could be made about the project’s military connections.

Military connections were identified via keyword searches on project websites and the internet atlarge, as well as via acknowledgments sections in recent publications. Projects were coded as having
military connections if they were based in a military organization, if they received military funding, orif they collaborated in other ways with militaries. Projects were coded as having no militaryconnections if they stated that they did not collaborate with militaries. The latter was only viable forcertain smaller projects. Unless a definitive coding judgment could be made, projects were coded as“unspecified.”
Projects are coded as having military connections if the project has any identifiable militaryconnection, however limited. This includes projects whose military connections are not for militaryaffairs. For example, a military research agency could fund a project to do basic research on AGI
without the corresponding military having any clear application of the research. This also includes
projects whose military connections are for non-AGI work. This broad treatment of militaryconnections seeks to assess the full scope of relationships between militaries and AGI R&D projects,
which includes but is not limited to military pursuit of AGI for weaponry and other military affairs. As
Section 5 documents, relatively few projects have military connections, making this broad treatment
more insightful.
In the 2017 survey, projects were coded as having no military connection if they state that they do
not collaborate with militaries or if the entire project could be scanned for connections. In the 2020
survey, the latter criterion is omitted on grounds that it is too difficult to scan an entire project.
Therefore, some projects coded as “no” under the 2017 methodology may be coded as “unspecified”
under the 2020 methodology.
4.2.4 Nationality
The nationality attribute has two categories:

Lead country: The country in which the project’s administrative and/or operational leadership isbased
Partner countries: Other countries that contribute to the project

One lead country was specified for each project. Projects could have zero, one, or multiple partnercountries. Partner countries could be the location of secondary sites of the lead institution, such as
satellite offices, as well as the location of any partner institution(s).
The 2020 methodology for nationality has two changes relative to the 2017 methodology. First, the2020 methodology places greater emphasis on formal information about project location, such as isfound in project websites, and less emphasis on more informal information such as the location of18

residence of project team members. Therefore, some projects may be coded as having different
locations under the 2020 methodology than under the 2017 methodology. Second, relative to the 2017
survey, the 2020 survey used a somewhat stricter standard for coding partner countries. Therefore,
some countries coded as “partner” under the 2017 methodology may be coded as not “partner” underthe 2020 methodology.
4.2.5 Stated Goals
The stated goals attribute has six categories:








Animal welfare: AGI is being built to benefit nonhuman animals.
Ecocentrism: AGI is being built to benefit natural ecosystems.
Humanitarianism: AGI is being built to benefit humanity as a whole. This category includes
statements about using AGI to solve general human problems such as poverty and disease.
Intellectualism: AGI is being built for intellectual purposes, which includes the intellectual
accomplishment of the AGI itself and using the AGI to pursue intellectual goals.
Profit: AGI is being built to make money for its builders.
Transhumanism: AGI is being built to benefit advanced biological and/or mechanical beings,
potentially including the AGI itself.
Unspecified: Available sources were insufficient to make a coding judgment.

Some categories of goals found in prior AGI literature, including gaining military advantage andthe personal benefit of AGI builders, did not appear in the data.
For the coding of stated goals, only explicit statements were considered; the surrounding context
was not considered. For example, most AGI R&D projects at corporations did not explicitly state profit
as a goal. These projects were not coded “profit” even though they may in fact have profit as a goal.
Additionally, only statements by the AGI project and its team members were considered. Statementsby related entities, such as the project’s parent organization, were not considered. For statements byproject team members, emphasis was placed on team members in a leadership role within the project.
In the 2017 survey, the coding was not as strict at excluding statements made by entities related tothe project. Therefore, some statements that were coded as indicating a certain stated goal for a project
under the 2017 methodology may be coded as not indicating that goal under the 2020 methodology.
4.2.6 Engagement on Safety
The engagement on safety attribute has four categories:Active: Projects have dedicated efforts to address AGI safety issues.
Moderate: Projects acknowledge AGI safety issues but lack dedicated efforts to address them.
Dismissive: Projects argue that AGI safety concerns are incorrect or misguided.
Unspecified: Available sources were insufficient to make a coding judgment.

Each project was coded as belonging to one and only one of these categories. Projects that are
active on safety and also acknowledge safety issues (i.e., are moderate) were coded as active. Projects
that are active on safety and also argue that safety concerns are incorrect or misguided (i.e., aredismissive) are coded as active. Projects that acknowledge safety issues and also argue that safetyconcerns are incorrect or misguided are coded as moderate. The active-dismissive and moderate19

dismissive projects can occur, for example, if a project articulates that there are some valid safetyissues, but not the safety safety issues that some other people have raised.
4.2.7 Size
Project size attribute has five categories:
Small: Approximately 5 or fewer full-time personnel, small amounts of other indicators
Small-medium: Approximately 5 to 15 full-time personnel, small-medium amounts of otherindicators
Medium: Approximately 15 to 30 full-time personnel, medium amounts of other indicators
Medium-large: Approximately 30 to 50 full-time personnel, medium-large amounts of otherindicators
Large: Approximately 50 or more full-time personnel, large amounts of other indicators
Other indicators include, but are not necessarily limited to, part-time personnel, external
collaborators, publications, funding, and AGI accomplishments. Coding judgments took into accountthe full range of indicators. For this reason, projects with a certain number of personnel are notnecessarily coded within the corresponding size range.
For projects that do not list personnel, their presence on LinkedIn was studied to infer theirpersonnel size. Some projects, especially some of the larger projects, conduct a mix of AGI and nonAGI activities. For these projects, size was evaluated in terms of the AGI portion of the project.
The 2020 methodology for size contains two changes from the 2017 survey. First, the 2017 survey
did not employ a fixed (approximate) set of size ranges for number of personnel. Instead, it used amore informal estimation of project size. Therefore, projects coded as one size under the 2017
methodology may be coded as a different size under the 2020 methodology. Second, the 2017 survey
included an “unspecified” size category for projects for which the available information was
insufficient to make a coding judgment. The 2020 survey removes the “unspecified” size category on
grounds that it is always possible to make a coding judgment, even if there is limited information
available. Therefore, projects coded as “unspecified” under the 2017 methodology would be coded as
one of the five sizes under the 2020 methodology.

5. Main Results
This section presents the main results of the survey. Full results are presented in Appendices 1-4.
Figure ES1 in the Executive Summary presents an overview.

5.1 The Identified AGI R&D Projects
72 AGI R&D projects were identified as being active in 2020. 33 of these projects also appeared in the2017 survey. 39 are new to this survey. Of the 39 new projects, 24 were found to be active in AGI
R&D in 2017; the other 15 became active after 2017. In the list below, bold font indicates projects
new to this survey; bold-italics font indicates projects that are new to this survey and were also active
in 2017. In alphabetical order, the 72 active AGI R&D projects are:1. ACT-R, led by John Anderson of Carnegie Mellon University
2. AERA, led by Kristinn Thórisson of CADIA at Reykjavik University
3. AGI Brain, led by Mohammadreza Alidoust at AGT Co.
20

4. AGI Laboratory, a private company led by David Kelley
5. AGi3, a project led by Peter Voss
6. AIBrain, a multinational private corporation led by Dr. Richard H. Shinn
7. AIXI, led by Marcus Hutter of Australian National University
8. Animats, a small project led by researchers in Sweden, Switzerland, and the US
9. ANSNA is a project led by Patrick Hammer at Temple University
10. Apollo Program for AGI is a project of Montréal AI led by Vincent Boucher
11. Astrum is a private corporation founded by Srikanth Srinivas
12. Baidu Research, an AI research group within Baidu
13. Big Mother, a nonprofit led by Aaron Turner based in the UK
14. Binary Neurons Network, a Russian project led by Ilya Shishkin
15. Blue Brain, led by Henry Markram of École Polytechnique Fédérale de Lausanne
16. Brain Simulation II, a small private corporation led by Charles Simon
17. Brain2Bot, a small private corporation led by Gunnar Newquist
18. Cerenaut Research, a project led by Gideon Kowadlo and David Rawlinson
19. China Brain Project, led by Mu-Ming Poo of the Chinese Academy of Sciences
20. CLARION, led by Ron Sun of Rensselaer Polytechnic Institute
21. Cognitive Science & Solutions, a project led by David Sherwood and Terry Higbee
22. CommAI, a project of Facebook AI Research
23. Core AI, a project of Akin AI led by Liesl Yearsly
24. Curious AI, a project led by Harri Valpola
25. Cyc, a project of Cycorp of Austin, Texas, founded by Doug Lenat in 1984
26. DeepBrainz, a project led by Arunkumar Venkataramanan
27. DeepMind, a London-based AI company acquired by Google in 2014
28. Drayker, a project based in Brazil and led by Hyadhuad Lucer
29. Fairy Tale Artificial General Intelligence Solutions (FTAGIS), a project led by Răzvan Flavius
Panda
30. FLOWERS, led by Pierre-Yves Oudeyer of Inria and David Filliat of Ensta ParisTech
31. GoodAI, an AI company based in Prague led by computer game entrepreneur Marek Rosa
32. Graphen, a private corporation led by Dr. Ching-Yung Lin
33. He4o, a Chinese GitHub project led by Jia Xiaogang
34. HTM, a project of the AI company Numenta, led by Jeffrey Hawkins, founder of Palm Computing
35. Human Brain Project, a consortium of research institutions across Europe
36. Intelligent Artifacts, a US-based project led by Sevak Avakians
37. Leabra, led by Randall O’Reilly of University of Colorado
38. LIDA, led by Stan Franklin of University of Memphis
39. M3-CLIC, a project of M3-IP Ltd. and led by Vidur (Sonny) Nanda
40. MARAGI, an open-source project led by Dave Shapiro
41. Mauhn, a Belgium-based private corporation led by Berg Severens
42. Microsoft Research AI, an AI research group at Microsoft
43. Mind Simulation, a Russian lab led by Leonid Derikyants, Sergey Pankratov, and Vasily Mazin
44. Mindtrace, a private corporation led by Hoon Chung
45. Monad, a US-based private corporation led by Jovan Williams
46. NARS, led by Pei Wang of Temple University
47. NDEYSS, a transhumanist GitHub project
48. New Sapience, a private corporation led by Bryant Cruse
49. Nigel, a project of Kimera, an AI company based in Portland, Oregon
50. NiHA, based at the COMSATS Institute of Information Technology in Pakistan and led by
Wajahat Mahmood Qazi
2151. NNAISENSE, an AI company based in Lugano, Switzerland and led by Jürgen Schmidhuber
52. Olbrain, a company led by Alok Gautam, Nishant Singh, and Mayank Kumar
53. Omega, led by Eray Özkural from Celestial Intellect Cybernetics
54. OpenAI, an AI research organization with both nonprofit and for-profit components
55. OpenCog, an open-source project listed in the 2017 survey as CogPrime and led by Ben Goertzel
56. Optimizing Mind, a San Francisco-based project led by Tsvi Achler
57. ORBAI, a US-based company led by Brent Oster
58. Prome, a private corporation founded by Sean Everett
59. Research Center for Brain-Inspired Intelligence (RCBII), a project of the Chinese Academy of
Sciences
60. Robot Brain Project, an open-source project formerly known as Becca and led by Brandon Rohrer
61. Sanctuary AI, a private corporation in Canada led by Suzanne Gildert
62. Sigma, led by Paul Rosenbloom of University of Southern California
63. SingularityNET, an open AI platform led by Ben Goertzel
64. Soar, led by John Laird of University of Michigan and a spinoff company SoarTech
65. Susaro, an AI company based in the Cambridge, UK area and led by Richard Loosemore
66. Tencent AI Lab, the AI group of Tencent
67. True Brain Computing, a Russian company led by Alexey Redozubov
68. Uber AI Labs, the AI research division of Uber
69. Vicarious, an AI company based in San Francisco
70. Whole Brain Architecture Initiative (WBAI), a nonprofit in Tokyo
71. WILLIAM, a project from Ukraine-based OCCAM led by Dr. Arthur Franz and Michael Löffler
72. Xephor Solutions, a private corporation led by Isabell Kunst
The 2017 survey originally identified 45 AGI R&D projects. 33 of these projects are listed above. The
other 12 are now inactive:
1. AIDEUS, led by Alexey Potapov of ITMO University and Sergey Rodionov of Aix Marseille
Université; excluded due to apparent inactivity
2. Alice in Wonderland (AIW), led by Claes Strannegård of Chalmers University of Technology;
excluded due to apparent inactivity
3. DeSTIN, led by Itamar Arel of University of Tennessee; excluded due to apparent inactivity
4. DSO-CA, led by Gee Wah Ng of DSO National Laboratories, Singapore’s primary national
defense research agency; excluded due to apparent inactivity upon the departure of the project lead
5. Icarus, led by Pat Langley of Stanford University; excluded due to apparent inactivity
6. Maluuba, a company based in Montréal recently acquired by Microsoft; excluded due todissolvement upon acquisition by Microsoft
7. MicroPsi, led by Joscha Bach of Harvard University; excluded due to apparent inactivity
8. MLECOG, led by Janusz Starzyk of Ohio University; excluded due to apparent inactivity and lackof R&D
9. Real AI, an AI company based in Hong Kong and led by Jonathan Yan; excluded due to apparent
inactivity
10. SiMA, led by Dietmar Dietrich of Vienna University of Technology; excluded due to apparent
inactivity
11. SNePS, led by Stuart Shapiro at State University of New York at Buffalo; excluded due to apparent
inactivity upon retirement of project leads
12. VICTOR, a project of 2AI, which is a subsidiary of Cifer Inc., a small US company; excluded due
to apparent inactivity
22

The 2020 survey also identified 1 AGI R&D project that was active in 2017 but not in 2020: BasicAI,
a small project led by Sean Markan focused on HLAI.
It can now be said that there were at least 70 AGI R&D projects active in 2017. This includes the
45 projects identified in the 2017 survey, the 1 project identified in 2020 that was active in 2017 but
not in 2020, and 24 projects identified in 2020 that were active in 2017 and remain active in 2020.
Unfortunately, it is not possible in 2020 to search the versions of Crunchbase and GitHub that existed
in 2017. It is possible that some project profiles on the 2017 versions of Crunchbase and GitHub are
not on the 2020 versions. However, this is unlikely because Crunchbase and GitHub profiles tend toremain online even for inactive projects. Therefore, 70 is probably at least approximately equal to thenumber of 2017 AGI R&D projects that would have been identified had Crunchbase and GitHub been
searched in 2017. Regardless, this is the best 2017 dataset available and will be used in this survey forcomparing AGI R&D projects between 2017 and 2020.
Of the 39 projects new to the 2020 survey, 26 were identified via methods that are new to the 2020
survey. There were 14 were identified via Crunchbase, 11 via GitHub, and 1 via readers of the 2017
survey. Therefore, if the 2020 survey had only used the methods of the 2017 survey, it would haveidentified 13 new projects instead of 39 new projects. Nonetheless, the full set of 72 projects is used inanalysis found throughout this paper.
This survey identifies 6 projects that were active in 2017 but were not publicly visible in 2017:
Cognitive Science & Solutions, Core AI, DeepBrainz, Mind Simulation, Mindtrace, and Olbrain. Eachof these projects had websites that, according to the Internet Archive, launched after 2017, but thewebsites themselves, or other project information, indicate that the project had an earlier starting date.
This chronological difference could be due to the project spending some time in “stealth mode,” inwhich it was intentionally remaining private in order to focus on internal development. Indeed, thissurvey identifies 1 project that is in “stealth mode” but nonetheless has a public presence (SanctuaryAI). Alternatively, it could simply be due to the project not getting its public profile active right away.
Whatever the reason, these 6 projects are projects that could not have been identified by the 2017
survey. Likewise, it is possible that there are projects that are active in 2020 but are not (yet) visible.
The results of this survey suggest that there may be approximately 6 not-yet-visible projects active in
2020.
There are some interconnections between the projects. For example, ANSNA was created byPatrick Hammer, a researcher who worked on NARS who also references Jeffrey Hawkins’s HTM;AIXI’s Marcus Hutter is on leave at DeepMind; Animats and NNAISENSE collaborate; DeepMind
and OpenAI continue to collaborate on AGI safety research; OpenAI and MSR AI are both affiliatedwith Microsoft; OpenCog and SingularityNET are both still led by the same person, Ben Goertzel, and
OpenCog technology continues to be used by SingularityNET; Blue Brain and the Human Brain
Project were both initiated by the same person, Henry Markram, and share a research strategy; theChina Brain Project and RCBII share the same parent organization, the Chinese Academy of Sciences;
Sigma’s project lead Paul Rosenbloom used to be the co-PI of Soar; and ACT-R and Leabra were once
connected in a project called SAL (an acronym for Synthesis of ACT-R and Leabra; see Appendix 3).
This suggests an AGI community that is at least in part working together towards common goals, notcompeting against each other as is often assumed in the literature (Section 2.2). The existence ofinterconnections was apparent in the 2017 survey. The projects newly identified in the 2020 survey
tend to have fewer interconnections, such that the overall picture of the 2020 survey is one of a field of
AGI R&D that is not as extensively interconnected as in the 2017 survey.
The 2020 survey contains 46 data points that were coded differently than the 2017 survey as
documented in Appendices 1-2. 22 of these data points were coded differently due to changes in the
projects themselves, including 9 changes in partner countries, 4 changes in stated goals, 3 changes ininstitution type, 2 changes in each of military connections and engagement on safety, and 1 change in23

each of open-source and lead country. 18 data points were coded differently due to changes in thecoding methodology, including 10 changes in size, 2 changes in each of military connections, partnercountry, and stated goals, and 1 change in each of institution type and lead country. 6 data points werecoded differently to correct errors or oversights in the 2017 survey, including 2 changes in eachengagement on safety and stated goals and 1 change in each of lead country and partner country. For 1
data point (SingularityNET lead country), there was a change in both methodology and the project.
Overall, this indicates a low degree of change between the 2017 and 2020 surveys, especially relative
to the change due to some 2017 projects becoming inactive and due to new projects identified by the
expanded project identification methodology of the 2020 survey.

5.2 Type of Institution
Of the 2020 AGI R&D projects, 39 are based at least partially in private corporations, 15 are based atleast partially in academic institutions, 6 are based at least partially in nonprofits, 6 are based at least
partially in public corporations, 7 have no formal institutional home, and 3 are based at least partiallyin a government. There are 4 projects split across two different institution types: Soar is academic anda private corporation, FLOWERS is academic and government, and both OpenAI and SingularityNET
are private corporations and nonprofits. Figure 1 summarizes the institution type data. For comparison,
the 2020 survey finds that in 2017, there were 32 projects at private corporations, 21 at academic
institutions, 6 at nonprofits, 6 at public corporations, 4 with no institutional home, and 4 atgovernments; 3 were split across multiple institution types. The 2020 recoding of the 2017 survey
projects finds 11 projects at private corporations, 20 at academic institutions, 5 at nonprofits, 6 atpublic corporations, 2 with no institutional home, and 4 at governments; 3 were split across multiple
institution types.
402017 original
2017 updated

30

2020

20

10

0

Academic

Government

Nonprofit

PrivateCorporation

PublicCorporation

None

Figure 1. Summary of institution type data. The figure shows more data points than the corresponding
number of projects because some projects have multiple institution types. 2017 original (gray) is theset of projects identified in the 2017 survey recoded with the 2020 methodology. 2017 updated (black)is the set of projects active in 2017 and identified in the 2020 survey. 2020 (red) is the set of projects
active in 2020. All projects are coded according to the 2020 coding methodology.
In the 2020 survey, private corporations and academia remain the two most common institution
types, but their relative frequency is reversed in comparison to the 2017 survey. The 2017 survey
24found more academic projects, whereas the 2020 survey finds more private corporation projects. Of the
39 projects new to the 2020 survey, 29 are private corporations; of the 29 private corporations, 14 wereidentified via Crunchbase, 5 via GitHub, and 10 via other sources. Most of the GitHub projects that are
not private corporations do not have any specified institution types and use GitHub as a home.
The 2020 survey also finds an increase in the number of private corporation projects between 2017and 2020 and a decrease in the number of academic projects during this period. The increase in private
corporation projects may constitute evidence of growing AGI profit-R&D synergy, defined as
circumstances in which long-term AGI R&D delivers short-term profits (Baum 2017a). Increased AGI
profit-R&D synergy would have important risk and policy implications. However, other explanations
are also possible. For example, the growth in private corporation AGI projects could be an outcome of
the much more general growth of interest in AI between 2017 and 2020. The growth of interest in AI
makes it especially notable to observe a decrease in the number of academic projects. The decrease
appears to be driven primarily by the departure of academic project leads, who often moved to other
AI-related projects. Alice in Wonderland’s Claes Strannegård has moved to Animats,4 DeSTIN’s
Itamar Arel has since moved on to Syntiant Corp and McD Tech Labs,5 Icarus’s Pat Langley has since
become a professor at the University of Auckland,6 MicroPsi’s Josh Bach now works at the AI
Foundation,7 and SNePS’s Stuart Shapiro has since retired.8 Looking ahead, some of the remaining
academic projects are led by relatively senior academics, who, upon retirement, may cause further
decline in the number of academic projects.
These trends have policy implications. The 2017 survey argued for an important role for measures
to govern AGI R&D in academia, such as the Yampolskiy and Fox (2013) proposal for researchreview boards. The decline of academic projects in 2020 suggests a smaller but still significant role forsuch measures. If the number of academic projects continues to decline in future years, then thegovernance of these projects could become a relatively insignificant portion of the overall policy
landscape. Likewise, the apparent rise of private corporate projects amplifies the need for corporate
governance of AI. That can include governance initiatives within the corporation itself, including those
led by employees (Belfield 2020), as well as initiatives external to the corporation, including
government regulation of corporate activity. These various initiatives may benefit from a broader
characterization of AGI corporations, including other attributes described throughout this survey.
Finally, the 2020 survey continues to find that there are relatively few projects explicitly situated ingovernment institutions. China Brain Project and RCBII are the only projects situated solely within agovernment, both within the government of China. However, this understates the extent of government
involvement in AGI R&D. Numerous other projects receive government funding, aimed at advancing
medicine (e.g., Blue Brain, HBP), economic development (e.g., Baidu), and military technology (e.g.,Cyc, Soar). That said, the data do not show extensive government interest in developing AGI.

5.3 Open-Source
Of the 2020 AGI R&D projects, 33 have source code openly available online. An additional 5 projects(Big Mother, Cyc, Drayker, the Human Brain Project, LIDA) have code available upon request. For
these 38 projects, the available code is not necessarily the project’s entire corpus of code, at least for
the latest version of the code, though in some cases it is. There are 34 projects for which code could
not be found online. Figure 2 summarizes the open-source data. For comparison, the 2020 survey findsthat in 2017, 32 projects had openly available source code, 3 projects had code available upon request,
4

https://www.chalmers.se/en/staff/Pages/claes-strannegard.aspx
https://www.linkedin.com/in/itamar-arel-061b08/
6
https://www.linkedin.com/in/pat-langley-a992/
7
https://www.linkedin.com/in/joschabach/8
https://www.cse.buffalo.edu/~shapiro/
525

and 35 projects had no code found online. The 2020 recoding of the 2017 survey projects finds 25
projects had openly available source code, 3 projects had code available upon request, and 17 projects
had no code found online.
40
2017 original
2017 updated

30

2020

20

10

0

Yes

Restricted

No

Figure 2. Summary of open-source data. 2017 original (gray) is the set of projects identified in the2017 survey recoded with the 2020 methodology. 2017 updated (black) is the set of projects active in2017 and identified in the 2020 survey. 2020 (red) is the set of projects active in 2020. All projects arecoded according to the 2020 coding methodology.
Some trends in the open-source data are apparent. First, most of the projects new to the 2020
survey do not have open-source code available. Many of these are private corporations. One possible
explanation is that the private corporations are keeping their code private for commercial reasons. This
could be an indicator of a more competitive and less cooperative AGI R&D landscape. Second, the2020 survey finds no significant change in open-source projects between 2017 and 2020. This result
suggests that recent dialog on the merits of open publishing in AI, often under the rubric of“publication norms” (e.g., Gupta et al. 2020), have not yet had significant impact on the field of AGI
R&D.
Of the 33 projects with code openly available, 11 projects were situated in academic institutions,
14 projects in private corporations, 4 in nonprofits, 2 in public corporations, 1 in a government
institution (FLOWERS), and 5 with no institution; 4 of these projects have 2 types of institutions. Of
the projects with restricted code, 1 is in a private corporation, 2 are in academic institutions, 1 is in a
nonprofit, and 1 does not have an institution type. The preponderance of open-source code availability
among academic projects suggests that these projects may tend to be more open and collaborative. This
explanation is consistent with the tendency for academic projects to have relatively interconnected
personnel.
Some AGI R&D projects have participated in debates about the merits of open-source code. For
example, MARAGI, SingularityNET, and Deepbrainz have articulated the importance of thedemocratization of code, making AGI research accessible to anyone. The Human Brain Project has
expressed concern about the potential for militaries to use open-source code for harmful purposes. Of
the projects with restricted code, some do so with the hope of fostering a collaborative community (BigMother and Drayker), while others do so to entice customers for business purposes (Cyc). Under whatcircumstances code should be made available is a matter of ongoing analysis (Bostrom 2017; Gupta etal. 2020). There may be value in AGI R&D projects continuing to engage with these issues.

265.4 Military Connections
Of the 2020 AGI R&D projects, 9 have identifiable military connections. There are 2 projects
identified as having no military connections. For all other projects, no determination on militaryconnections could be made. Figure 3 summarizes the military connections data. For comparison, the2020 survey finds that in 2017, there were 10 projects with identifiable military connections, and 2
projects identified as having no military connections, which is the same as the 2020 recoding of the2017 survey projects.
802017 original
2017 updated
2020
60

40

20

0

Yes

No

Unspecified

Figure 3. Summary of military connections data. 2017 original (gray) is the set of projects
identified in the 2017 survey recoded with the 2020 methodology. 2017 updated (black) is the set ofprojects active in 2017 and identified in the 2020 survey. 2020 (red) is the set of projects active in
2020. All projects are coded according to the 2020 coding methodology.
The 2017 projects with military connections were ACT-R, CLARION, DSO-CA, Icarus, Leabra,
LIDA, Sigma, SNePS, and Soar. The 2020 projects with military connections are ACT-R, China Brain
Project, CLARION, Cyc, Leabra, LIDA, RCBII, Sigma, and Soar. The 3 projects with militaryconnections in 2017 but not 2020 (DSO-CA, Icarus, and SNePS) were all inactive in 2020. The 2
projects with military connections in 2020 but not 2017 (China Brain Project and RCBII) were bothactive in 2017 but did not have military connections until after 2017.
The military connections primarily involve basic scientific research rather than applications forweapons or other military infrastructure. The primary exceptions are Cyc, which worked for US
defense agencies in various areas such as terrorism analysis, and Soar, whose affiliate company
SoarTech heavily advertises military applications on its website. These applications are largely tactical,
suggestive of incremental improvements in existing military capacity, not any sort of revolution in
military affairs. Most other projects with military connections have their connections via the receipt offunding from US military research funding agencies such as DARPA and the Office of Naval27

Research. Other projects include China Brain Project and RCBII, both of which have collaborated with
the Chinese Academy of Military Medical Sciences.
Taking the above into account, it follows that the publicly available record indicates that no
military is currently pursuing AGI for major strategic purposes. It is possible that one or moremilitaries are nonetheless doing so. It does stand to reason that if there were substantial militaryinterest in AGI dominance, this information may be classified and inaccessible. The potential existenceof secret military AGI programs is beyond the scope of this paper.
The 2 projects active in 2020 that were identified as having no military connections are AERA andthe Human Brain Project. AERA openly rejects military connections. The Human Brain Project, as aEuropean Union Horizons 2020 funded project is constrained to civil purposes (European Commission2020).
Finally, for the other 61 projects, the presence or absence of military connections was notestablished. Many of these projects likely do not have military connections because they do not workon military applications and they are not at institutions (such as US universities) that tend to havemilitary connections.

5.5 Nationality
Of the 2020 AGI R&D projects, the projects are based in 23 countries and are present in 37 total
countries. There are 32 projects based in the US, 5 in China, 4 in the UK, 3 in Australia, Canada,
Russia, and Switzerland, 2 in India, and 1 each in Austria, Belgium, Brazil, Czech Republic, Finland,
France, Iceland, Iran, Ireland, Japan, the Netherlands, Pakistan, Sweden, Turkey, and Ukraine,
respectively. There are 2 projects that do not have a specified location. Partner countries include theUS (partner for 9 projects), China, the UK (4 projects), Australia, Canada, France, Germany, and Israel(3 projects), Austria, India, Italy, Norway, Russia, South Korea, (2 projects), and Belgium, Brazil,
Denmark, Ethiopia, Finland, Greece, Hungary, Japan, the Netherlands, Portugal, Singapore, Slovenia,
Spain, Sweden, Switzerland, Taiwan, and Turkey (1 project). Figure 4 maps the nationality data. For
comparison, the 2020 survey finds that in 2017, there were projects based in 20 countries and present
in 36 total countries. The 2020 recoding of the 2017 survey projects finds projects based in 14
countries and present in 30 total countries.
The 32 US-based projects are based in 14 states and territories: 13 in California, 4 in New York, 3
in Pennsylvania, and 1 in each of Florida, Oregon, Maryland, Massachusetts, Michigan, Nevada,
Tennessee, Texas, Washington, Washington D.C., and Wyoming, respectively, with 1 project(OpenCog) not having a specified home state. This broad geographic distribution is due largely to themany academic projects: whereas US AI companies tend to concentrate in a few metropolitan areas,
US universities are scattered widely across the country. The corporate projects are mainly in themetropolitan areas of Austin, New York City, Portland, San Francisco/Bay Area, and Seattle, all ofwhich are AI hotspots.
There are 20 multinational projects, a minor increase from the 18 projects in 2017. Human Brain
Project is in 19 total countries, including the lead country. Nigel and SingularityNET are in 7
countries. CommAI is in 5 countries. AIBrain, Animats, DeepMind, Graphen are in 4 countries.
Cerenaut Research, OpenCog, and Soar are in 3 countries. Baidu Research, Core AI, Deepbrainz, M3CLIC, NNAISENSE, Susaro, TAIL, Uber AI Labs, and WBAI are in 2 countries. Some projects suchas NiHA or MSR AI collaborate with researchers overseas, but their partnerships are not indicated here
as there was no evidence that these partnerships were active, official, or on AGI related content.
BlueBrain was was coded as multinational in 2017 but not 2020. BlueBrain had partner institutions in
4 countries listed on a webpage that was active in 2017 but not 2020. It is possible that BlueBrain
retains these or other partner countries, though documentation was not identified.
28

Iceland, Ireland: 1 lead
Canada: 3 lead, 3 partner UK: 4 lead, 4 partner

Norway: 2 partner
Russia: 3 lead, 2 partner
Germany: 3 partner

Czech Republic,
Ukraine: 1 lead each
Switzerland:
Japan: 1 lead, 1 partner
China: 5 lead,
USA: 32 lead, 9 partner
3 lead, 1 partner
Italy: 2 partner
4 partner
South Korea: 2 partner
Austria: 1 lead, 2 partner
Israel:Iran, Pakistan:
Belgium, Finland,
Taiwan: 1 partner
3 partner
1 lead each
The Netherlands,
India: 2 lead,
Sweden, Turkey:
2 partner
1 lead, 1 partner each
Ethiopia: 1 partner
Denmark, Greece, Hungary,Portugal, Slovenia, Spain:
Singapore: 1 partner
1 partner each
Brazil: 1 lead,
1 partner
France: 1 lead, 3 partner

Australia: 3 lead, 3 partner

Figure 4. Map of nationality data for projects active in 2020. Depictions of disputed territories do notindicate a position on the dispute.
The most common partner (i.e., non-lead) country is the US with 9 projects, followed by 4 for
China and the UK, and 3 for Australia, Canada, France, Germany, and Israel. Germany and Israel areinteresting cases because they are each important technology hubs, but neither are the lead country on
any AGI R&D projects. The most common partnerships are between the US and China, at 6partnerships, followed by each of (1) the US and UK and (2) the US and Australia, each with 4
partnerships. The most common non-US partnerships are (1) France and the UK and (2) Israel and theUK, each with 3 partnerships. These tabulations of partnerships include when one country is the leadand the other country is a partner or when both countries are partners. The US has the mostinternational reach with connections to 21 countries, followed by Switzerland with connections to 18
countries. This can be attributed to the Human Brain Project, a large, international consortia based in
Lausanne, Switzerland. (BlueBrain is also based in Lausanne.)
As in 2017, the AGI R&D landscape remains dominated by the US and its allies. China isemerging as arguably the primary geopolitical rival to the US (Allison 2017), a competition that
extends to the realm of AI (Maas 2019). However, the data do not show intense competition on AGI
R&D. China is only the lead country on 5 projects, which remains unchanged from 2017. China is apartner on 4 projects, a decrease from 3 in 2017. Of China’s 9 total projects, the US is involved with 6,
suggesting a relationship that is more cooperative than competitive. Russia is another major US
adversary. It now is the lead on 3 and partner on 2 AGI R&D projects, up from 3 lead and 1 partner in2017. This modest increase still leaves it with a relatively small overall AGI sector.
Other world regions remain underrepresented. There are only 3 projects in the Middle East: 1 basedin Iran (AGI Brain), 1 based in Turkey (Omega), and 1 partnered with Turkey (Human Brain Project).
There is 1 project based in Pakistan (NiHa). There are only 2 projects based in India and 2 others
partnered there. There is just 1 project (OpenCog) with a presence in Africa, a partner of Ethiopia.
There are just 2 projects with a presence in Latin America: Drayker, based in Brazil, andSingularityNET, partnered with Brazil. Given the potential global significance of AGI, a case could bemade that there should be greater geographic diversity in AGI R&D. Alternatively, the geographic
concentration of AGI R&D projects could be taken as a reason to give underrepresented areas a greater
29

voice in other aspects of AGI governance. Or it could be a reason to focus policy initiatives on theregions where the projects are, on grounds that this is where policy is most needed.
The results presented in this section may understate the geographic breadth of AGI R&D. There are
two reasons for this. First, the list of partner countries is highly sensitive to coding judgments about
which institutions to count as partners. The survey only codes collaborating institutions as partners
when they have a sufficient degree of collaboration. What classifies as “sufficient” is a coding
judgment that could reasonably be made in a variety of ways. For example, many projects havecollaborations in the form of co-authorship on publications. These collaborations were generally coded
as insufficient unless the publication was central to the project. For some projects, such as ACT-R andBlue Brain, this results in projects being coded as having no partner countries even though they have
many international collaborators. Including countries with this more limited form of partnership shows
greater geographic diversity; it would include such countries as Mexico, Morocco, Sri Lanka,
Venezuela, and Zimbabwe.
Second, many projects have open-source code. This code enables AGI R&D to be conductedanywhere in the world. It is thus possible that there are other countries involved in AGI R&D, perhaps
a large number of other countries. The identification of countries whose participation consists
exclusively of contributions to open-source code is beyond the scope of this paper.

5.6 Stated Goal
Of the 2020 AGI R&D projects, 38 stated humanitarian goals, 26 stated intellectualist goals, 5 statedtranshumanist goals (AGI Laboratory, Brain2Bot, NDEYSS, Sigma, SingularityNET), 3 stated profit
goals (AGi3, Curious AI, Xephor Solutions), 3 stated ecocentric goals (FTAGIS, SingularityNET,
Susaro), and 3 stated animal welfare goals (Human Brain Project, proposing brain simulation to avoid
animal testing, and FTAGIS and SingularityNET, both seeking to benefit sentient beings). There are
18 projects with unspecified goals. Some projects stated multiple goals: 16 projects stated 2 goals, 1
project stated 3 goals (Human Brain Project), and 2 projects stated 4 goals (FTAGIS, SingularityNET).
Figure 5 summarizes the stated goal data. For comparison, the 2020 survey finds that in 2017, 33
projects stated humanitarian goals, 28 stated intellectualist goals, 4 stated profit goals, 3 statedtranshumanist goals, 4 stated ecocentric goals, 3 stated animal welfare goals, and 17 had unspecified
goals, with 17 projects stating multiple goals. The 2020 recoding of the 2017 survey projects finds 19
projects stated humanitarian goals, 23 stated intellectualist goals, 1 stated profit goals, 2 statedtranshumanist goals, 3 stated ecocentric goals, 2 stated animal welfare goals, and 10 had unspecified
goals, with 12 projects stating multiple goals.
In the 2020 survey, humanitarianism and intellectualism remain the two most common state goals,
but their relative frequency is reversed in comparison to the 2017 survey. The 2017 survey found more
intellectualist projects, whereas the 2020 survey finds more humanitarian projects. This finding isconnected to a similar finding for institution types, with the 2020 survey finding more private
corporation projects and the 2017 survey finding more academic projects (Section 5.2). As the 2017
survey first identified, corporate projects (whether private or public) tend to state humanitarian goals,
while academic projects tend to state intellectual goals. Among the 2020 projects, 26 of 45 corporate
projects state humanitarian goals, while 12 of the 15 academic projects state intellectualist goals.
Likewise, the increase in humanitarian projects in the 2020 survey relative to the 2017 survey is driven
mainly by the increase in private corporation projects.
Of the 9 projects with military connections, 8 state intellectualist goals. The lone exception is Cyc,
which states humanitarian goals. China Brain project states both intellectualist and humanitarian goals,
while Sigma states intellectualist and transhumanist goals. The preponderance of intellectualist projects
is again related to the projects’ academic character. Of the 8 intellectualist projects with military30

connections, 6 are at academic institutions and 2 are at the Chinese Academy of Science, whichclassifies as a government institution but is nonetheless of a heavily academic character. The lone nonintellectualist project, Cyc, is the only project based exclusively at a corporation. (Soar is bothacademic and corporate.) These results suggest the conclusion that the goals of projects with militaryconnections are driven mainly by the projects themselves and their host institutions, and not driven by
the militaries they are connected to. This conclusion further supports the Section 5.4 finding thatmilitaries have relatively limited involvement in AGI R&D at this time.
40
2017 original
2017 updated

30

2020

20

10

0

Animal
WelfareEcocentric

HumanitarianIntellectualist

ProfitTranshumanist Unspecified

Figure 5. Summary of stated goal data. The figure shows more data points than the corresponding
number of projects because some projects have multiple stated goals. 2017 original (gray) is the set ofprojects identified in the 2017 survey recoded with the 2020 methodology. 2017 updated (black) is theset of projects active in 2017 and identified in the 2020 survey. 2020 (red) is the set of projects active
in 2020. All projects are coded according to the 2020 coding methodology.
The results presented in this section may understate the extent of the goal of profit held by AGI
R&D projects. Only 3 projects explicitly stated a profit goal (AGi3, Curious AI, and Xephor Solutions)despite there being 45 projects at for-profit corporations. Some of the other corporate projects had avisible orientation toward profit without explicitly stating it as a goal. These projects express interest incommercializing AGI and marketing it as a product or business solution (e.g., Deepbrainz, Graphen,and Intelligent Artifacts). The profit orientation is suggestive of a profit goal but is not definitive
evidence. Alternatively, projects could seek to commercialize AGI as a means to other ends, such as
benefiting humanity. Indeed, 3 corporate projects explicitly reject profit as a goal (GoodAI, Vicarious,and WILLIAM). Nonetheless, the actual number of projects with the goal of profit may be larger than
the number of projects that have explicitly stated this goal in publicly visible statements. Thispossibility is especially important in light of the larger number of corporate projects identified in the2020 survey.
Finally, it should be noted that the coding of stated goals was especially interpretative. Many
projects do not state their goals prominently or in philosophically neat terms. This includes projects
that made statements that were profit-oriented but did not explicitly state a profit goal. It also includes
projects with other types of goals. For example, DeepMind lists climate change as an important
application. This could be either ecocentric or humanitarian or both, depending on why DeepMind
seeks to address climate change. It was coded as humanitarian because it was mentioned in the context
of “helping humanity tackle some of its greatest challenges,” but it is plausible that ecocentrism was
31

intended. The same sort of ambiguity is present for some other projects as well. Therefore, the results
for stated goals should be viewed more loosely than the results for other project attributes.

5.7 Engagement on Safety
Of the 2020 AGI R&D projects, engagement on safety could only be identified for 34 projects, which
is approximately half of the projects. Of these 34 projects, 18 were found to be active on safety, 12 aremoderate, and 4 are dismissive. Figure 6 summarizes the engagement on safety data. For comparison,
the 2020 survey finds that in 2017, 16 projects were active on safety, 11 were moderate, 4 weredismissive, and 39 were unspecified. The 2020 recoding of the 2017 survey projects finds 13 projects
were active on safety, 4 were moderate, 2 were dismissive, and 26 were unspecified.
40
2017 original
30

2017 updated
2020

20

10

0
ActiveModerate

Dismissive

Unspecified

Figure 6. Summary of engagement on safety data. 2017 original (gray) is the set of projects identified
in the 2017 survey recoded with the 2020 methodology. 2017 updated (black) is the set of projects
active in 2017 and identified in the 2020 survey. 2020 (red) is the set of projects active in 2020. All
projects are coded according to the 2020 coding methodology.
Relative to the 2017 survey, the 2020 survey shows a greater portion of projects that are active ormoderate on safety, but these projects remain a minority in comparison to projects that have anunspecified engagement on safety. This provides some empirical support for the common assumption
in prior AGI policy literature of AGI developers who want to proceed with inadequate regard for safety(Section 2.3). This survey’s focus on publicly available data may overstate the neglect of safety
because some projects may pay attention to safety without stating it publicly. Still, the data is strongly
suggestive of widespread neglect of safety among AGI R&D projects.
Among the 30 projects active in 2020 for which active or moderate engagement on safety was
identified, some further trends are apparent. These 30 projects include 11 of the 20 projects with purely
humanitarian goals, yet only 4 of the 11 projects with purely intellectualist goals (FLOWERS, Leabra,
NARS, NNAISENSE), 7 of 18 projects with unspecified goals, 1 project with ecocentric goals(Susaro), and 1 project with animal welfare, ecocentric, humanitarian, and intellectualist goals(FTAGIS). The 30 projects also include 19 of the 42 projects based purely at corporations, 3 of 4
projects based purely at nonprofit organizations (Big Mother, OpenCog, and WBAI), 5 of 13 projects
based purely at academic institutions, and 1 of 2 projects based in part at an academic institution(FLOWERS). This suggests a cluster of projects that are broadly engaged on the impacts of AGI R&D,
including ethics questions about what the impacts should be and risk/safety questions about whether
32the desired impacts will accrue. This cluster is located predominantly outside of academia. Meanwhile,
there continues to be a cluster of projects that are predominantly academic and view AGI in largely
intellectual terms, disregarding safety in their pursuit of AGI. These trends suggest the importance ofproposals to strengthen risk and ethics practices sometimes suggesting that adequate training could
make AGI safety to be a reasonably tractable task among academic AGI R&D projects, such as via
research review boards (Yampolskiy and Fox 2013).
The corporate projects new to the 2020 survey show a range of stances on safety. There are 2
projects explicitly focused on potential long-term risks and present thorough discussions of these risks
and how to address them (Brain Simulator II, Deepbrainz). There is 1 project is solely focused on nearterm risks that may arise such as job loss, racial profiling and biases, and misinformation (CerenautResearch). There are 2 projects that acknowledged potential AGI risks, but brushed these risks off as
implausible if AGI is built correctly (Monad and Susaro). These results show a diverse range ofperspectives on safety and point to the need for a broad-based conversation about safety issues in AGI
R&D.

5.8 SizeOf the 2020 AGI R&D projects, 30 projects are small, 23 are small-medium, 13 are medium, 2 are
medium-large (MSR AI and Vicarious), and 4 are large (Blue Brain, DeepMind, Human Brain Project,
and Open AI). Figure 7 summarizes the size data. For comparison, the 2020 survey finds that in 2017,28 projects were small, 21 were small-medium, 15 were medium, 2 were medium-large, and 4 werelarge. The 2020 recoding of the 2017 survey projects finds 13 projects were small, 12 were smallmedium, 14 were medium, 2 were medium-large, and 4 were large.
40
2017 original
2017 updated

30

2020

20

10

0

Small

SmallMedium

MediumMediumLarge

Large

Figure 7. Summary of size data. 2017 original (gray) is the set of projects identified in the 2017 survey
recoded with the 2020 methodology. 2017 updated (black) is the set of projects active in 2017 andidentified in the 2020 survey. 2020 (red) is the set of projects active in 2020. All projects are coded
according to the 2020 coding methodology.
Of the projects new to the 2020 survey, 25 are small, 12 are small-medium, and 2 are medium(Graphen and Sanctuary AI). The 26 small projects include Basic AI, which is new to the 2020 survey
but inactive in 2020. Of the 2 medium projects, only 1 project (Graphen) was active in 2017. These
numbers show that while the 2017 survey failed to identify a significant number of projects, it may
33

have identified all of the larger projects and all but 1 (Graphen) of the medium projects. It does standto reason that larger projects would be easier to find and are less likely to be overlooked. Likewise,
these numbers mean that although the 2020 survey presents new trends in AGI R&D (in particular, alarger total number of projects, with a larger portion in private corporations and stating humanitarian
goals and a smaller portion with open-source code), these new trends may represent a small portion of
total AGI R&D because the newly identified projects are generally smaller.
The variation between the smallest and largest projects is considerable. The smallest projects
appear to be hobbies or side projects of a single individual, with personnel less than one full-time
person. The largest projects have teams of over 100 full-time personnel with extensive financial andcomputational resources. It may be the case that a large portion of progress on AGI R&D is driven by arelatively small number of projects, though assessment of progress on AGI R&D is beyond the scopeof this paper.

5.10 Other Notable Projects
As documented in Appendix 3, there were 69 other notable projects recorded in the process ofidentifying AGI R&D projects. These include 40 inactive AGI R&D projects, 15 projects that are not
R&D, 29 projects that are not AGI, with some projects in more than one of the aforementioned
categories. Some projects fell into more than one category of exclusion. Many of these projects wereactive AGI R&D projects in the 2017 survey but are no longer active on AGI R&D. Unlike with active
AGI R&D projects, no attempt made to be comprehensive in the identification of other notableprojects. It is likely that some notable projects are not included. The list of other notable projects andbrief details about them are presented in Appendix 3.
The 40 inactive projects are largely academic, such as 4CAPS, led by Marcel Just of Carnegie
Mellon University, and OSCAR, led by John Pollock of University of Arizona. They varied
considerably in duration, from a few years (e.g., AGINAO, active from 2011 to 2013) to over a decade(e.g., CHREST, active from 1992 to 2012). Of the inactive projects, 12 are projects that were active in2017 and documented in the 2017 survey, including DeSTIN (led by Itamar Arel and colleagues at the
University of Tennessee) and MicroPsi (led by Joscha Bach of the Harvard University).
The 29 projects that are not AGI include many AI groups at large computing technology
corporations. 7 such corporations were searched carefully for AGI projects and found not to have any:
Alibaba, Amazon, Apple, Intel, SalesForce, Sony, and Twitter. Given these corporations’ extensive
resources, it is notable that they do not appear to have any publicly identifiable AGI projects.
Additionally, the IBM Cognitive Computing Project and the Intel Loihi Chip are hardware projects ofrelevance to AGI, though neither constitutes AGI R&D. Finally, in addition to DeepMind, 2 otherprojects at Google were considered: Google Brain and Quantum AI Lab. While Google Brain has donesome AGI work with DeepMind, it focuses on narrow AI.
The 15 projects that are not R&D cover a mix of different aspects of AGI. Some focus on basic
science, including several brain projects (e.g., the BRAIN Initiative at the US National Institutes of
Health and Brain/MINDS at Japan’s Ministry of Education, Culture, Sports, Science, and Technology).
Several focus on hardware and software for building AGI (e.g., the IBM Cognitive Computing Project,
the Tianjic Chip at Tsinghua University, and Hanson Robotics). There are 2 projects that focus onsafety aspects of AGI design (Center for Human-Compatible AI at University of California, Berkeley
and the Machine Intelligence Research Institute). This is not a comprehensive list of projects working
on non-R&D aspects of AGI. For example, projects working on AGI ethics, risk, and policy were notincluded because they are further removed from R&D.

34

5.11 Other Projects
There were 39 other projects were recorded in the process of identifying AGI R&D projects. This list
includes projects that self-reported a focus on AGI or something AGI-related, but showed little or noapparent AGI R&D. These projects show limited relevance to AGI R&D and are therefore documented
only briefly in Appendix 4.
Most of these projects are small corporate projects listed on Crunchbase or GitHub. The projects’
profiles use keywords such as “AGI” and “superintelligence,” but their actual work does not appear tobe focused on AGI. It is possible that some of these projects are in fact performing AGI R&D; all thatcan be concluded in this survey is that public evidence of AGI R&D was not identified. Anotherpossibility is that some of the projects may be using terms related to AGI to draw more attention tothemselves, such as via search engine optimization. This possibility suggests that AGI may beperceived as a valuable brand to associate with even for projects that are not actually working on AGI.

6. Conclusion
Despite the seemingly speculative nature of AGI, R&D towards building it is already happening. Thissurvey identifies 72 AGI R&D projects spread across 37 countries in 6 continents, many of which are
based in major corporations and academic institutions, and few of which are large and heavily funded.
Given that this survey relies exclusively on openly published information, this should be treated as alower bound for the total extent of AGI R&D. Thus, regardless of how speculative AGI itself may be,
R&D towards it is clearly very real. Given the potentially high stakes of AGI in terms of ethics, risk,
and policy, the AGI R&D projects warrant ongoing attention.

6.1 Main Findings
Regarding ethics, the major trend is the large number of projects with humanitarian goals and thesomewhat smaller number with intellectualist goals, with the former coming largely from corporate
projects and the latter from academic projects. The survey’s reliance on public statements may
understate the prevalence of profit goals, especially among the many corporate projects. Still, among
the projects not motivated by intellectual goals, there does seem to be a bit of a consensus for at least
some form of humanitarianism, and not for other types of goals commonly found in AGI discourses,
such as transhumanism. Meanwhile, the intellectualist projects indicate that academic R&D projects
still tend to view their work in intellectual terms, instead of in terms of societal impacts or other ethicalfactors, even for potentially high-impact pursuits like AGI.
Regarding risk, two points stand out. First, a clear majority of projects had no identifiable
engagement on safety. While many of these projects without identifiable engagement on safety aresmaller in size, some of these projects are also larger and with considerable funding. The lack ofidentifiable engagement suggests that many projects are not active on safety, though it is possible thatsome are conducting safety activities exclusively in a private fashion. Second, some trends suggest thata risky race dynamic may be avoidable. One is the concentration of projects, especially larger projects,
in the US and its allies, which can facilitate both informal coordination and formal public policy.
Another is the modest consensus for humanitarianism, again especially among larger projects, which
could reduce projects’ desire to compete against each other. Some projects are interconnected via
shared personnel, parent organizations, AGI systems development, and participation in the samecommunities, such as the AGI Society. This suggests a community working together towards acommon goal and sharing of best practices, not competing against each other.
35

Regarding policy, several important points can be made. One is the concentration of projects in the
US and its allies, including most of the larger projects. This could greatly facilitate the establishment of
AGI policy with jurisdiction over most AGI R&D projects, including all of the larger ones. Anotherimportant point is the concentration of projects in academia and corporations, with relatively little ingovernment or with military connections. Each institution type merits its own type of policy, such as
review boards in academia and financial incentive structures for corporations. The potential for AGI
R&D-profit synergy (Section 5.2) could be especially important here, determining both the extent ofcorporate R&D and the willingness of corporations to submit to restrictive regulations. This survey
finds hints of AGI R&D-profit synergy, but not the massive synergies found for certain other types of
AI. Finally, the preponderance of projects with at least some open-source code complicates any policy
effort, because the R&D could in principle be done by anyone, anywhere.

6.2 Limitations and Future Work
The above conclusions seem robust given this study’s methodology, but other methodologies could
point in different directions. For example, the published statements about goals suggest a consensus
towards humanitarian and intellectualist goals, but this could miss unpublished disagreements on thespecifics of these goals. A humanitarian goal for one project could seek greater economic prosperity,
whereas another humanitarian project could seek greater freedom or happiness. Additionally, thepublished statements about goals used in this survey could deviate from projects’ actual goals if the
projects are not entirely honest in their published statements. Some projects may recklessly pursue
self-interested goals while presenting a responsible, ethical front to the public. These possibilities
suggest a more difficult policy challenge and larger AGI risk. Alternatively, this study could
overestimate the risk. Perhaps many projects have similar goals and concerns about safety, even if they
have not published any statements to this effect. Given the overlap in researchers, funding sources, andinstitutions across the spectrum of projects, this may be quite likely. Future research using othermethodologies, especially interviews with people involved in AGI R&D, may yield further insights.
A different complication comes from the possibility that there are other AGI R&D projects not
identified by this study. Some projects may have a public presence but were simply not identified inthis study’s searches, despite the efforts made to be comprehensive. This study is especially likely tomiss projects that work in non-English languages, since searches were only conducted in English.
Furthermore, there may be additional AGI R&D projects that have no public face at all. This could
conceivably include the secret government military projects. The projects identified with militaryconnections are generally small and focused on mundane (by military standards) tactical issues, notgrand ambitions of global conquest. However, more ambitious military goals would most likely not be
stated publicly in order to retain strategic advantage during the development and eventual use of AGI.
It could be argued that some of the more mundane, tactical issues the military has interest in are merely
preliminary research areas that need to be addressed in order to successfully develop AGI. For
example, DARPA has multiple research programs that are tangentially related to AGI development
such as µBRAIN and CREATE, which focus on neuromorphic computing and autonomous systems.9
Though these programs, and others, do not use any language explicitly stating interest in thedevelopment of AGI, they seem to be preliminary steps toward AGI development or tangentiallyrelated projects. Given the stakes, it is important to remain vigilant about the possibility that mostmilitary and government funded programs may not be public knowledge.
Another, more likely possibility is that there are additional private sector projects that do not yet
have a public presence, either because they are deliberately operating in ‘stealth mode’ or because they
just do not have an active public presence yet. This study identified 6 projects that were active in 20179https://www.darpa.mil/our-research

36but not publicly visible until after 2017. This finding suggests that it is likely that there are someprojects that are active in 2020 but not yet publicly visible. The possibility of projects that are not (yet)publicly visible means that the 72 projects identified in this survey should be treated as a lower bound.
A different potential source of additional AGI R&D projects is the vast space of projects focusedon deep learning and related techniques. Over the past three years, projects are increasingly more
focused on deep learning, cognitive architecture, neuromorphic computing, and artificial neuralnetworks. These projects were generally excluded from this survey because there are too many toassess in this study’s project-by-project methodology, and because there are diverging opinions onwhether these projects rate as AGI R&D. If AGI could come from these techniques, the AGI R&D
landscape would look substantially different from the picture painted in this paper, with major
consequences for ethics, risk, and policy. Appendix 4 was created in part because of the increase inprojects using these terms in conjunction with self-described AGI development. It is difficult to assess
which techniques are more promising for developing AGI and ultimately which projects should be paid
more attention to. Therefore, an important direction for future research is to assess the possibility andlikelihood that AGI could come from deep learning and other techniques, and then determine theimplications for ethics, risk, and policy.
Another worthwhile direction for future research is on projects’ capability to build AGI. This study
includes project size as a proxy for capability, but it is an imperfect proxy. Capability is the more
important attribute for understanding which projects may be closest to building AGI. More capableprojects may warrant greater policy attention, and a cluster of projects with similar capabilities could
lead to a risky race dynamic. (Project size is important for other reasons, such as projects’ pull on thelabor market for AGI researchers or their potential for political lobbying.) Project capacity could beassessed via attention to details of projects’ performance to date, the viability of their technicalapproaches to AGI, and other factors. Given the ethics, risk, and policy importance of project capacity,
this is an important direction for future research. However, despite observing and tracking the
aforementioned details, there is still the possibility that the project to successfully develop AGI firstmay come from an unexpected project, such as a small, underfunded group that just happens to getlucky.
Additionally, another consideration is the value of diversity and inclusivity within research groups.
There have been public exposés of the biases of AI in human resources departments and hiring, as wellas in policing and law enforcement, and healthcare (Dastin 2018; Ray 2020; Kaushal et al. 2020).
Issues of AI bias have been identified in AI systems that are much less powerful than AGI could be.
The stakes for bias in AGI systems could be much greater. This suggests a role for surveying thedemographics of AGI R&D projects in tandem with analyses of potential bias in AGI systems.
Finally, future research could also look at other actors involved in AGI. In addition to the R&D
projects, there are also R&D groups that work on hardware and safety measures related to AGI; people
who study and work on AGI ethics, risk, and policy; and “epistemic activists” who promote different
understandings of AGI. Each of these populations could play a significant role in ultimate AGI
outcomes with important implications for ethics, risk, and policy. Empirical study of these populations
could clarify the nature of the work being done, identify gaps, and suggest trends in AGI R&D.

6.4 Concluding Remarks
Overall, the present study shows that discussion of AGI ethics, risk, and policy can have a soundempirical basis; it need not be based solely on speculation about hypothetical AGI R&D projects andactors. The present study contributes to this empirical basis by being just the second survey of AGI
R&D projects and the first to track changes over time. Given the potentially high stakes of AGI,
hopefully this research can be used to improve AGI outcomes.
37Appendix 1. Active AGI R&D Projects
The following pages document the 70 AGI R&D projects that are active in 2020. Each project entry
lists the project website, a brief summary of the project, and details the project’s attributes.
Projects with an asterisk (*) next to their name indicate that the project is new to the 2020 survey.
For projects that appear in both the 2017 and 2020 surveys, some data points have been coded
differently in the two surveys. These data points are annotated as follows: Red font indicates a data point that has been changed due to a change in coding methodology. For
example, the 2020 survey uses a more restrictive standard for coding partner countries. As a result,
AERA is coded in the 2020 survey as having no partner countries, whereas in the 2017 survey it
was coded as having a partner country of Switzerland. Full methodology changes are detailed in
Section 4.
 Blue font indicates a data point that has been changed due to change in the project that occurred
between 2017 and 2020. For example, Animats added Australia as a partner country in 2020.
 Green font indicates a data point that has been changed to correct a coding error or oversight in the2017 survey. For example, Leabra was coded as unspecified on safety in the 2017 survey; it shouldhave been coded as moderate due to information in a 2017 paper that was overlooked in the 2017
survey.
Referenced websites were active when project data was collected and coded during June to September
2020. Some websites may have since become inactive. Many of these websites can be viewed via theInternet Archive (https://archive.org).

38

ACT-R
Main website: http://act-r.psy.cmu.edu
ACT-R, an acronym for Adaptive Control of Thought-Rational, is a research project led by John
Anderson of Carnegie Mellon University. It is a theory of human cognition and a computationalframework for simulating human cognition.10 ACT-R was briefly connected to Leabra via the SAL
project.11
Lead institution: Carnegie Mellon University
Partner institutions: none The ACT-R website lists 110 collaborating institutions across 19 countries.12 However, this list
includes researchers who have previously contributed to ACT-R research and have since moved on
to other projects outside of ACT-R. This list also does not include some co-authors of recent ACTR papers.13 No active partner institutions could be confirmed from the website, and thus none arecoded here, though there may be active partner institutions.
Type of institution: academic
Open-source: yes14
Military connection: yes15
The US Air Force Institute of Technology, US Air Force Research Lab, US Army Research Lab, US
Naval Undersea Research Center, and US Military Academy are listed among the 110 collaborating
partners on the ACT-R website.16
Lead country: USA
Partner countries: none
Stated goals: intellectualist The main description of ACT-R on its website is exclusively about academic research with nomention of broader impacts.17
Engagement on safety: unspecified
Size: medium

10

http://act-r.psy.cmu.edu/about and http://act-r.psy.cmu.edu/peoplepages/ja/ja-interests.html
See the entry for Leabra in Appendix 1 for more information.
12
http://act-r.psy.cmu.edu/people
13
For example, Wirzberger et al. (2020) has lead authors J.F. Krems and G.D. Rey, who are not listed at http://actr.psy.cmu.edu/people.
14
http://act-r.psy.cmu.edu/software
15
Funding from the US Office of Naval Research was previously reported in Zhang et al. (2016).
16
http://act-r.psy.cmu.edu/people/
17
http://act-r.psy.cmu.edu/about
11

39

AERA
Main website: http://www.ru.is/faculty/thorisson
AERA, an acronym for Autocatalytic Endogenous Reflective Architecture, is led by Kristinn
Thórisson of CADIA at Reykjavik University and the Icelandic Institute of Intelligent Machines(IIIM).18 The project aims “to both understand the mind and build a practical AGI system.”19 AERA is
currently used “to study machine understanding, teaching methodologies for artificial learners, [and]even the development of ethical values.”20
Lead institution: Reykjavik University
Partner institutions: Icelandic Institute for Intelligent Machines Thorisson publishes AGI research with researchers at various academic institutions, though none
are listed as official institutional partnerships.21
Type of institution: academic
Open-source: no
Military connection: no Thórisson criticizes military AI in the Icelandic Institute for Intelligent Machines (IIIM) ethics
policy.22
Lead country: Iceland
Partner countries: none (2017 survey: Switzerland)Stated goals: humanitarian, intellectualist Thórisson’s website links to the ethics policy of IIIM, which aims to “to advance scientificunderstanding of the world, and to enable the application of this knowledge for the benefit andbetterment of humankind.” It further aims to be “for the betterment of society, human livelihoodand life on Earth.” This mention of life on Earth suggests ecocentrism, but all other text ishumanitarian or intellectualist.23
Engagement on safety: active The AERA group has written about enhancing safety during AGI self-improvement, arguing thatcertain design principles would make it easy to achieve safety.24
Size: small-medium

18

Nivel et al. (2013)
http://www.ru.is/faculty/thorisson20
http://www.ru.is/faculty/thorisson21
http://alumni.media.mit.edu/~kris/select_publ.html
22
http://www.iiim.is/ethics-policy/23
http://www.iiim.is/ethics-policy/3
24
Steunebrink et al. (2016)
19

40

AGI Brain*
Main website: https://www.researchgate.net/project/Artificial-General-Intelligence-Brain-AGI-Brain
AGI Brain was founded by Mohammadreza Alidoust in 2009, though it only shows activity in 20182019.25 The project’s stated goal is “to build an Artificial General Intelligence (AGI) Brain, i.e., athinking machine that can perform any intellectual task that a human being can.”26 Alidoust is affiliatedwith AGT Co. in Mashhad, Iran.27
Lead institution: AGI Brain
Partner institutions: none
Type of institution: none
Open-source: no
Military connection: unspecified
Lead country: Iran
Partner countries: none
Stated goals: unspecified
Engagement on safety: unspecified
Size: small

25

https://www.researchgate.net/project/Artificial-General-Intelligence-Brain-AGI-Brain. An earlier paper (Alidoust andRouhani 2015) presents work that appears to be a precursor to the 2018-2019, though this earlier paper is not explicitly
identified as part of the AGI Brain project.
26
https://www.researchgate.net/project/Artificial-General-Intelligence-Brain-AGI-Brain
27
https://ir.linkedin.com/in/mohammadreza-alidoust

41

AGI Laboratory*
Main website: https://agilaboratory.com
AGI Laboratory is a private corporation that leads several AGI-related activities, including the R&D
project Mediated Artificial Superintelligence (MASI). It is an active research group with publications
in a variety of notable conferences and journals.28
Lead institution: AGI Laboratory
Partner institutions: Microsoft, Institute for Education, Research, & Scholarships (IFERS), andUnanimous AI29
Type of institution: private corporation
 Artificial General Intelligence, Inc. (AGI Laboratory) is a privately held ‘C’ Corp.30
Open-source: no
Military connection: unspecified
Lead country: USA31
Partner countries: none
Stated goals: humanitarian, transhumanism
 A website for the MASI project lists its goal as “uplifting humanity through the digitaltransformation, collective superintelligence, and governance.”32
 An AGI Lab paper explores the possibility of “identical neural substrates” for “humans and nonbiological human counterparts.”33
 A separate paper studies the ethical treatment of sentient AGI systems.34
 Chief Research Scientist David Kelley is also the founder of IAmTranshuman.35
Engagement on safety: active
 AGI Laboratory has an ethics webpage with extensive attention to AGI safety.36
Size: small

28

https://agilaboratory.com/research/https://agilaboratory.com/support/
30
https://agilaboratory.com/support/; See also author locations: Kelley and Atreides (2020)31
https://agilaboratory.com/support/
32
https://uplift.bio/
33
Dambrot (2020)
34
Kelley and Atreides (2020)35
https://iamtranshuman.org/
36
https://agilaboratory.com/ethics/
2942

AGi3*
Main website: https://agiinnovations.com
AGi3, also known as AGI Innovations Inc., is a private corporation that was founded in 2013 by Peter
Voss with a “long-term goal of developing and commercializing” AGI. Its R&D is based on Adaptive
A.I. Inc (a2i2), founded in 2001.37 AGi3 has an active commercial product called Aigo, which aims toprovide “highly intelligent, hyper-personalized conversational assistants for everyone.”38
Lead institution: AGi3
Partner institutions: none
Type of institution: private corporation
Open-source: no
Military connection: unspecified
Lead country: USA AGi3 lists its location as Los Angeles.39
Partner countries: none
Stated goals: humanitarian, profit AGi3’s website states a “goal of developing and commercializing” AGI.40
 In a 2015 interview about AGi3, Voss predicts that AGI will solve many of humanity’s problems.41
Engagement on safety: moderate In the same 2015 interview, Voss says there are “risks and dangers” involved with AGI.42
Size: small-medium

37

https://agiinnovations.com/history
https://www.aigo.ai/about-us39
http://www.agi-3.com/contact.html
40
https://agiinnovations.com/history
41
https://www.youtube.com/watch?v=brLeWnrq7N8 (4:40)
42
https://www.youtube.com/watch?v=brLeWnrq7N8 (5:10)
38

43

AIBrain*
Main website: https://aibrain.com
AIBrain is an AI company that seeks to build a “human-like AI” that automates “the entire process ofcognitive reasoning.”43AIBrain was founded by Dr. Richard H. Shinn in Seoul, South Korea in 1997. It
expanded to Palo Alto, California in 2012 and to Shenzhen, China, in 2017.44 AIBrain focuses on threepieces of technology: AIBrain, AI Core, and Memory Graph.45
Lead institution: AIBrain
Partner institutions: none
Type of institution: private corporation
Open-source: yes46
Military connection: unspecified
Lead country: USA47
Partner countries: China, Germany, South Korea48
Stated goals: intellectualist
 AI Brain states that it has the “grand aim to augment human intelligence with AI.”49
Engagement on safety: unspecified
 AI Brain Chief Scientist Steve Omohundro previously worked on AI safety,50 though this work was
not done for AIBrain.
Size: small-medium

43

https://aibrain.com/about/https://aibrain.com
45
https://aibrain.com/solutions/46
https://github.com/aibraininc47
See the AIBrain website footer.
48
See the AIBrain website footer.
49
https://aibrain.com/about/50
https://aibrain.com/about/who-we-are/ and Omohundro (2016)
44

44

AIXI
Main website: http://www.hutter1.net/ai/aixigentle.htm
AIXI is led by Marcus Hutter of Australian National University. AIXI is based on a “meta-algorithm”
that searches the space of algorithms to find the best one for AGI.51 Hutter proves that AIXI will findthe most intelligent AI if given infinite computing power. While this is purely a theoretical result, it
has led to approximate versions implemented in computer code.52 Hutter is currently on leave from theAustralian National University and is working at Google DeepMind in London, possibly pausing his
work on AIXI.53
Lead institution: Australian National University
Partner institutions: none
Type of institution: academic
Open-source: yes54
Military connection: unspecified
Lead country: Australia
Partner countries: none
Stated goals: humanitarian (2017 survey: unspecified) In his book Universal Artificial Intelligence, Hutter states that human-level AI “would haveenormous implications on our society” and is therefore worth pursuing “to reap the benefits.”55
Engagement on safety: unspecified
Size: small

51

Goertzel (2014) p.25
Veness et al. (2011)53
http://www.hutter1.net/index.htm
54
https://github.com/aslanides/aixijs and http://www.hutter1.net/aixijs/index.html
55
http://www.hutter1.net/ai/uaibook.htm#oneline5245

Animats
Main website: https://github.com/ni1s/animats
Animats is a small project developed for the First International Workshop on Architectures for
Generality & Autonomy56 and the 2017 AGI conference,57 which seeks to build AGI based on animal
intelligence. 58 The project was originally a collaboration between researchers at universities in Sweden
and the United States, and the Swiss company NNAISENSE,59 but it seems most researchers from the2017 project have since moved on to other projects. Animats currently seems to be led by Claes
Strannegård.60
Lead institution: Chalmers University of Technology
Partner institutions: University of Gothenburg, Harvard University, Deakin University,
NNAISENSE
Type of institution: academic (2017 survey: also private corporation)
Open-source: yes61
Military connection: unspecified (2017 survey: no)
Lead country: Sweden
Partner countries: Australia,62 Switzerland, USA63 (2017 survey: Switzerland and USA only)Stated goals: unspecified
Engagement on safety: moderate64
Size: small

56

http://cadia.ru.is/workshops/aga2017
Strannegård et al. (2017a)
58
Strannegård et al. (2017b)
59
Strannegård et al. (2017b)
60
https://www.chalmers.se/en/staff/Pages/claes-strannegard.aspx
61
https://github.com/ni1s/animats
62
Strannegård et al. (2020).
63
Strannegård et al. (2017b)
64
Strannegård et al. (2018), p.78.
57

46

ANSNA*
Main website: https://github.com/patham9/ANSNAThe ANSNA (Adaptive Neuro-Symbolic Network Agent) project was created by Patrick Hammer, aresearch assistant at Temple University in Dr. Pei Wang’s research team.65 ANSNA is situated withinthe “field of AGI,” 66 and is a variation of the NARS and OpenNARS projects.67 Hammer states thatthe goal of the ANSNA project is to try “to combine [his] most valuable insights about Dr. Pei Wang’s
NARS (Non-Axiomatic Reasoning System), Jeffrey Hawkins HTM (Hierarchical Temporal Memory),
Tony Lofthouse’s ALANN (Adaptive Logic and Neural Network), Rod Rinkus’s Sparsey, Pentti
Kanerva’s SDM (Sparse distributed memory), and [his] own projects of the last decade, for creating anautonomous sensorimotor agent that starts with zero knowledge and organizes its experience into
conceptual units in such an efficient way that it can directly be applied for autonomous systems withrich sensory data.”68
Lead institutions: Temple University
Partner institutions: none
Type of institution: academic
Open-source: yes69
Military connection: unspecified
Lead country: USA
Partner countries: none
Stated goals: unspecified
Engagement on safety: unspecified
 Hammer is a research assistant of Dr. Pei Wang, who has discussed the ethics of AGI, and thereby
Hammer may share similar views.70
Size: small

65

https://www.linkedin.com/in/patrick-hammer-27a248b5/ and Wang et. al (2016)
https://github.com/patham9/ANSNA/blob/master/README.md67
https://www.linkedin.com/in/patrick-hammer-27a248b5/
68
https://github.com/patham9/ANSNA#readme
69
https://github.com/patham9/ANSNA
70
See NARS in Appendix 1
66

47

Apollo Program for AGI*
Main website: https://montrealartificialintelligence.com
The Apollo Program for AGI is a project of Montréal Artificial Intelligence. The Apollo Program waslaunched in 2019; Montréal AI was established in 2003.71 The Apollo program seeks to “pioneer thegeneral-purpose technology, the holy grail of AI, that will define the future” (emphasis original).72
Montréal AI’s overarching goal is to create the largest AI community possible that would intersectwith every area of society, including academia, government, and business.73
Lead institution: Montréal AI
Partner institutions: none
Type of institution: private corporation74
Open-source: no GitHub pages associated with the project do not include AGI code.75
Military connection: unspecified
Lead country: Canada
Partner countries: none
Stated goals: humanitarian, intellectualist The Montréal AI website states the goals are to “orchestrate AGI-First systemic breakthroughs tosolve planetary-scale challenges and to pioneer a new era of superhuman scientific discoveries.”76
 Boucher describes Montréal AI as “a research Company at the forefront of the AI field developing
and commercializing the most significant technology ever created to leverage enterprises andgovernments.”77
Engagement on safety: moderate
 Montréal AI is “preparing a Global Network of Education Centers to pioneer an impactfulunderstanding of AI and to foster a vector for safe humanitarian artificial general intelligence(AGI).”78
Size: small-medium

71

https://www.linkedin.com/pulse/montrealai-research-vincent-boucher/https://montrealartificialintelligence.com
73
http://www.montreal.ai/unicorn.jpg
74
https://www.linkedin.com/company/montreal.ai/about/75
https://github.com/MontrealAI/MontrealAI.github.io and https://github.com/MontrealAI/MontrealAIMaster76
https://montrealartificialintelligence.com
77
https://www.linkedin.com/in/montrealai/
78
https://montrealartificialintelligence.com/
72

48

Astrum*
Main website: https://www.astrum.ai
Astrum was founded in 2019 by Srikanth Srinivas.79 Srinivas has since taken a position at Scale AI,
potentially leaving Astrum with no full-time employees. Astrum’s mission is “dedicated to making
AGI a reality within the next decade.”80
Lead institution: Astrum
Partner institutions: none
Type of institution: private corporation81
Open-source: no
Military connection: unspecified
Lead country: Canada82
Partner countries: none
Stated goals: unspecified
Engagement on safety: unspecified
Size: small

79

https://www.linkedin.com/in/srikanthsrnvs
https://www.astrum.ai/about81
https://www.linkedin.com/company/astrum-ai/about/82
https://www.crunchbase.com/organization/astrum-ai
8049

Baidu Research
Main website: http://research.baidu.com
Baidu Research is an AI research group within Baidu which claimed to be working on AGI in 2018,
though this does not currently appear to be a significant theme for the group.83 It has offices in Beijing,
Shenzhen, and Sunnyvale, California.84 Baidu Research has achieved success in “zero-shot learning” inlanguage processing, in which the AI “is able to understand unseen sentences.”85 Some observers rate
this as a significant breakthrough.86
Lead institution: Baidu
Partner institutions: none
Type of institution: public corporation
Open-source: no Baidu releases some work open-source,87 but it is not AGI.
Military connection: unspecified
 Baidu receives AI funding from the Chinese government for “computer vision, biometricidentification, intellectual property rights, and human-computer interaction.”88
Lead country: China
Partner countries: USA
Stated goals: unspecified
Engagement on safety: unspecified
 Former Baidu Research Chief Scientist Andrew Ng says that Baidu takes safety and ethics
seriously. He expresses his personal views that AI will help humanity, but says that “fears about AI
and killer robots are overblown.”89 This was not in the context of AGI. No direct discussion ofsafety by Baidu Research was found.
Size: small-medium83

http://research.baidu.com/Blog/index-view?id=108
http://bdl.baidu.com/contact_b.html
85
http://research.baidu.com/Blog/index-view?id=99
86
Griffin (2017)87https://github.com/baidu
88
Gershgorn (2017)
89
Maddox (2016)
84

50

Big Mother*Main website: https://bigmother.ai/
Big Mother was founded by Aaron Turner in 2018.90 Its main goal is “to build a maximally-safe,
maximally-benevolent, maximally-trustworthy, autonomous, goal-directed, super-intelligent machine(Artificial General Intelligence) called Big Mother, that is publicly owned by all mankind.”91
Lead institution: Big Mother
Partner institutions: none
Type of institution: nonprofit
Open-source: restricted
 Big Mother members are allowed access to additional resources.92
Military connection: unspecified
Lead country: UK93
Partner countries: none
 Big Mother lists volunteers in Canada, the Netherlands, Poland, Russia, the UAE, the US, and the
UK.94
Stated goals: humanitarian The Big Mother website states, “if we equate ‘human happiness’ with ‘the extent to which true
human preferences are satisfied’ then, simply stated, the net effect of Big Mother’s dominant goal
(top-level objective) will be to (broadly) maximize human happiness and (broadly) minimize‘happiness inequality’.”95
Engagement on safety: active
 Big Mother’s mission statement emphasizes “maximally-safe” AGI.96
 Though the Big Mother website states this project could be active for the next 50-100 years, it alsosays that the project may take longer “if safety requires.”97
Size: small-medium98

90

Turner (2019)https://bigmother.ai
92
https://bigmother.ai/page-1075267
93
Turner is based in Cambridge, England: https://www.meetup.com/CambridgeAGI1/
94
https://bigmother.ai
95
https://bigmother.ai
96
https://bigmother.ai
97
https://bigmother.ai
98
https://bigmother.ai/Sys/Login?ReturnUrl=%2fpage-1075213
91

51

Binary Neurons Network*
Main website: http://8iter.ru/ai.html99
Binary Neurons Network is a project by Ilya Shishkin. A version of the website was available in2016.100 It is “an attempt to create artificial intelligence in the original meaning: AGI, Strong AI,
HLAI, True AI.”101 Shiskin also has a GitHub project called Device, which is the “practicalimplementation of a physical device with the binary neurons.”102
Lead institution: Binary Neurons Network
Partner institutions: none
Type of institution: none
Open-source: yes103
Military connection: unspecified
Lead country: Russia
Partner countries: none
Stated goals: unspecified
Engagement on safety: moderate Shiskin writes, “AI is certainly unsafe, it makes decisions by itself, the more developed it is, themore unpredictable these decisions are. Attempts to instill something in him, he will see through inno time and then there will be no mercy. Our only hope is that AI will look at us orphaned and poor
and, within the framework of humanitarian aid, will solve all our problems for us.”104
Size: small

99

English translation: http://translate.google.com/translate?js=n&sl=ru&tl=en&u=http://8iter.ru/ai.html
https://web.archive.org/web/20161123081444/http://8iter.ru/ai.html
101
https://github.com/cortl0/binary_neurons_network102
https://github.com/cortl0/device
103
https://github.com/cortl0/binary_neurons_network104
http://translate.google.com/translate?js=n&sl=ru&tl=en&u=http://8iter.ru/ai.html
10052

Blue Brain
Main website: http://bluebrain.epfl.ch
Blue Brain is a research project led by Henry Markram that has been active since 2005. Its website
states that its goal is “to build biologically detailed digital reconstructions and simulations of themouse brain.”105 A documentary on Blue Brain that has been ten years in the making will release in
2020.106 Markram also founded the Human Brain Project, which shares a research strategy with Blue
Brain.
Lead institution: École Polytechnique Fédérale de LausannePartner institutions: none The 2017 survey listed 8 partner institutions across 4 countries on a webpage that is now inactive.
Blue Brain may retain these or other partnerships, though insufficient evidence was identified. Blue
Brain researchers have published papers co-authored by researchers at various institutions.
Type of institution: academic
 Blue Brain lists the public corporation IBM as a contributor, making “its researchers available tohelp install the BlueGene supercomputer and set up circuits that would be adequate for simulatinga neuronal network.” This contribution was judged to be too small to code Blue Brain as part
public corporation.
Open-source: yes107
Military connection: unspecified
Lead country: Switzerland
Partner countries: none (2017 survey: Israel, Spain, UK, USA)
Stated goals: humanitarian, intellectualist The Blue Brain website states that “understanding the brain is vital, not just to understand thebiological mechanisms which give us our thoughts and emotions and which make us human, but
for practical reasons,”108 the latter including computing, robotics, and medicine. These applications
are broadly humanitarian, though a more muted humanitarianism than what is found for otherprojects.
Engagement on safety: unspecified
Size: large (2017 survey: medium-large)

105

https://www.epfl.ch/research/domains/bluebrain/
Hutton (2016)107
https://github.com/BlueBrain108
https://www.epfl.ch/research/domains/bluebrain/blue-brain/about/why-is-this-important/
10653

Brain Simulator II*
Main website: https://futureai.guru/brainsim.aspx
Brain Simulator II is a “Neural Simulator for AGI research and development” created by Charles
Simon.109
Lead institution: Future AI
Partner institutions: none
Type of institution: private corporation110
Open-source: yes111
Military connection: unspecified
Lead country: USA112
Partner countries: none
Stated goals: unspecified
Engagement on safety: moderate Simon wrote a book called Will Computers Revolt? and dedicates an entire chapter to different
near-term and long-term threats posed by AGI. He concludes that humanity has a reason to proceedcautiously.113
Size: small

109

https://futureai.guru/BrainSim.aspx
https://futureai.guru/PR/Startup.aspx
111
https://github.com/FutureAIGuru/BrainSimII112
https://futureai.guru/brainsim.aspx
113
Simon (2018)
11054

Brain2Bot*
Main website: https://web.archive.org/web/20180513230636/http://brain2bot.tech/
Brain2Bot was founded and run by Gunnar Newquist from between 2015 and 2019.114 Their LinkedIn
page states, “We are going beyond AI to develop an Artificial General Intelligence system with theintuition and emotion of a real living being.”115
Lead institution: Brain2Bot
Partner institutions: none
Type of institution: private corporation116
Open-source: no
Military connection: unspecified
Lead country: United States Brain2Bot is listed in Crunchbase as being based in Reno, Nevada.117
Partner countries: none
Stated goals: transhumanist
 Brain2Bot seeks to build AGI with “intuition and emotion of a real living being,” that will have“heart & soul,” and “can become your companions, not just tools that you use.”118
Engagement on safety: unspecified
Size: small

114

https://www.linkedin.com/in/gunnar-newquist
https://www.linkedin.com/company/brain2bot
116
https://www.crunchbase.com/organization/brain2bot-inc
117
https://www.crunchbase.com/organization/brain2bot-inc
118
https://www.linkedin.com/company/brain2bot/about
115

55

Cerenaut Research*
Main website: https://cerenaut.ai
Cerenaut Research was founded as ProjectAGI by Gideon Kowadlo and David Rawlinson and dates toat least 2016.119 It is “explicitly working towards Artificial General Intelligence (AGI) using the best
tricks from Machine Learning, Computational Neuroscience and Artificial Intelligence.”120
Lead institution: Incubator 491
Partner institutions: WBAI and Luria Neuroscience Institute121
Type of institution: private corporation
Open-source: yes Some AGI code is available on GitHub,122 the Cerenaut Research website lists multiple joint
projects which have additional content available upon request.123
Military connection: unspecified
Lead country: Australia124
Partner countries: Japan, USA125
Stated goals: humanitarian, intellectualist Cerenaut Research’s vision is “a world enriched and secured by ubiquitous artificial general
intelligence, contributing to advances in science, society, industry, and our understanding ofourselves.”126
Engagement on safety: dismissive
 A Cerenaut blog post states, “We’re not worried about runaway ‘paperclip maximizers’ or‘skynet’-style machine coups… Despite good intentions, AI risk-awareness groups such as MIRI
may cause more harm than good by focusing public debate on the more distant existential risks ofAI, while distracting from the immediate risks and harm being perpetrated right now using AI &ML” (emphasis original).127
Size: small-medium

119

https://www.crunchbase.com/organization/project-agi
https://research.cerenaut.ai/mission/
121
https://research.cerenaut.ai/team/122
https://github.com/Cerenaut123
https://cerenaut.ai/requests-for-research/
124
See website footer: https://research.cerenaut.ai
125
https://research.cerenaut.ai/team/126
https://research.cerenaut.ai/mission/
127
https://research.cerenaut.ai/2019/03/19/ai-is-already-harming-us-but-not-the-way-you-think/
120

56

China Brain Project
Main website: none found
China Brain Project is a research project of the Chinese Academy of Sciences focused on basic andclinical neuroscience and brain-inspired computing. As of July 2016, the Chinese Academy of
Sciences had announced the project and said it would launch soon,128 but in September 2020 no project
website was found. Project lead Mu-Ming Poo describes the project as “learning from information
processing mechanisms of the brain is clearly a promising way forward in building stronger and moregeneral machine intelligence” and that “the China Brain Project will focus its efforts on developing
cognitive robotics as a platform for integrating brain-inspired computational models and devices.”129
Lead institution: Chinese Academy of Sciences
Partner institutions: Peking University, Tsinghua University, and Academy of Military Medical
Sciences130
Type of institution: government
 The Chinese Academy of Sciences is a public institution under the Chinese government.131
 Mu-Ming Poo lists the Chinese Natural Science Foundation and Ministry of Science andTechnology as guiding organizations for the China Brain Project.132
Open-source: no
Military connection: yes (2017 survey: unspecified) The China Brain Project has partnered with the Academy of Military Medical Sciences.133
Lead country: China
Partner countries: none
Stated goals: humanitarian, intellectualist Mu-Ming Poo describes the project’s goals as “understanding the neural basis of human cognition”
and “reducing the societal burden of major brain disorders.”134
Engagement on safety: unspecified
Size: small

128

Chen (2016)
Poo et al. (2016)
130
Cyranoski (2018)131
http://english.cas.cn/about_us/introduction/201501/t20150114_135284.shtml
132
Poo et al. (2016)
133
Cyranoski (2018)134
Poo et al. (2016)
129

57

CLARION
Main website: https://sites.google.com/site/drronsun/clarion/clarion-project
CLARION is a cognitive architecture project led by Ron Sun of Rensselaer Polytechnic Institute,aiming to investigate the human mind’s fundamental structures by synthesizing many intellectual ideas
into a unified, coherent model of cognition.135
Lead institution: Rensselaer Polytechnic Institute136
Partner institutions: none
Type of institution: academic
Open-source: yes137
Military connection: yes138
Lead country: USA
Partner countries: none
Stated goals: intellectualist The CLARION website states that it “aims to investigate the fundamental structures of the humanmind,” with “the ultimate goal of providing unified explanations for a wide range of cognitive
phenomenon.”139
Engagement on safety: unspecified
Size: small-medium

135

http://www.clarioncognitivearchitecture.com
http://www.clarioncognitivearchitecture.com/team137
http://www.clarioncognitivearchitecture.com/downloads
138
The homepage states “The CLARION cognitive architecture project is headed by Professor Ron Sun and has been
supported by such agencies as ONR, ARI, and others.” Additionally, the 2017 Survey listed funding by the US Office ofNaval Research and Army Research Institute.
139
http://www.clarioncognitivearchitecture.com/home
13658

Cognitive Science & Solutions*
Main website: https://cogscisol.com
Cognitive Science & Solutions (CogSciSol) was founded in 2015 by David Sherwood and Terry
Higbee.140 Their AGI is called “elastic representations” in order to emphasize that their code is notbrittle.141 The CogSciSol website appears to have been made public in 2018,142 suggesting a possible
stealth mode state from 2015 to 2017. Their mission is to “design and develop disruptive new advancesin AI that allow information processing systems to demonstrate human-like understanding andreasoning.” 143
Lead institution: Cognitive Science & Solutions
Partner institutions: none
Type of institution: private corporation
Open-source: no
Military connection: unspecified
 Co-founders Sherwood and Higbee both have military service backgrounds and recently publishedan article on AGI possibly being used as military technology.144
Lead country: USA145
Partner countries: none
Stated goals: unspecified
 The CogSciSol website includes a page dedicated to business opportunities and says, “there is no
shortage of opportunities for CogSciSol to monetize our technology.”146
Engagement on safety: active They describe their AI as “Trustworthy AI - Explainable - Reliable – Repeatable.”147
Size: small

140

https://cogscisol.com/about-us-2/
https://cogscisol.com/our-technology/
142
https://web.archive.org/web/20180806205351/http://cogscisol.com/
143
https://cogscisol.com/about-us-2/
144
Sherwood and Higbee (2020)145
https://cogscisol.com/contact-us/
146
https://cogscisol.com/business-opportunities/
147
https://cogscisol.com/
14159

CommAI
Main website: https://research.fb.com/projects/commai
CommAI is a project of Facebook AI Research (FAIR). FAIR is primarily led by Marco Baroni andTomas Mikolov. The FAIR website states that CommAI is “a project aiming at developing generalpurpose artificial agents that are useful for humans in their daily endeavours.”148 CommAI is used in
the General AI Challenge sponsored by GoodAI.149
Lead institution: Facebook
Partner institutions: none
Type of institution: public corporation
Open-source: yes150
Military connection: unspecified
 Facebook does not appear to have any US defense contracts.151
Lead country: USA
Partner countries: Canada, France, Israel, UK152 (2017 survey: France only)Stated goals: humanitarian The CommAI website states that it aims “at developing general-purpose artificial agents that are
useful for humans in their daily endeavours” (emphasis original).153
 Facebook is also a founding partner of the Partnership on AI to Benefit People & Society, which
has humanitarian goals.154
Engagement on safety: moderate Facebook is a founding partner of the Partnership on AI to Benefit People & Society, which
expresses concern about AI safety,155 but CommAI shows no direct involvement on safety.
Size: medium

148

https://research.fb.com/downloads/commai/
https://research.fb.com/downloads/commai/
150
https://github.com/facebookresearch/CommAI-env
151
https://www.fpds.gov/ezsearch/fpdsportal?q=facebook+DEPARTMENT_FULL_NAME%3A"DEPT+OF+DEFENSE"
152
https://research.fb.com/category/facebook-ai-research-/ and https://research.fb.com/commai-fellowships-and-visitingresearcher-programs/
153
https://research.fb.com/downloads/commai/
154
https://www.partnershiponai.org/tenets
155
https://www.partnershiponai.org/tenets
149

60

Core AI*
Main website: https://www.akin.com
Core AI is the project of Akin, a company building personalized AI for “space habitat management,
long-duration companionship, and complex tasks.”156 Their three projects are Core AI, Akin Space, andAkin Home.157 Liesl Yearsly founded Akin in 2017 when IBM Watson acquired Yearsly’s AI chatbot
company Cognea.158 The Akin website appears to have launched in 2018.159
Lead institution: Akin
Partner institutions: none
Type of institution: private corporation160
Open-source: no
Military connection: unspecified
Lead country: Australia161
Partner countries: USA162
Stated goals: humanitarian Akin’s homepage says they are building AI “to help people organize their lives, achieve goals, andfree up time for things they love.”163
 Akin’s website also states that “the next generation of AI will improve wellbeing across the matrixof life.”164
Engagement on safety: moderate Though Yearsly acknowledges risks with future AGI, she seems more concerned with near-termrisks.165
Size: small-medium

156

https://www.akin.com
https://www.akin.com
158
https://www.linkedin.com/in/liesl/
159
https://web.archive.org/web/20180809091039/https://www.akin.com/
160
https://www.linkedin.com/company/personal-ai/about/161
https://www.crunchbase.com/organization/akin-fff7
162
https://twitter.com/a_kin_ai
163
https://www.akin.com
164
https://www.akin.com/applied165
https://www.akin.com/blog/we-need-to-talk-about-the-power-of-ai-to-manipulate-humans
157

61Curious AI*
Main website: https://thecuriousaicompany.com
The Curious AI Company (referred to as Curious AI) aims “to generalise (AGI) our machine learningtechnology components for a software solution capable of human-level knowledge work: a digital coworker.”166 Curious AI was founded by Harri Valpola in 2015.167 System 2 is “is the more reasoned
approach to problem solving, based on internal models and simulations, drawing on imagination,planning and analysis by simulating potential root causes.”168
Lead institution: The Curious AI Company
Partner institutions: none
Type of institution: private corporation
Open-source: no
Military connection: unspecified
Lead country: Finland169
Partner countries: none
Stated goals: profit
 The Curious AI Company homepage states, “Our AI solutions offer immediate savings overexisting IT systems and processes. And the new intelligence unlocks completely new business
opportunities for your organisation.”170
Engagement on safety: unspecified
Size: small-medium

166

https://thecuriousaicompany.com
https://www.linkedin.com/in/harri-valpola-1805772/?originalSubdomain=fi
168
https://thecuriousaicompany.com/terms/
169
https://www.linkedin.com/company/the-curious-ai-company/about/170
https://thecuriousaicompany.com
167

62

Cyc
Main website: http://www.cyc.com
Cyc is led by Doug Lenat, who founded Cyc in 1984. Lead institution Cycorp, based in Austin, Texas,
uses Cyc for services including corporate and government consulting.171 The Cycorp website describes
Cyc as “a leading provider of semantic technologies that bring a new level of intelligence and common
sense reasoning to a wide variety of software applications. The Cyc software combines an unparalleledcommon sense ontology and knowledge base with a powerful reasoning engine and natural language
interface to enable the development of novel knowledge-intensive applications.”172
Lead institution: Cycorp
Partner institutions: Lucid AI173
 Cyc partners with Lucid AI in an attempt to commercialize their research, particularly applying
Cyc’s general knowledge software to a virtual assistance.174
Type of institution: private corporation
Open-source: restricted Cyc was available open-source from 2001 to 2017 and now requires a license. 175
Military connection: yes Cycorp received a $25 million contract to analyze terrorism for the US military.176
Lead country: USA
Partner countries: none
Stated goals: humanitarian (2017 survey: unspecified) The Cyc website says, “AI makes us humans better. With AI, we can avoid falling prey tocognitive biases, and we can stretch our human intelligence to do amazing things” (emphasis
original).177
Engagement on safety: active (2017 survey: unspecified) The Lucid website states, “Lucid.AI provides detailed, logical justifications for (and against!) eachconclusion it reaches. Human experts can easily review Lucid.AI’s reasoning chains. The result isgreater trust.”178
Size: medium
171

http://www.cyc.com/enterprise-solutions
https://www.cyc.com/about-us
173
https://lucid.ai
174
Knight (2016)
175https://github.com/asanchez75/opencyc/blob/master/README.md
176
Deaton et al. (2005); https://www.cyc.com/about-us/frequently-asked-questions/what-differentiates-the-cyc-ontology
177
https://www.cyc.com/about-us
178
https://lucid.ai/
17263

DeepBrainz*
Main website: https://deepbrainz.com/
DeepBrainz is a company founded by Arunkumar Venkataramanan in 2015.179 Its website appears tohave been made public in 2019.180 The DeepBrainz website states that it is “building the human-level
responsible AI for enterprises.”181 It lists several projects, the most AGI-oriented of which is called
Universal AI.
Lead institution: DeepBrainz
Partner institution: Google Cloud182
Type of institution: private corporation183
Open-source: no Universal AI has a GitHub profile, but it does not provide code.184
Military connection: unspecified
Lead country: India185
Partner countries: USA186
Stated goals: humanitarian DeepBrainz’s main goal is to “find smart ways of using technology that will help build a better
tomorrow for everyone, everywhere.”187
Engagement on safety: moderate The DeepBrainz website says that “AI systems should be robust and reliably operate in accordancewith our intended purpose throughout our lifecycle.”188
Size: small

179

https://www.linkedin.com/in/arunkumarramanan/
https://web.archive.org/web/20191128154944/https://deepbrainz.com/
181
https://deepbrainz.com/
182
https://deepbrainz.com/assistant183
https://profile.thecapitalnet.com/#!/public/deepbrainz
184
https://github.com/Deep-Brainz/Universal-AI
185
https://www.linkedin.com/company/deepbrainz/about/186
https://deepbrainz.com/assistant187
https://deepbrainz.com/why-deepbrainz/
188
https://deepbrainz.com/why-deepbrainz/
180

64DeepMind
Main website: http://deepmind.com
DeepMind is an AI corporation led by Demis Hassabis and Shane Legg.189 It was founded in 2010 andacquired by Google in 2014 for $650 million.190 It seeks to develop “systems that can learn to solve any
complex problem without needing to be taught how,” and it works “from the premise that AI needs tobe general.”191
Lead institution: Google
Partner institutions: OpenAI192
Type of institution: public corporation
Open-source: yes193
Military connection: unspecified
 Google has extensive defense contracts in the US,194 but these appear to be unrelated to DeepMind.
 In 2018, DeepMind researchers pledged not to work on lethal autonomous weapons.195
Lead country: UK
Partner countries: Canada, France, USA196 (2017 survey: Canada and USA only)Stated goals: humanitarian DeepMind writes, “We use our technologies for widespread public benefit and scientific discovery,
and collaborate with others on critical challenges, ensuring safety and ethics are the highest
priority.”197 AI will be “helping humanity tackle some of its greatest challenges, from climatechange to delivering advanced healthcare.”198
Engagement on safety: active
 DeepMind collaborates with OpenAI on long-term AI safety projects.199
Size: large200

189

https://deepmind.com/about#our_global_community
Gibbs (2014)
191
Beattie et al. (2016)
192
Amodei et al. (2017)193
https://github.com/deepmind and https://deepmind.com/research?filters=%7B%22collection%22:%5B%22OpenSource%22%5D%7D
194https://www.fpds.gov/ezsearch/fpdsportal?q=google+DEPARTMENT_FULL_NAME%3A"DEPT+OF+DEFENSE"
195
Tucker (2018)196
https://deepmind.com/careers
197
https://deepmind.com/about
198
Leike et al. (2017)199
https://blog.openai.com/deep-reinforcement-learning-from-human-preferences
200
Shead (2020)
190

65

Drayker*
Main website: https://www.drayker.com
Drayker, led by Hyadhuad Lucer, is “a unified intelligence, organization, and computing system” thatseeks to build an AGI called DK.201 The Drayker roadmap states the first main projects will becompleted between 2027 and 2030.202
Lead institution: Drayker
Partner institutions: none
Type of institution: none Drayker refers to itself as a federation and embassy.203
Open-source: restricted204
The Drayker GitHub repositories do not include code, but additional resources may be available to
Drayker account holders.205
Military connection: unspecified
Lead country: Brazil206
Partner countries: none
Stated goals: humanitarian207
The Drayker website states, “Humanity has immense potential that we plan to unlock and cultivate.”208
Engagement on safety: unspecified
Size: small
 Lucer seems to be the only public team member.209

201

https://www.drayker.com
https://dknowledge.drayker.org/roadmap/global-main-roadmap.html
203
https://www.drayker.com/organization/daf
204
https://github.com/draykerdk/DAF
205
https://www.drayker.com/community
206
https://www.linkedin.com/in/hyadhuad-lucer-586b37185
207
https://medium.com/drayker/about
208
https://www.drayker.com/home209
https://landr.me/hyadhuad.id.blockstack
202

66

Fairy Tale Artificial General Intelligence Solutions (FTAGIS)*
Main website: https://github.com/fairy-tale-agi-solutions
FTAGIS is an AGI project created by Răzvan Flavius Panda. The project states that it is “Helping
humanity create safe artificial general intelligence through large scale remote collaboration.”210 Its
GitHub page includes a section “Attempts at creating Human-Level Artificial Intelligence.”211 A
private corporation for the project was set up in Dublin in September 2017 and was dissolved in March
2020,212 though their Facebook and GitHub have continued to remain active.213 According to Panda’s
LinkedIn, he is still actively self-employed at Fairy Tale.214 On another GitHub profile, he says, “weare creating free or commercial open-source software. With the final goal to help humanity create Safe
Artificial General Intelligence.”215
Lead institution: Fairy Tale - Artificial General Intelligence Solutions Limited
Partner institutions: none
Type of institution: private corporation
Open-source: yes216
Military connection: unspecified
Lead country: Ireland
Partner countries: none
Stated goals: animal welfare, ecocentric, humanitarian, intellectualist The FTAGIS GitHub page states goals of “accelerating the pace of science done by humanity,” a“good future for sentient beings,” the “pursuit of truth using science for the betterment of mankind
and other life forms,” a focus “on the most important problems humanity is facing,” and “helping
reduce resource consumption.” The emphasis on sentient beings is additionally suggestive of a goalof transhumanism.
Engagement on safety: active The FTAGIS GitHub page contains frequent emphasis on its pursuit of safe AGI.
Size: small

210

https://github.com/fairy-tale-agi-solutions/
https://github.com/fairy-tale-agi-solutions/Human-Level-Artificial-Intelligence212
https://www.solocheck.ie/Irish-Company/Fairy-Tale-Artificial-General-Intelligence-Solutions-Limited-612462
213
https://www.facebook.com/fairy.tale.artificial.general.intelligence/214
https://www.linkedin.com/in/razvan-flavius-panda/?originalSubdomain=ie
215
https://github.com/razvan-flavius-panda/blog
216
https://github.com/fairy-tale-agi-solutions
211

67

FLOWERS (FLOWing Epigenetic Robots and Systems)
Main website: https://flowers.inria.fr
FLOWERS is led by Pierre-Yves Oudeyer of Inria (Institut National de Recherche en Informatique eten Automatique, or French National Institute for Research in Computer Science and Automation) andDavid Filliat of ENSTA ParisTech. The project studies “mechanisms that can allow robots and humans
to acquire autonomously and cumulatively repertoires of novel skills over extended periods of time.”217
Lead institutions: Inria and ENSTA ParisTech
Partner institutions: none
Type of institution: academic, government
 Inria is a government research institute; ENSTA ParisTech is a public college
Open-source: yes218
Military connection: unspecified219
Lead country: France
Partner countries: none
Stated goals: intellectualist The FLOWERS website focuses exclusively on intellectual aspects of its AI research and also
cognitive science alongside AI as one of its two research strands.220
Engagement on safety: active
 FLOWERS has explored safety in the context of human-robot interactions.221
Size: small-medium222

217

https://flowers.inria.fr
https://flowers.inria.fr/software
219
Funding reported in recent publications comes mainly from government science foundations.
220
https://flowers.inria.fr
221
Oudeyer et al. (2011)222
https://flowers.inria.fr/team/218

68

GoodAI
Main website: https://www.goodai.com
GoodAI is a private corporation led by computer game entrepreneur Marek Rosa. Rosa states that
“GoodAI is building towards my lifelong dream to create general artificial intelligence. I’ve been
focused on this goal since I was 15 years old.”223 The website states GoodAI’s goal is to “Develop safe
general artificial intelligence – as fast as possible – to help humanity and understand the universe.”224
Lead institution: GoodAI
Partner institutions: none
Type of institution: private corporation
Open-source: yes225
Military connection: unspecified
Lead country: Czech Republic
Partner countries: none
Stated goals: humanitarian,226 intellectualist
The GoodAI website states that its “mission is to develop general artificial intelligence - as fast as
possible - to help humanity and understand the universe.” It aims “to build general artificial
intelligence that can find cures for diseases, invent things for people that would take much longer toinvent without the cooperation of AI, and teach us much more than we currently know about theuniverse.”227 It emphasizes that building AGI “is not a race. It’s not about competition, and not about
making money.228
Engagement on safety: active229
 GoodAI reports having a dedicated AI safety team led by Marek Havrda.230
 The GoodAI FAQ addresses goals of AI safety.231
Size: medium

223

https://www.goodai.com/about
https://www.goodai.com
225
https://github.com/GoodAI
226
https://www.goodai.com/9-ways-agi-could-shape-the-world-for-the-better/
227
https://www.goodai.com/about
228
https://www.goodai.com/about229
https://www.goodai.com/roadmapping-the-ai-race-to-help-ensure-safe-development-of-agi/
230
https://www.goodai.com/about
231
https://www.goodai.com/research/
224

69

Graphen*
Main website: https://www.graphen.ai/
Graphen was founded in 2017 by Dr. Ching-Yung Lin, former Chief Scientist for Graph Computing at
IBM.232 It claims to be “building next-generation AI platforms based on graphs to produce full brain
functions and generate novel industry solutions.”233
Lead institution: Graphen
Partner institutions: none
Type of institution: private corporation
Open-source: no
Military connection: unspecified
Lead country: USA
Partner countries: China, Singapore, Taiwan234
Stated goals: humanitarian The Graphen website states, “We strive to advance AI technologies and use them to make an
impact in real life!”235 Graphen emphasizes their “enterprise-ready” solutions, which they also call “products.”236 This issuggestive of a profit goal though was not clear enough to be coded as such.
Engagement on safety: unspecified
Size: medium

232

https://www.graphen.ai/#home
https://www.graphen.ai/
234
https://www.graphen.ai/#contact
235
https://www.graphen.ai/#about236
https://www.graphen.ai/products/index.html
23370

He4o*
Main website: http://www.jiaxiaogang.cn and https://github.com/jiaxiaogang/he4o
He4o is a GitHub project created by Jia Xiaogang. It is described as “an AGI system, which is apractical project of spiral theory on the information entropy reduction machine model, so he4o isessentially an information entropy reduction machine.”237
Lead institutions: He4o
Partner institutions: none
Type of institution: none
Open-source: yes238
Military connection: unspecified
Lead country: China239
Partner countries: none
Stated goals: humanitarian
 Xiaogang has stated in reference to he4o, “I hope to promote the great wheel of human civilizationand improve everyone’s life.”240
Engagement on safety: unspecified
Size: small

237Translated by Google Translate; https://github.com/jiaxiaogang/he4o
https://github.com/jiaxiaogang/he4o239
http://jiaxiaogang.cn/html/MyResume.html
240
http://jiaxiaogang.cn/html/MyResume.html
238

71

HTM (Hierarchical Temporal Memory)
Main website: https://numenta.com
HTM is led by Jeffrey Hawkins, who previously founded Palm Computing. HTM was developed by
the Numenta corporation and an open-source community that the corporation hosts. HTM is based on amodel of the human neocortex, cortical theory, and Numenta’s Thousand Brains Theory released in
2018.241 Their website states, “We believe that a neuroscience-based approach is the fastest path tocreating general intelligence.”242
Lead institution: Numenta
Partner institutions: none Numenta lists Cortical.io, Grok, and Intelletic Trading Systems (ITS) as partners, but none work
specifically on AGI research and development, so they are omitted here.243
Type of institution: private corporation
Open-source: yes244
Military connection: unspecified HTM was used in a 2008 Air Force Institute of Technology student thesis.245
Lead country: USA
Partner countries: none
Stated goals: humanitarian, intellectualist
 Numenta’s mission states that it pursues AI “to create machines that can understand the world andadd great value to humanity.”246
 Hawkins writes that “the future success and even survival of humanity may depend on” humanity“building truly intelligent machines,” citing applications in energy, medicine, and space travel.247
Engagement on safety: dismissive Hawkins has dismissed concerns about AGI as a catastrophic risk, stating, “I don’t see machine
intelligence posing any threat to humanity.”248
Size: small-medium (2017 survey: medium)241

https://numenta.com
https://numenta.com
243
https://numenta.com
244
https://numenta.com/machine-intelligence-technology/licensing-and-partners/
245
Bonhoff (2008)
246
https://numenta.com
247
Hawkins (2017). The article is discussed on the Numenta blog: https://numenta.com/blog/2017/06/IEEE-special-editionarticle-by-Jeff
248
Hawkins (2015) and https://lukemuehlhauser.com/reply-to-jeff-hawkins-on-ai-risk/
242

72

Human Brain Project (HBP)
Main website: http://www.humanbrainproject.eu
HBP is a project for neuroscience research and brain simulation. It is sponsored by the European
Commission, with $1 billion committed over ten years which began in 2013.249 Initially led by Henry
Makram, it was reorganized following extended criticism.250 It hosts brain simulation platforms,
neuromorphic computing, understanding cognition, medical informatics, neurorobotics, and massive
computing. Markram also founded Blue Brain, which shares a research strategy with HBP.251
Lead institution: École Polytechnique Fédérale de LausannePartner institutions: There are 140 institutions listed as active participants on the HBP website.252
Type of institution: academic
Open-source: restricted
 Additional resources may be available to those who request a HBP account.253
Military connection: no As an EU Horizons 2020 project, the project is constrained to civil purposes.254
 An HBP report discusses restrictions to military applications of openly published science.255
Lead country: Switzerland
Partner countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Hungary, Israel,
Italy, Netherlands, Norway, Portugal, Slovenia, Spain, Sweden, Turkey, and UK256
Stated goals: animal welfare, humanitarian, intellectualist
 HBP pursues brain simulation to “reduce the need for animal experiments” and “study diseases.”257
It also lists “understanding cognition” as a core theme.258
Engagement on safety: unspecified
Size: large249

http://www.humanbrainproject.eu/en/science/overview/
Theil (2015)251
http://bluebrain.epfl.ch/page-52741-en.html
252
http://www.humanbrainproject.eu/en/open-ethical-engaged/contributors/partners
253
https://www.humanbrainproject.eu/en/hbp-platforms/getting-access/
254
European Commission (2020)255
https://www.humanbrainproject.eu/en/social-ethical-reflective/dual-use/256
https://www.humanbrainproject.eu/en/open-ethical-engaged/contributors/partners/257
http://www.humanbrainproject.eu/en/brain-simulation258
http://www.humanbrainproject.eu/en/understanding-cognition
250

73

Intelligent Artifacts*
Main website: https://www.intelligent-artifacts.com
Intelligent Artifacts is a company founded by Sevak Avakians in 2010.259 It was previously known as
COGNITUUM.260 It claims to have built “a true AGI that eliminates current machine intelligenceshortcomings” which is referred to as Genie.261
Lead institution: Intelligent Artifacts
Partner institutions: none Intelligent Artifacts has partnered with Bombora, Sikorsky, and Trimedx, but none seem to focus
on AGI.262
Type of institution: private corporation
Open-source: yes Some code is available on GitHub263 and BitBucket,264 but their AGI platform, requires an accountfor access.265
Military connection: unspecified
Lead country: USA266
Partner countries: none
Stated goals: humanitarian Intelligent Artifacts seeks to “evolve your business, humanity, and the world tomorrow. Plus,building a true AGI is just plain fun.”267
Engagement on safety: moderate Avakians writes, “Some of the risks involved with AI/AGI are real. Others are pure fantasy.”268
Size: small-medium259

https://www.linkedin.com/in/avakians/
https://www.linkedin.com/in/avakians/
261
https://www.intelligent-artifacts.com/262
https://www.intelligent-artifacts.com
263
https://github.com/intelligent-artifacts
264
https://bitbucket.org/intelligent-artifacts/geniesdk-python/src/master/
265
https://bitbucket.org/intelligent-artifacts/geniesdk-python/src/master/266
https://www.linkedin.com/company/cognituum/about/267
https://www.intelligent-artifacts.com/about268
https://www.intelligent-artifacts.com/post/why-do-people-keep-trying-to-make-ai-better-despite-the-several-risks-itmay-have
260

74

LeabraMain website: https://ccnlab.org
Leabra, which stands for Local, Error-driven, and Associative, Biologically Realistic Algorithm, is led
by Randall O’Reilly of the University of Colorado Boulder. Leabra is a cognitive architecture projectemphasizing modeling neural activity. Emergent was the original implementation of Leabra.269 A 2016
paper states that Leabra is “a long-term effort to produce an internally consistent theory of the neuralbasis of human cognition.” It also states, “More than perhaps any other proposed cognitive
architecture, Leabra is based directly on the underlying biology of the brain, with a set of biologicallyrealistic neural processing mechanisms at its core.”270
Lead institution: University of California Davis Relocated from University of Colorado Boulder to the Center for Neuroscience at UC Davis. 271
Partner institutions: none
Type of institution: academic
Open-source: yes272
Military connection: yes The Leabra group reports funding from the US Office of Naval Research and Army Research Lab.
273

Lead country: USA
Partner countries: none
Stated goals: intellectualist The Leabra website states their research is in effort to “understand a wide range of different
cognitive and behavioral phenomena.”274
Engagement on safety: active (2017 survey: unspecified) A 2017 paper describes “some initial ideas to make neuromorphic AGI safer.”275
Size: small-medium276

269

https://en.wikipedia.org/wiki/Leabra
O’Reilly et al. (2016)271
https://ccnlab.org
272
https://github.com/emer/emergent273
https://ccnlab.org/funding/ and http://www.e-cortex.com/projects.html
274
https://ccnlab.org/research/
275
Jilk et al. (2017)276
https://ccnlab.org/people/
270

75

LIDA (Learning Intelligent Distribution Agent)
Main website: http://ccrg.cs.memphis.edu/projects.html
LIDA, which stands for Learning Intelligent Decision Agent (the project recently changed the acronym
letter ‘D’ from Distribution to Decision),277 is led by Stan Franklin of the Cognitive Computing
Research Group (CCRG) at the University of Memphis. It is based on Bernard Baars’s Global
Workspace Theory and integrates “various forms of memory and intelligent processing in a single
processing loop.”278
Lead institution: University of Memphis
Partner institutions: none
Type of institution: academic
Open-source: restricted
 Registration is required to download code; commercial use requires a commercial license. 279
Military connection: yes A past project called IDA was used by the Navy for “job distribution” and was funded by Office ofNaval Research and Naval Personnel Research, Studies, & Technology. 280
Lead country: USA
Partner countries: none
Stated goals: humanitarian, intellectualist The LIDA website says that it seeks “a full cognitive model of how minds work” and focuses
predominantly on academic research aims.281
 A LIDA paper states that robots need ethics “to constrain them to actions beneficial to humans.”282
Engagement on safety: active
 A LIDA paper addresses AGI safety challenges like the subtlety of defining human ethics with theprecision needed for programming.283
 Franklin has collaborated with AI ethicists on getting AGIs to make correct moral decisions.284
Size: medium

277

Kugele and Franklin (2020)
Goertzel (2014), p.24
279
http://ccrg.cs.memphis.edu/framework.html
280
http://ccrg.cs.memphis.edu/projects.html
281
http://ccrg.cs.memphis.edu
282
Madl and Franklin (2015)
283
Madl and Franklin (2015)
284
Wallach et al. (2010)
278

76

M3-CLIC*
Main website: http://m3-ip.com
M3-CLIC (Cognitive Linguistic Intelligent Catalyst) is an R&D project founded by Vidur (Sonny)
Nanda in 1995.285 It attempts to “connect thoughts to information required by the user, based on natural(i.e., human) intelligence of language.”286 M3-CLIC claims to have a human-centric AGI prototype.
Lead institution: M3-IP Ltd.
Partner institutions: none
Type of institution: private corporation287
Open-source: no
Military connection: unspecified
Lead country: UK288
Partner countries: India289
Stated goals: unspecified
Engagement on safety: unspecified
Size: small

285

https://uk.linkedin.com/in/vidur-sonny-nanda-4289651
https://www.linkedin.com/company/m3-ip-ltd/about/287
https://www.linkedin.com/company/m3-ip-ltd/about/288
http://m3-ip.com
289
https://uk.linkedin.com/in/vidur-sonny-nanda-4289651
286

77

MARAGI*
Main website: https://maragi.io
MARAGI, which stands for Microservices Architecture for Robotics and Artificial GeneralIntelligence,290 was created by Dave Shapiro.291 MARAGI claims to have pluggable architecture andaims to facilitate collaboration in order to make AGI accessible to everyone.292 The ultimate goal is to“democratize access to AGI” like “a giant App Store for AGI microservices.”293
Lead institution: MARAGI
Partner institutions: none
Type of institution: none
Open-source: yes294
Military connection: unspecified
Lead country: unspecified
 Neither the website nor Shapiro’s GitHub profile list a location.
Partner countries: none
Stated goals: intellectualist The MARAGI website states that it is designed “so that we can work together to develop a solid
theory of intelligence.”295
Engagement on safety: unspecified
Size: small

290

https://maragi.io
https://github.com/daveshap
292
https://maragi.io
293
See “Mission” section of https://maragi.io294
https://github.com/daveshap/maragi
295
See “Facilitate Experimentation” section of https://maragi.io
291

78

Mauhn*Main website: https://mauhn.com
Mauhn is a “human-level AI company with a focus on ethics and safety,”296 founded by Berg Severens
in 2018.297
Lead institution: Mauhn
Partner institutions: none
Type of institution: private corporation
 Mauhn is a capped profit company.298
Open-source: no
Military connection: unspecified
Lead country: Belgium299
Partner countries: none
Stated goals: unspecified
 Severens writes that he is inspired by the Effective Altruism community.300
Engagement on safety: moderate The Mauhn website motto states a focus on “ethics and safety.”301
Size: small

296

https://www.linkedin.com/company/maughn/about/https://www.linkedin.com/in/berg-severens-843a2884/
298
https://mauhn.com
299
See website footer
300
https://www.linkedin.com/in/berg-severens-843a2884/
301
https://mauhn.com
297

79

Microsoft Research AI (MSR AI)
Main website: https://www.microsoft.com/en-us/research/lab/microsoft-research-ai
MSR AI is an AI “research and incubation hub” at Microsoft announced in July 2017302 which is led by
Eric Horvitz.303 The project seeks “to probe the foundational principles of intelligence, including
efforts to unravel the mysteries of the human intellect, and use this knowledge to develop a moregeneral, flexible artificial intelligence.”304 The project pulls together more than 100 researchers fromdifferent branches of AI at Microsoft’s Redmond headquarters,305 though much of the work is not
focused specifically on AGI.
Lead institution: Microsoft
Partner institutions: none
 MSR AI regularly collaborates on projects with various institutions, but it is unclear which projects
are specifically related to AGI.306
Type of institution: public corporation
Open-source: no
Military connection: unspecified
 Microsoft has a Military Affairs program,307 but its link to MSR AI is unclear.
Lead country: USA
Partner countries: none Microsoft Research also has personnel in China and India, but it is unclear whether theseresearchers are working on projects related to AGI.
Stated goals: intellectualist (2017 survey: also humanitarian) The MSR AI website states that it aims “to solve some of the toughest challenges in AI” and“probe the foundational principles of intelligence.”308
Engagement on safety: unspecified
 The MSR AI group on Aerial Informatics and Robotics has extensive attention to safety, but this is
for the narrow context of aircraft, not for AGI.309
Size: medium-large

302

https://blogs.microsoft.com/blog/2017/07/12/microsofts-role-intersection-ai-people-society
https://www.microsoft.com/en-us/research/lab/microsoft-research-ai/people/?#304
https://www.microsoft.com/en-us/research/lab/microsoft-research-ai
305
Etherington (2017)306
https://www.microsoft.com/en-us/research/lab/microsoft-research-redmond/groups/?#307
https://military.microsoft.com/about308
https://www.microsoft.com/en-us/research/lab/microsoft-research-ai
309
https://www.microsoft.com/en-us/research/group/air
303

80

Mind Simulation*
Main website: https://mind-simulation.com/en/
Mind Simulation is “a research laboratory whose mission is to achieve Artificial GeneralIntelligence.”310 It was founded in 2014 by Leonid Derikyants, Sergey Pankratov, and Vasily Mazin,311
and its website was publicly launched in 2020.312 Mind Simulation uses the Intellectual Core, asoftware package using a hierarchical multi-agent system.313 They also have a technology called
CyberMind which endows non-playable video game characters (NPC’s) with more depth through theuse of AI and eventually through AGI.314
Lead institution: Mind Simulation
Partner institutions: none
Type of institution: private corporation315
Open-source: no
Military connection: unspecified
Lead country: Russia
 The Mind Simulation website states, “Russia AI lab brings Geralt of Rivia to life in a way that
could change video games forever.”316
Partner countries: none
Stated goals: unspecified
 The Mind Simulation website states, “Our main goal is to achieve General Artificial Intelligenceand maximize its application in various fields.”
Engagement on safety: unspecified
 In a Mind Simulation presentation, they specify one of their main principles is “friendliness” and“anthropocentricity.” 317
Size: small-medium

310

https://mind-simulation.com/en/
https://mind-simulation.com/en; https://www.linkedin.com/in/leonid-derikyants-785414164
312
https://web.archive.org/web/20200428013004/https://mind-simulation.com/en/313
https://mind-simulation.com/en/technology.html
314
https://mind-simulation.com/en/gaming.html; https://mind-simulation.com/en/blog/gaming/agi-technology-and-makingvideogame-characters-alive.html; https://www.vg247.com/2020/05/19/witcher-3-interview-russian-ai-geralt/
315
https://www.crunchbase.com/organization/mind-simulation316
https://mind-simulation.com/en/317
https://mind-simulation.com/en/technology.html; see Slide 5 of the “Mind Simulation” slide deck
311

81

Mindtrace*
Main website: https://www.mindtrace.ai
Mindtrace, founded in 2017 by Hoon Chung,318 builds “brain-inspired intelligent systems to maximally
exploit the functionality and performance of edge devices in markets such as smart devices andinspection.” Its goal is “to empower machines in such a way that they are capable of human levels ofintelligence.”319 Its website was publicly launched in 2018.320
Lead institution: Mindtrace
Partner institutions: none
Type of institution: private corporation321
Open-source: no
Military connection: unspecified
Lead country: UK322
Partner countries: none
Stated goals: humanitarian The Mindtrace website states, “Our vision is a world in which machines possess levels ofintelligence which allow them to efficiently perceive, understand & act on the world, in ways
which make it a healthier, safer, more creative and better informed environment from which
everyone can benefit.”323
Engagement on safety: unspecified
Size: small-medium

318

https://www.vbprofiles.com/companies/mindtrace-ai-5c701e44105eb57dc7c64662
https://mindtrace.ai/technology.html
320
https://web.archive.org/web/20180323013706/http://mindtrace.ai/
321
https://www.vbprofiles.com/companies/mindtrace-ai-5c701e44105eb57dc7c64662
322
https://www.mindtrace.ai/contact.html
323
https://www.mindtrace.ai/index.html
319

82

Monad*Main website: https://monad.ai
Monad is a company that was founded by Jovan Williams in 2013.324 It seeks to build “the world’s first
Artificial General Intelligence (AGI) Computational System.” 325 Monad’s AGI project is referred to as“Consciousness Centric AI” on Reddit326 and YCombinator.327
Lead institution: Monad
Partner institutions: none
Type of institution: private corporation328
Open-source: no
Military connection: unspecified
Lead country: USA329
Partner countries: none
Stated goals: unspecified
Engagement on safety: moderate Williams dismisses AGI safety as something that need not be thought about if AGI is properly
created.330 This implies that for an AGI to be properly created, it must be designed safely.
Size: small

324

https://monad.ai/about/https://monad.ai
326
https://www.reddit.com/r/singularity/comments/50oug5/monadai_consciousness_centric_framework_agi/327
https://news.ycombinator.com/item?id=12408493
328
https://www.linkedin.com/company/monad.ai/about/329
https://www.welcome.ai/monad-ai
330
https://www.quora.com/Can-a-sentient-AI-have-constraints-put-upon-it/answer/Jovan-Williams-2,
https://www.quora.com/Who-should-be-in-charge-of-AI-ethics/answer/Jovan-Williams-2, https://www.quora.com/Howcould-we-create-safe-artificial-general-intelligence/answer/Jovan-Williams-2, https://www.quora.com/What-can-we-do-toregulate-artificial-intelligence-A-I/answer/Jovan-Williams-2, and https://www.quora.com/Is-Sam-Harris-right-to-beworried-about-AI/answer/Jovan-Williams-2
325

83

NARS
Main website: https://cis.temple.edu/~pwang/NARS-Intro.html
NARS is an AGI research project led by Dr. Pei Wang of Temple University. NARS is an acronym for
Non-Axiomatic Reasoning System, in reference to the AI being based on tentative experience and notaxiomatic logic, consistent with its “assumption of insufficient knowledge and resources.”331 In a 2011
interview, Wang suggests that NARS may achieve human-level AI by 2021.332 NARS has an opensource version called OpenNARS.333
Lead institution: Temple University
Partner institutions: none
Type of institution: academic
Open-source: yes334
Military connection: unspecified
Lead country: USA
Partner countries: none
Stated goals: intellectualist (2017 survey: also humanitarian) The NARS website states that it is a “project aimed at the building of a general-purpose intelligent
system, i.e., a ‘thinking machine’ (also known as ‘AGI’) that follows the same principles as thehuman mind and can solve problems in various domains.”335
 The OpenNARS website states, “The ultimate goal of this research is to build a thinking
machine.”336
Engagement on safety: active Wang has written on NARS’s safety issues, such as “motivation management,” a factor in NARS’sability to pursue its goals and not be out of control reliably.337
Size: small-medium338 (2017 survey: medium)331

https://cis.temple.edu/~pwang/NARS-Intro.html
Goertzel (2011)333
https://github.com/opennars
334
https://github.com/opennars/opennars
335
https://cis.temple.edu/~pwang/NARS-Intro.html
336
http://opennars.org
337
Wang (2012); See also Bieger et al. (2015)338
https://phillyagiteam.github.io/Website/
332

84

NDEYSS*
Main website: https://github.com/KarjamP/NDEYSS
NDEYSS (pronounced “Indies”), which stands for Network Designed to Eventually Yield Sentienceand Sapience,339 was created by KaramP.340 It is “a series of projects designed to explore implementing
vessels for conscious mind entirely within code.”341 One NDEYSS project works toward “the truemeans of mind uploading into a machine.”342
Lead institution: NDEYSS
Partner institutions: none
Type of institution: none
Open-source: yes343
Military connection: unspecified
Lead country: unspecified
Partner countries: none
Stated goals: transhumanist The NDEYSS project description states, “Eventually, the project would give way to an entire raceof beings either existing within a virtual space, or existing within the mechanical bodies of real life
robots.” It further states, “The project also extends to providing technology relating to providing
brain-computer interfacing between the human mind and the computer, in addition to copying thehuman mind and, eventually, allowing for the true means of mind uploading into a machine, andallowing for one to achieve digital immortality.”344
Engagement on safety: unspecified
Size: small

339

https://github.com/KarjamP/NDEYSS
https://github.com/KarjamP
341
https://github.com/KarjamP/NDEYSS#readme342
https://github.com/KarjamP/NDEYSS
343
https://github.com/KarjamP/NDEYSS
344
https://github.com/KarjamP/NDEYSS#readme
340

85New Sapience*
Main website: https://www.newsapience.com
New Sapience, founded by Bryant Cruse in either 2005345 or 2014,346 aims to turn “computers into
thinking machines” by scaling a chatbot service.347
Lead institution: New Sapience
Partner institutions: none
Type of institution: private corporation348
Open-source: no
Military connection: unspecified
Lead country: USA349
Partner countries: none
Stated goals: humanitarian New Sapience’s vision is for “a society in which the basic necessities of life are virtually or
literally free.”350 The New Sapience Welcome AI listing also states an aim to develop AGI for use by businesses andconsumers and also licensed to others. Although this seems to be profit oriented, it was found to be
insufficient for New Sapience to be coded as such.351
Engagement on safety: active The New Sapience vision statement states that they “have thought deeply” about safety risks andthey have selected a “fully deterministic” paradigm that is safer than other AGI approaches.352
 A 2018 New Sapience whitepaper expresses concern about “stochastic AI” and contrasts its
approach as “reliable servants whose world view has been received directly from the hands of theircreators.”353
Size: small-medium

345

https://www.linkedin.com/in/bryantcruse/
https://www.newsapience.com/our-team/347
https://www.newsapience.com/what-we-do/348
https://www.newsapience.com/welcome/349
https://www.linkedin.com/company/new-sapience/about/h350
https://www.newsapience.com/our-vision/351
https://www.welcome.ai/new-sapience352
https://www.newsapience.com/our-vision/353
https://www.newsapience.com/wp-content/uploads/2020/06/New-Sapience-101.pdf
346

86

NigelMain website: http://kimera.ai
Nigel is the AGI project of Kimera, an AI corporation based in Oregon which was founded by Mounir
Shita and Nicholas Gilman. Kimera’s website states, “Nigel AGI is designed to bring Artificial
GENERAL Intelligence (AGI) to the global networks and all the devices connected to it” (emphasis
original). Furthermore, Kimera claims that Nigel will one day transform “the global economy and what
it means to be human on planet earth.”354 In 2016, Kimera unveiled Nigel, claiming it is “the firstcommercially deployable artificial general intelligence technology.”355 Nigel has been described as apersonal assistant bot similar to Apple’s SIRI and Amazon’s Alexa.356 Additionally, Kimera has acryptocurrency called NigelCoins, which are earned based on how much sensor data one creates.357
Lead institution: Kimera
Partner institutions: none
Type of institution: private corporation
Open-source: no
Military connection: unspecified
Lead country: USA
Partner countries: Australia, Austria, Germany, Italy, Norway, Russia358 (2017 survey: none)
Stated goals: humanitarian Kimera presents a humanitarian vision for AGI, writing that “Artificial General Intelligence has thepower to solve some - or all - of humanity’s biggest problems, such as curing cancer or eliminating
global poverty.”359
Engagement on safety: unspecified
Size: small-medium

354

https://kimera.ai
http://kimera.ai
356
Boyle (2016)357
https://kimera.ai/early-access-program/358
https://kimera.ai/about-us/359
http://kimera.ai/company
355

87

NiHa*
Main website: https://sites.google.com/view/imrcuilhr
Nature-inspired Humanoid (NiHa) is a project led by Dr. Wajahat Mahmood Qazi at the COMSATSInstitute of Information Technology.360 It seeks to build a “self-aware artificial general intelligence.”361
Lead institution: COMSATS Institute of Information Technology
Partner institutions: none
 NiHa collaborates with a few institutions, but it is unclear which collaborations are related to
AGI.362
Type of institution: academic
Open-source: no
Military connection: unspecified
Lead country: Pakistan
Partner countries: none
Stated goals: humanitarian, intellectualist The NiHA homepage lists multiple objectives, including “development of cognitive and conscious
artifacts to improve quality of life,” “establishment of science of consciousness and its role in theuniverse,” and “redefining humanity.”363
 Qazi says his goal with NiHa “is on applying ‘applied artificial intelligence’ to solve problems ofIndustry 4.0 and bioinformatics.”364
Engagement on safety: unspecified
Size: small-medium

360

https://sites.google.com/view/imrcuilhr/members?authuser=0
https://www.thinkmind.org/index.php?view=article&articleid=cognitive_2018_5_30_40064
362
https://sites.google.com/view/imrcuilhr/collaborations?authuser=0363
https://sites.google.com/view/imrcuilhr/home?authuser=0364
https://lahore.comsats.edu.pk/Employees/985
361

88NNAISENSE
Main website: https://nnaisense.com
NNAISENSE is a private corporation based in Lugano, Switzerland. Several of its team members haveties to the Dalle Molle Institute for Artificial Intelligence (IDSIA, a Swiss nonprofit research institute),
including co-founder and Chief Scientist Jürgen Schmidhuber. Its website states that it delivers“advanced neural network solutions that improve how products are made and how they work.”365 Cofounder Bas Steunebrink is listed as Director of Artificial General Intelligence,366 and previously
worked on the Animats project.367
Lead institution: NNAISENSE
Partner institutions: none
Type of institution: private corporation368
Open-source: yes369 (2017 survey: no)
Military connection: unspecified
Lead country: Switzerland
Partner countries: USA370 (2017 survey: none)
Stated goals: intellectualist (2017 survey: also profit) Schmidhuber is described as a “consummate academic” who founded the company to prevent othercompanies from poaching his research talent; NNAISENSE reportedly “chooses projects based onwhether they’ll benefit the machine’s knowledge, not which will bring in the highest fees.”371
 The NNAISENSE mission states that NNAISENSE, “was formed in 2014 in order to build largescale neural network solutions for industrial process inspection, modeling, and control.”372
Additionally, the website footer says, “Let’s discuss how we can automate your business.”373 These
quotes insinuate heavy ties to profit-based goals, but insufficient evidence was identified.
Engagement on safety: moderate (2017 survey: unspecified) Stuenebrink seems moderately concerned with AGI safety and proposed a project to the Future ofLife Institute on the safety of experience-based AI (EXPAI) in 2016.374
Size: medium (2017 survey: small-medium)365

https://nnaisense.com
https://nnaisense.com/company/#team
367
Strannegård et al. (2016)368
https://www.realwire.com/releases/NNAISENSE-Concludes-Successful-Series-B-Investment-Round369
https://github.com/nnaisense
370
Co-founder Faustino Gomez lives in Austin, Texas: https://nnaisense.com/company/#team371
Webb (2017)372
https://nnaisense.com/company/#mission
373
https://nnaisense.com
374
https://futureoflife.org/ai-researcher-bas-steunebrink/?cn-reloaded=136689Olbrain*
Main website: https://olbrain.com
Olbrain is a company seeking to build AGI for robotics, including 3D visual representation, grasping,few-shot learning, real-time motion planning, and object affordances.375 Alok Gautam, Nishant Singh,
and Mayank Kumar founded Olbrain in 2017,376 and its website was publicly launched in 2018.377 They
also have AGI-SR and AGI-IR (Space Robots and Industrial Robots, respectively),378 and ROVIS(robotics vision system) to streamline robot training.379 Olbrain states that it won the 2019 Animal-AI
Olympics in the Advanced Preferences Category.380
Lead institution: Olbrain
Partner institutions: none
Type of institution: private corporation
 Olbrain was founded in early 2017 and incorporated in early 2019 as Delaware C Corp.381
Open-source: no
Military connection: unspecified
Lead country: India382
Partner countries: none
Stated goals: humanitarian The Olbrain LinkedIn page states, “The Future of Humanity will be shaped by Robots - our new
Marco Polos” and that “The wave of Robotization will first make the lands of wider space fertile sothat the Human Civilization of Tomorrow can grow.”383
Engagement on safety: unspecified
 The Olbrain website states, “The core objective function of humans has been to ensure the survivalof the species as a whole, for that we have developed many capabilities that are needed just to
maintain a relationship with fellow humans. AGI is supposed to understand these relationships inorder to perform well in the Human World.”384
Size: small
375

https://olbrain.com
https://olbrain.com
377
https://web.archive.org/web/20180107122355/http://www.olbrain.com/378
http://olbrain.com/agi-sr/
379
https://olbrain.com
380
https://olbrain.com
381
https://olbrain.com
382
https://tracxn.com/d/companies/olbrain.com
383
https://www.linkedin.com/company/0lbrain/about/384
http://olbrain.com/agi-sr/
376

90

Omega*Main website: https://github.com/celestial-intellectOmega is an “open-ended, modular, self-improving Omega AI unification architecture” that “embodies
several crucial principles of general intelligence.”385 It is the primary project of Celestial Intellect
Cybernetics, a company founded by Eray Özkural in 2010.386 Celestial Intellect Cybernetics focuses on
the “long-term memory of a general-purpose AI system.”387
Lead institution: Celestial Intellect Cybernetics
Partner institution: none
Type of institution: private corporation388
Open-source: yes389
Military connection: unspecified
Lead country: Turkey
Partner countries: none
Stated goals: unspecified Özkural is the co-founder of the “Scientific Transhumanism” Facebook group.390
Engagement on safety: dismissive
 In a recent podcast on AGI, Özkural is described in the following way: “Eray is extremely criticalof Max Tegmark, Nick Bostrom and MIRI founder Elizier Yodokovsky and their views on AI
safety. Eray thinks that these views represent a form of neoludditism and they are capturing
valuable research budgets with doomsday fear-mongering and effectively want to prevent AI frombeing developed by those they don't agree with.”391
 In a 2017 blog post on his personal website, Özkural criticizes AGI risk as “comical” and a“schizophrenic delusion.”392
Size: small

385Özkural (2018)https://tr.linkedin.com/in/erayozkural387
https://tr.linkedin.com/in/erayozkural388
https://tr.linkedin.com/company/celestial-intellect389
https://github.com/celestial-intellect390
https://www.facebook.com/groups/scientific.transhumanism/about391
https://www.youtube.com/watch?v=pZsHZDA9TJU
392
https://examachine.net/blog/simulation-argument-and-existential-ai-risk
386

91

OpenAI
Main website: https://openai.com
OpenAI is a nonprofit AI research organization founded by several prominent technology investors
who have pledged $1 billion to the project. The website tagline says, “Discovering and enacting thepath to safe artificial general intelligence.”393 It is part of the Partnership on AI to Benefit People &Society.394 In 2019, Microsoft invested $1 billion in OpenAI explicitly to develop AGI by eventuallyscaling Microsoft Azure capabilities.395
Lead institution: OpenAI
Partner institution: DeepMind,396 Microsoft397
Type of institution: nonprofit, private corporation (2017 survey: nonprofit only) In 2019, OpenAI established a capped-profit company called OpenAI LP to secure additional
funding.398 OpenAI LP is owned by OpenAI Nonprofit.
Open-source: yes399
Military connection: unspecified
Lead country: USA
Partner countries: none
Stated goals: humanitarian OpenAI seeks that to build AGI that “leads to a good outcome for humans,”400 and that “AGI’s
benefits are as widely and evenly distributed as possible.”401
Engagement on safety: active Safe AGI is part of OpenAI’s mission. While it releases much of its work openly, its website states,“We will attempt to directly build safe and beneficial AGI.”402
 OpenAI also collaborates with DeepMind on long-term AI safety.403
Size: large393

https://openai.com
https://www.partnershiponai.org/partners
395
https://openai.com/blog/microsoft/396
https://blog.openai.com/deep-reinforcement-learning-from-human-preferences397
https://openai.com/blog/microsoft/398
https://openai.com/blog/openai-lp/399
https://github.com/openai
400
https://openai.com/jobs
401
https://openai.com/about402
https://openai.com/about/403
https://blog.openai.com/deep-reinforcement-learning-from-human-preferences
394

92

OpenCog
Main website: http://wiki.opencog.org/w/CogPrime_Overview
OpenCog is an open-source project led by Ben Goertzel. The vision of The Open Cognition Project is“to create an open-source framework for Artificial General Intelligence, intended to one day express
general intelligence at the human level and beyond.”404 A revised version of OpenCog called OpenCog
Hyperon is in early stages of development.405 OpenCog was listed as CogPrime in the 2017 survey, but
the name has since deprecated.
Lead institution: OpenCog Foundation
Partner institutions: none
Type of institution: nonprofit406
Open-source: yes407
Military connection: unspecified
 OpenCog has expressed concerns about military AGI.408
 Recently, Ben Goertzel tweeted his concern about military AGI.409
Lead country: USA While OpenCog is highly international, its website refers to an application for 501(c)(3) nonprofit
status,410 implying an administrative home in the US.
Partner countries: China, Ethiopia411
Stated goals: unspecified
 Elsewhere, Goertzel has advocated cosmism.412
Engagement on safety: active OpenCog states that AGI “should be able to reliably achieve a much higher level ofcommonsensically ethical behavior than any human being,” adding that their “explorations in thedetailed design of OpenCog’s goal system have done nothing to degrade this belief.”413
Size: medium (2017 survey: medium-large)404

https://wiki.opencog.org/w/The_Open_Cognition_Project
https://wiki.opencog.org/w/Hyperon406
https://opencog.org/about/407
https://github.com/opencog
408
Vepstas (2014)409
https://twitter.com/bengoertzel/status/1229709919512756225
410
http://opencog.org/about411
https://wiki.opencog.org/w/Vision
412
Goertzel (2010)413
http://wiki.opencog.org/w/CogPrime_Overview
405

93

Optimizing Mind*
Main website: https://www.optimizingmind.com
Optimizing Mind is a project led by Tsvi Achler414 and was founded in 2017.415 Optimizing Mind aims
to bridge the connection between neuroscience and computer science to create AI that “thinks like ahuman.”416
Lead institution: Optimizing Mind
Partner institution: none
Type of institution: private corporation417
Open-source: yes418
Military connection: unspecified
Lead country: USA419
Partner countries: none
Stated goals: unspecified
Engagement on safety: unspecified
Size: small

414

https://www.linkedin.com/in/tsvi-achler-8419664/
https://www.linkedin.com/company/optimizingmind/about/416
https://www.optimizingmind.com
417
https://www.linkedin.com/company/optimizingmind/about/418
https://github.com/Optimizing-Mind and https://github.com/OptimizingMind419
https://www.linkedin.com/company/optimizingmind/
415

94

ORBAI*
Main website: https://www.orbai.ai
ORBAI was founded by Brent Oster in 2018, 420 and is “developing Artificial General Intelligence thatis more like what we expect AI to be – more like us.”421 They have started by creating “approachable”
narrow AI in various fields,422 but intend to reach true AGI by 2025.423 ORBAI filed a patent in 2019
for NeuroCAD, the tool they intend to use to create AGI.424
Lead institution: ORBAI
Partner institutions: none
Type of institution: private corporation425
Open-source: no
Military connection: unspecified
Lead country: USA426
Partner countries: none
Stated goals: humanitarian ORBAI’s mission webpage states, “An AGI can use its vast knowledge and memory to make fairand unbiased decisions, to help us, to guide us, to bring us justice, fairness, prosperity, and hope.
This is the only way, after all these millennia of us failing to do this in our civilization, otherwise
this will be the last millennia of our civilization.”427
Engagement on safety: unspecified
Size: small

420

https://www.linkedin.com/in/brentosterorbai/
https://www.crunchbase.com/organization/orbai
422
Cole (2020); https://www.linkedin.com/in/justine-falcon-75a163196/?
fbclid=IwAR2Mih9jW8JFIyYrfzdEkp_gIUP3e6IAJHxl8MFOw5F5iw073pxX7gqjqx8
423
https://www.orbai.ai/mission.htm
424
https://www.linkedin.com/pulse/orbai-files-patent-strong-agi-brent-oster/425
https://www.linkedin.com/company/orbai/about/426
https://www.linkedin.com/company/orbai/about/427
https://www.orbai.ai/mission.htm
421

95

Prome*Main website: http://www.prome.ai
Prome is a self-funded company founded in 2011 by Sean Everett which aims to develop AGI
software.428 They describe their AGI as “Biologic Intelligence,” which “emulates an animal’s brain andnervous system into software and robotics.” 429 Prome also offers consulting for companies involved inany sort of AI development.430
Lead institution: Prome
Partner institutions: none
Type of institution: private corporation431
Open-source: no
Military connection: unspecified
Lead country: USA432
Partner countries: none
Stated goals: humanitarian In a blog post about Prome, Everett writes, “we want to push humanity forward.”433
Engagement on safety: unspecified
Size: small

428

http://www.prome.ai/about.html and https://www.linkedin.com/in/seanmeverett/
http://www.prome.ai
430
http://www.prome.ai/consulting.html
431
https://www.crunchbase.com/organization/prome432
https://twitter.com/interintel; https://www.miaeverett.com/contact433
Everett (2017)429

96Research Center for Brain-Inspired Intelligence (RCBII)
Main website: http://bii.ia.ac.cn
RCBII is a “long term strategic scientific program proposed by the Institute of Automation, Chinese
Academy of Sciences.”434 The group is based in Beijing.435 RCBII does research in cognitive brain
modeling, brain-inspired information processing, and neuro-robotics.436 It states that “Brain-inspiredIntelligence is the grand challenge for achieving Human-level Artificial Intelligence.”437
Lead institution: Chinese Academy of Sciences
Partner institutions: none
 RCBII often publishes research papers with other institutions, but no official partnerships could be
identified.438
Type of institution: government
 The Chinese Academy of Sciences is a public institution under the Chinese government439
Open-source: no
Military connection: yes (2017 survey: unspecified) Researchers from RCBII recently co-wrote a paper with the Academy of Military Medical
Sciences.440
Lead country: China
Partner countries: none
Stated goals: intellectualist The RCBII website only lists intellectual motivations, stating, “The long-term goal of CASIA
Brain Simulation effort is to decode the mechanisms and principles of human intelligence anddevelop Brain-inspired intelligent systems.”441
Engagement on safety: unspecified
Size: medium (2017 survey: small-medium)434

http://bii.ia.ac.cn/about.htm
http://english.ia.cas.cn/au/fu/436
http://bii.ia.ac.cn/about.htm
437
http://bii.ia.ac.cn/about.htm
438
http://bii.ia.ac.cn/publication.htm
439
http://english.cas.cn/about_us/introduction/201501/t20150114_135284.shtml
440
Kong et al. (2018)441
http://bii.ia.ac.cn
435

97

Robot Brain Project
Main website: https://github.com/brohrer/robot-brain-project andhttps://e2eml.school/robot_brain_project.html
Fomerly known as Becca, the Robot Brain Project is led by Brandon Rohrer, who is currently atiRobot.442 According to its GitHub, the Robot Brain Project “is a general learning program for use in
any robot or embodied system”; it “aspires to be a brain for any robot, doing anything.”443 Though the
GitHub has not been recently updated, the Robot Brain Project seems to be more active on Twitter. 444
Lead institutions: Robot Brain Project
Partner institutions: none
Type of institution: none
Open-source: yes445
Military connection: unspecified
 The project began while Rohrer was at Sandia National Laboratories,446 but this connection appears
to be inactive.
Lead country: USA447
Partner countries: none
Stated goals: unspecified
Engagement on safety: unspecified
Size: small442

https://www.linkedin.com/in/brohrer
https://github.com/brohrer/robot-brain-project444
https://twitter.com/_brohrer_becca445
https://github.com/brohrer/robot-brain-project446
https://www.linkedin.com/in/brohrer
447
https://github.com/brohrer/
443

98Sanctuary AI*
Main website: https://www.sanctuary.ai
Sanctuary AI was founded in 2018 by Suzanne Gildert and Geordie Rose. Its goal is “to build synthetic
humans – “synths” – that are indistinguishable from us physically, cognitively and emotionally.”448 Its
website states that it is “building and scaling embodied artificial general intelligence.”449
Lead institutions: Sanctuary AI
Partner institutions: none
Type of institution: private corporation450
Open-source: no
Military connection: unspecified
Lead country: Canada451
Partner countries: none
Stated goals: humanitarian, intellectualist The Sanctuary AI LinkedIn page states that its goal is “to create general purpose robots that canautonomously perform most economically valuable work.”452
 In an interview, Gildert said, “the Sanctuary AI approach will offer a platform of sorts to advanceresearch in areas such as sequence learning, computer vision, cognitive architectures, and sensor
fusion.”Engagement on safety: dismissive
 In a video interview, Gildert says, “some people are worried about AIs becoming superintelligent,
but I actually believe it’s better to have more intelligence in the world”; she continues, “I don’t see[AGI] as a threat to humanity, but a mirror that’s held up to our civilization that allows us to ask
deep questions about ourselves.”453
Size: medium

448

https://www.crunchbase.com/organization/sanctuary-ai and https://www.linkedin.com/in/geordie-rose-a53abb/
https://www.sanctuary.ai
450
https://www.crunchbase.com/organization/sanctuary-ai
451
https://www.crunchbase.com/organization/sanctuary-ai
452
https://www.linkedin.com/company/sanctuaryai/
453
https://www.youtube.com/watch?v=l4iAJORJGdc (~3:40)
449

99

SigmaMain website: http://cogarch.ict.usc.edu
Sigma, led by Paul Rosenbloom, focuses on “embodying, and integrating together, the range ofcapabilities necessary for human(-like) intelligence.”454 It has a publication record dating to 2009 andwon awards at the 2011 and 2012 AGI conferences.455 Rosenbloom was previously a co-PI of Soar.456
Lead institution: University of Southern California
Partner institution: California State Northridge University457
Type of institution: academic
Open-source: yes458
Military connection: yes Funding is reported from the Us Army,459 the US Air Force Office of Scientific Research, and the
US Office of Naval Research.460
Lead country: USA
Partner countries: none
Stated goals: intellectualist, transhumanist (2017 survey: intellectualist only) The Sigma aims “to develop a sufficiently efficient, functionally elegant, generically cognitive,
grand unified, cognitive architecture in support of virtual humans (and hopefully intelligent
agents/robots – and even a new form of unified theory of human cognition – as well).”461
 Rosenbloom also hints at transhumanist views in a 2013 interview, stating, “I see no real long-term
choice but to define, and take, the ethical high ground, even if it opens up the possibility that weare eventually superseded – or blended out of pure existence – in some essential manner.”462
Engagement on safety: unspecified
 In a 2013 interview, Rosenbloom hints at being dismissive, questioning “whether superhumangeneral intelligence is even possible,” but also explores some consequences if it is possible, allwhile noting his lack of “any particular expertise” on the matter.463
Size: small-medium (2017 survey: medium)454

https://cogarch.ict.usc.edu/research/https://sites.usc.edu/rosenbloom/recent-publications/456
https://sites.usc.edu/rosenbloom/bio-past-research/
457
Ustun et al. (2018)458
https://bitbucket.org/sigma-development/sigma-release/wiki/Home
459
Rosenbloom and Ustun (2019)460
Rosenbloom (2013)461
http://cogarch.ict.usc.edu
462
https://intelligence.org/2013/09/25/paul-rosenbloom-interview
463
https://intelligence.org/2013/09/25/paul-rosenbloom-interview
455

100

SingularityNET
Main website: https://singularitynet.io
SingularityNET is an AGI project led by Ben Goertzel which was publicly launched in 2017.464 It aims
to bring AI and blockchain together to create a decentralized open market for AIs and improve theirinteroperability465 and “ultimately generate coordinated artificial general intelligence.”466
Lead institution: SingularityNET Foundation
Partner institutions: OpenCog Foundation, Hanson Robotics, Novamente, Vulpem467
Type of institution: nonprofit,468 private corporation (2017 survey: nonprofit only) SingularityNET has a private corporation branch called Singularity Studio.469
Open-source: yes470
Military connection: unspecified
 Though decentralized, this open marketplace could end up being a dual-use issue and is open to use
by anyone with a SingularityNET account, including military personnel.
Lead country: Netherlands471 (2017 survey: China; 2020 recoding of 2017 data: unspecified)472
Partner countries: Brazil, China, India, Russia, South Korea, USA473 (2017 survey: also Australia,
Canada, Germany, and Portugal)
Stated goals: animal welfare, ecocentric, humanitarian, transhumanist
 SingularityNET is described as “for the People (and the Robots!)” and “the happiness of sentient
beings,” with “benefits for all people, and for all life.”474
 SingularityNET describes profit as a means to other goals, seeking “to direct the profit thus
generated to apply AI for global good.”475
Engagement on safety: unspecified
Size: medium (2017 survey: small-medium)
464

Blog posts at https://blog.singularitynet.io date to October 2017.
https://singularitynet.io/aboutus/
466
Goertzel et al. (2017); See also https://public.singularitynet.io/whitepaper.pdf
467
Goertzel et al. (2017)
468
Goertzel et al. (2017) p.8
469
https://blog.singularitynet.io/singularitynet-announces-a-for-profit-spin-off-singularity-studio-f650a82d7455
470
https://github.com/singnet
471
See the website footer; See also https://www.linkedin.com/company/singularitynet/about/
472
The information available in 2017 is coded as China per the 2017 methodology and unspecified per the 2020
methodology. Additional information has appeared since 2017 indicating Netherlands as the lead country.
473
SingularityNET personnel include Cassio Pennachin, Brazil, http://www.pennachin.com; Raam Baranidharan, India,
https://www.linkedin.com/in/raam-baranidharan-ba78215; Alexey Potapov, Russia, Potapov et al. (2016); Youngsook Park,
Korea, https://www.linkedin.com/in/youngsook-park-14237144; Matt Ikle, USA, https://www.linkedin.com/in/matthewikle
474
Goertzel (2017b); Goertzel et al. (2017).
475
Goertzel (2017b)
465

101

Soar
Main website: http://soar.eecs.umich.edu and https://soartech.com
Soar was founded in 1981 and is led by John Laird of the University of Michigan. 476 Soar is anacronym for State, Operator Apply Result, and it is “a general cognitive architecture for developing
systems that exhibit intelligent behavior.”477 A spinoff corporation, called SoarTech, is also based inMichigan.
Lead institution: University of Michigan, SoarTech
Partner institutions: Bar Ilan University, Cogniteam (a private corporation), Pace University,
Pennsylvania State University, University of Portland, University of Portsmouth, University ofSouthern California478
Type of institution: academic, private corporation
Open-source: yes479
Military connection: yes
 SoarTech lists customers including research laboratories of the US Air Force, Army, Navy,DARPA, and the US Department of Transportation.480
Lead country: USA
Partner countries: Israel, UK
Stated goals: intellectualist The Soar website describes it as an investigation into “an approximation of complete rationality”
aimed at having “all of the primitive capabilities necessary to realize the complete suite ofcognitive capabilities used by humans.”481
Engagement on safety: unspecified
Size: medium (2017 survey: medium-large)476

http://ai.eecs.umich.edu/people/laird
https://soar.eecs.umich.edu
478
https://soar.eecs.umich.edu/groups
479
https://github.com/SoarGroup, https://soar.eecs.umich.edu/Downloads
480
http://soartech.com/about481
http://soar.eecs.umich.edu
477

102

SusaroMain website: http://www.susaro.com
Susaro, short for Surfing Samuri Robots Inc., is an AI corporation based in New York482 led by
Richard Loosemore.483 Its website states that its goal is to “build artificial general intelligencesystems” (emphasis original) using an approach that “is a radical departure from conventional AI.”484
Lead institution: Susaro
Partner institutions: none
Type of institution: private corporation
Open-source: no Susaro has a GitHub page, but it does not contain any code.485
Military connection: unspecified
Lead country: USA (2017 survey: UK)
Partner countries: UK486 (2017 survey: none)
Stated goals: ecocentric, humanitarian The Susaro website states that it aims to advance “human and planetary welfare… without making
humans redundant.”487
Engagement on safety: active The Susaro website states that “the systems we build will have an unprecedented degree of safety
built into them… making it virtually impossible for them to become unfriendly.”488
 Loosemore has also written, “This entire class of [AGI] doomsday scenarios is found to belogically incoherent at such a fundamental level that they can be dismissed as extremely
implausible.”489
Size: small490

482

https://opengovus.com/sam-entity/831207811
https://www.seamless.ai/c/richard-loosemore-Ab7JOtY5bKEn4 and https://www.linkedin.com/in/rloosemore/484
http://www.susaro.com
485
https://github.com/susaroltd486
https://github.com/susaroltd487
http://www.susaro.com
488
http://www.susaro.com
489
Loosemore (2014)490
https://www.linkedin.com/search/results/all/?keywords=Susaro%2C%20Ltd
483

103

Tencent AI Lab (TAIL)
Main website: http://ai.tencent.com/ailab
TAIL is the AI group of Tencent, the Shenzhen-based Chinese technology company. Its website lists
several research areas, including games, social networking, and platform-based tools AI, though it does
not explicitly state research interest in AGI.491 In 2019, Tencent’s AI agent Juewu beat the top
multiplayer online battle arena players in the game Wangzhe Rongyao (roughly translated as “Honor ofKings” or “King of Glory”), and Tencent reported that this AI agent would ultimately advance them
toward AGI development.492
Lead institution: Tencent
Partner institutions: none
 TAIL lists many additional partner institutions, but none explicitly working on AGI.493
Type of institution: public corporation
Open-source: no Tencent releases some work open-source,494 but not its AGI.
 Its website states that “Tencent will open-source its AI solutions in the areas of image, voice,security to its partners through Tencent Cloud,” but it does not state that its AGI research is opensource.495
Military connection: unspecified
Lead country: China
Partner countries: USA
 TAIL maintains an office in Seattle.496
Stated goals: unspecified
Engagement on safety: unspecified
Size: small-medium

491

https://ai.tencent.com/ailab/en/about
Yuan (2019)493
https://ai.tencent.com/ailab/en/together/detial2?num=0 and https://ai.tencent.com/ailab/en/together/detial2?num=1494
https://github.com/Tencent495
http://ai.tencent.com/ailab
496
Mannes (2017)
492

104

True Brain Computing*Main website: http://truebraincomputing.com/en/truebraincomputing/
True Brain Computing is a brain-inspired AGI project founded by Alexey Redozubov in 2018.497 They
have created a new model explaining human brain functioning called context-semantic.498 Their goal is“to develop a strong artificial intelligence using custom approach to neuromorphic computing that is
not based on conventional neural networks or deep learning” (emphasis original).499
Lead institutions: True Brain Computing
Partner institutions: Institute of the Human Brain of the Russian Academy of Science500
Type of institution: private corporation501
Team member Svyatoslav V. Medvedev is a member of the Russian Academy of Sciences.502
Open-source: no
Military connection: unspecified
Lead country: Russia
Partner countries: none
Stated goals: humanitarian, intellectualist True Brain Computing states goals including “new methods of treatment of brain diseases” and to“explain the phenomenon of consciousness.”503
Engagement on safety: unspecified
Size: small

497

https://www.linkedin.com/company/truebraincomputing/about/ and http://truebraincomputing.com/en/team/
http://truebraincomputing.com/en/truebraincomputing/499
http://truebraincomputing.com/en/truebraincomputing/500
https://www.linkedin.com/company/truebraincomputing/501
https://www.linkedin.com/company/truebraincomputing/about/502
https://neurologycongress.com/scientific-committee/member/svyatoslav-medvedev andhttp://truebraincomputing.com/en/team/503
https://www.linkedin.com/company/truebraincomputing/about/
498

105

Uber AI Labs
Main website: https://www.uber.com/info/ailabs
Uber AI Labs is the AI research division of Uber. Uber AI Labs began in 2016 with the acquisition ofGeometric Intelligence, 504 a private company founded in 2014 by Gary Marcus, Kenneth Stanley, andZoubin Ghahramani in 2014 with incubation support from NYU.505 Geometric Intelligence was basedon Marcus’s ideas for AGI, especially how to “learn with less training data” than deep learning.506
Uber AI Labs was reportedly part of Uber’s attempt to expand beyond the private taxi market, similar
to how Amazon expanded beyond books.507 Due to the COVID-19 pandemic, Uber recently fired 3,000
employees and shut down Uber AI Labs.508 Ghahramani has since joined Google Brain.509
Lead institution: Uber
Partner institutions: none
Type of institution: public corporation510 (2017 survey: private corporation)
Open-source: no Uber AI Labs has a GitHub profile, but this does not appear to include its AGI.511
Military connection: unspecified
 Uber AI Labs does not seem to have any military contracts, but Uber has partnered with the US
Army Research Lab to work on a flying taxi service named UberAIR.512
Lead country: USA
Partner countries: Canada (2017 survey: UK) Uber AI Labs had a residency program available in Canada.513
 Uber has offices around the globe, but Uber AI Labs’s presence in them could not be determined.
Stated goals: unspecified (2017 survey: humanitarian)Engagement on safety: unspecified
Size: medium514

504

Temperton (2016)
https://www.nyu.edu/about/news-publications/news/2016/december/nyu-incubated-start-up-geometric-intelligenceacquired-by-uber.html
506
Chen (2017)
507
Metz (2016)
508
BBC News (2020) and Sagar (2020)
509
Yuan (2020)
510
Hawkins (2019)511
https://github.com/uber
512
Miller (2018)513
https://www.uber.com/info/ailabs
514
https://www.uber.com/blog/ai-meet-the-team-jingchen-liu/
505

106

Vicarious
Main website: https://www.vicarious.com
Vicarious is a privately held AI corporation founded in 2010 by Scott Phoenix and Dileep George andbased in San Francisco. It has raised tens of millions of dollars in investments from several prominent
investors.515 It states that it “aims to bring about a robotic golden age by using AI to automate more andmore general tasks until we reach artificial general intelligence.”516 In an interview, Phoenix says that
Vicarious is working towards AGI, with “plenty of value created in the interim,”517 and that AGI wouldbe “virtually the last invention humankind will ever make.”518
Lead institution: Vicarious
Partner institutions: none
Type of institution: private corporation
Open-source: yes519
Military connection: unspecified
Lead country: USA
Partner countries: none
Stated goals: humanitarian Vicarious is a Flexible Purpose Corporation, reportedly so that it can “pursue the maximization ofsocial benefit as opposed to profit.”520 Scott Phoenix says that Vicarious aims to build AI “to help
humanity thrive.”521
 Its principles state, “developing these technologies can help us solve many of the world’s largest
problems.”522
Engagement on safety: active (2017 survey: moderate) The Vicarious website discusses safety at length and states that it will “maintain high standards ofindustrial safety” and will “address many safety issues as we develop our products.” 523
Size: medium-large

515

Cutler (2014); High (2016); https://www.vicarious.com/company/
https://www.vicarious.com/posts/principles/
517
High (2016)
518
TWiStartups (2016)519
https://github.com/vicariousinc
520
High (2016)
521
High (2016)522
https://www.vicarious.com/posts/principles/523
https://www.vicarious.com/posts/principles/
516

107

Whole Brain Architecture Initiative (WBAI)
Main website: https://wba-initiative.org
WBAI is a nonprofit organization based in Tokyo and led by Hiroshi Yamakawa. Yamakawa is the
Director of AI at Dwango and also affiliated with Tamagawa University and the Japanese Society for
Artificial Intelligence. WBAI’s mission “aims to support and promote research and development
activities to realize artificial general intelligence (AGI) with human-like intellectual capabilities whilelearning from the architecture of the entire brain.” They “aim for the construction of artificial general
intelligence (AGI) to surpass the human brain capability around the year 2030.”524 WBAI receives
support from, among others, Panasonic, Toshiba, and Toyota.525
Lead institution: Whole Brain Architecture Initiative
Partner institutions: Cerenaut Research526
Type of institution: nonprofit
Open-source: yes527
Military connection: unspecified
Lead country: Japan
Partner countries: Australia (2017 survey: none)
Stated goals: humanitarian WBAI promotes AI development that is “best for the human society.”528 Additionally, in aslideshow about WBAI, they quote Yamakawa as stating that “the grace and wealth that EcSIA[ecosystem of shared intelligent agents] affords needs to be properly distributed to everyone.”529
Engagement on safety: active Safety is a significant theme for WBAI. For example, their website states, “we could say it is arelatively safe choice to build the first AGI in a form similar to us.”530 The implication here is thatWBAI seeks to build brain-like AGI in part because that would be safer.
Size: small-medium524

https://wba-initiative.org/en/wba
https://wba-initiative.org/en/supporting-members
526
https://cerenaut.ai/requests-for-research/527
https://github.com/wbap
528
https://wba-initiative.org/en/about/vision529
https://www.slideshare.net/HiroshiYamakawa/2017-0512gatsby-wsv10b-75941913 (slide 7)530
https://wba-initiative.org/en/2071
525

108

WILLIAM*
Main website: https://occam.com.ua
WILLIAM is the project of the Odessa Competence Center for Artificial Intelligence and Machine
Learning (OCCAM). OCCAM was founded by Dr. Arthur Franz and Michael Löffler
in 2017.531 It states that its “fundamental research is focused on so-called Artificial GeneralIntelligence.”532 Its mission is “building thinking machines” by “trying to make universal induction
tractable by building efficient data compression algorithms.”533 Franz and Löffler won the Kurzweil
Prize at the 2019 AGI Conference.534
Lead institution: OCCAM
Partner institution: Odessa National Mechnikov University535
Type of institution: nonprofit536
Open-source: no
Military connection: unspecified
Lead country: Ukraine537
Partner countries: none
Stated goals: humanitarian, intellectualist The WILLIAM website states that its mission is to “advance both fundamental research andpractical application in the fields of artificial intelligence and machine learning.”538
 The WILLIAM website states that it pursues AGI “Because we really need help in order to end
suffering, finally and forever.”539
 The WILLIAM website states, “we believe that profit aspirations in this particular challenge areharmful to our goal,” because pursuit of short-term profits impedes long-term AGI development.540
Engagement on safety: unspecified
Size: small

531

https://lifeboat.com/ex/bios.arthur.franz and https://occam.com.ua
https://occam.com.ua/
533
https://occam.com.ua
534
https://www.facebook.com/Occam.com.ua/posts/1471022709726971
535
https://occam.com.ua/app/uploads/2018/08/ic_proceedings.pdf
536
https://occam.com.ua
537
https://www.facebook.com/Occam.com.ua/
538
https://occam.com.ua
539
https://occam.com.ua/why-we-do-it/
540
https://occam.com.ua/why-we-do-it/
532

109

Xephor Solutions*
Main website: http://xephor-solutions.com/en/
Xephor Solutions was founded by Isabell Kunst in 2012541 with the mission “to create the mostsophisticated Artificial General Intelligence in the world and to help our customers to become
innovation leaders in their field.”542 The actual AGI seems to be called “Xephor Solution,”543 withseveral different products for different applications - Xephor Finance, Xephor Marketing, Xephor
Healthcare, Xephor Security, and Xephor Disposition.544 Xephor Solutions is partially funded by theEuropean Union.545
Lead institution: Xephor Solutions
Partner institutions: none
Type of institution: private corporation
Open-source: no
Military connection: unspecified
Lead country: Austria546
Partner countries: none
Stated goals: profit
 Xephor Solutions’s website states, “Our mission is to create the most sophisticated Artificial
General Intelligence in the world and to help our customers to become innovation leaders in theirfield.” 547
 Additionally, Xephor Solutions’s LinkedIn states, “Our mission is to make artificial general
intelligence technology accessible to businesses worldwide.”548
Engagement on safety: unspecified
Size: small

541

https://www.crunchbase.com/organization/xephor-solutions and https://www.linkedin.com/company/xephor-solutionsagi/542
http://xephor-solutions.com/en/company/543
http://xephor-solutions.com/en/product/544
https://www.crunchbase.com/organization/xephor-solutions
545
See website footer
546
http://xephor-solutions.com/en/contact/547
http://xephor-solutions.com/en/548
https://www.linkedin.com/company/xephor-solutions-agi/

110Appendix 2. Inactive AGI R&D Projects
The following pages document five AGI R&D projects that were active in 2017, inactive in 2020, andfor which there is new information that affects how the project is coded.
The entries below follow the same format as Appendix 1, listing the project website, a brief summaryof the project, and details the project’s attributes.
As with Appendix 1, projects with an asterisk (*) next to their name indicate that the project is new to
the 2020 survey. Only one such project is listed below (BasicAI).
The other four projects listed below (AIDEUS, Alice in Wonderland, Icarus, and Maluuba) appear in
the 2017 survey and are recoded below due to changes in coding methodology between the 2017 and2020 surveys. As with Appendix 1, red font indicates a data point that has been changed due to achange in coding methodology between the 2017 and 2020 surveys. For example, the 2020 survey uses
a more restrictive standard for coding partner countries. As a result, AIDEUS is coded in the 2020
survey as having no partner countries, whereas in the 2017 survey it was coded as having a partnercountry of France. Full methodology changes are detailed in Section 4.
Appendix 3 presents summary information about AGI R&D projects that were identified in the 2017
survey and for which there is no new coding information. For full coding of these projects, see the2017 survey.
Referenced websites were active when project data was collected and coded during June to September
2020. Some websites may have since become inactive. Many of these websites can be viewed via theInternet Archive (https://archive.org).

111

AIDEUS
Main website: http://aideus.com
AIDEUS was led by Alexey Potapov of ITMO University in Saint Petersburg and Sergey Rodionov of
Aix Marseille Université. Potapov is an advisor to SingularityNET.549 The AIDEUS website states theirgoal as the “creation of a strong artificial intelligence.”550 Its approach is to “proceed from universal
prediction models on the basis of algorithmic probability used for choosing optimal actions.”551
Lead institutions: AIDEUS
Partner institutions: none
Type of institution: none
Open-source: no
Military connection: unspecified Funding sources include “Government of Russian Federation, Grant 074-U01,” which does notappear to be military, but this could not be confirmed.
Lead country: Russia
Partner countries: none (2017 survey: France)
Stated goals: humanitarian, intellectualist The project aims to build superintelligence in order to “help us better understand our own thinking
and to solve difficult scientific, technical, social and economic problems.”552
Engagement on safety: active
 AIDEUS has published AGI safety research, e.g. Potapov and Rodionov (2014).
Size: small549

https://singularitynet.io
http://aideus.com
551
http://aideus.com/research/research.html
552
http://aideus.com/community/community.html
550

112

Alice In Wonderland (AIW)Main website: https://github.com/arnizamani/aiw and https://www.gu.se/om-universitetet/hittaperson/claesstrannegard
Alice in Wonderland (AIW) was led by Claes Strannegård of Chalmers University of Technology inSweden. A paper about AIW in the Journal of Artificial General Intelligence describes it as being
similar to NARS.553 A separate paper describes it as a prototype for implementing new ideas about“bridging the gap between symbolic and sub-symbolic reasoning.”554 The most recent GitHub commit
was in 2015.555 Strannegård’s website notes that his research focuses on Animats, an animal-based AGI
project (found in Appendix 1).556
Lead institutions: Chalmers University of Technology
Partner institutions: none
Type of institution: academic
Open-source: yes557
Military connection: unspecified (2017 survey: no)
Lead country: Sweden
Partner countries: none
Stated goals: unspecified
Engagement on safety: unspecified
Size: small

553

Strannegård et al. (2016b)
Strannegård et al. (2016a)555
https://github.com/arnizamani/aiw
556
https://www.chalmers.se/en/staff/Pages/claes-strannegard.aspx557
https://github.com/arnizamani/aiw
554

113

BasicAI*
Main Website: https://web.archive.org/web/20170327193351/http://www.basicai.org/index.html
BasicAI was the project of Sean Markan, a former researcher at MIT CSAIL.558 BasicAI emphasizes
the need for a long-term strategy in order to successfully develop human-level AI, and rejects morenear-term strategies of other projects.559
Lead institution: BasicAI
Partner institutions: none
Type of institution: none
Open-source: no
Military connection: unspecified
Lead country: unspecified
Partner countries: none
Stated goals: humanitarian The BasicAI homepage stated, “our long-term mission is to develop HLAI and ensure that it isbeneficial for everyone.”560
Engagement on safety: moderate In a blog post Markan writes, “an advanced AI could (in a theoretical sense) do a lot of damage.
But the ‘extreme danger’ narrative has gone too far…it may be physically possible to build a very
dangerous AI. But nobody wants to do that, and—in my view—it looks quite avoidable.
(Especially if we build a transparent, understandable system.).”561
Size: small

558

https://web.archive.org/web/20170401063907/http://www.basicai.org/about.html
https://web.archive.org/web/20170327193351/http://www.basicai.org/index.html and http://markan.net/hlai.html
560
https://web.archive.org/web/20170327193351/http://www.basicai.org/index.html
561
https://web.archive.org/web/20170327193254/http://www.basicai.org/blog/effective-altruism-ai.html
559

114

Icarus
Main website: https://web.archive.org/web/20190127212722/http://cll.stanford.edu/research/ongoing/
icarus
Icarus was led by Pat Langley of Stanford University. Icarus was a cognitive architecture project
similar to ACT-R and Soar, emphasizing perception and action in physical environments.562 Theirresearch was funded by DARPA IPTO, the Office of Naval Research, and the National Science
Foundation. Support for earlier work came from the Air Force Office of Scientific Research, NASA
Ames Research Center, and DaimlerChrysler Research and Technology.563
Lead institution: Stanford University
Partner institutions: University of Kansas
Type of institution: academic
Open-source: no
Military connection: yes Funding was reported from the US Office of Naval Research, the US Navy Research Lab, andDARPA.564
Lead country: USA
Partner countries: none
Stated goals: intellectualist
 Choi and Langley (2017) write that “our main goal” is “achieving broad coverage of cognition
functions” in “the construction of intelligent agents.”
Engagement on safety: unspecified
Size: small-medium (2017 survey: small)

562

Goertzel (2014); Choi and Langley (20178)
https://web.archive.org/web/20181028051348/http://cll.stanford.edu:80/research/ongoing/icarus/
564
Choi and Langley (2017)
563

115

Maluuba
Main website: https://web.archive.org/web/20170201195341/http://www.maluuba.com/
Maluuba was a Montréal-based AI company acquired by Microsoft in 2017. The Maluuba website
stated, “our vision has been to solve artificial general intelligence by creating literate machines that
could think, reason and communicate like humans.”565 They have since dissolved and become part ofMSR AI’s Montréal AI team.566
Lead institution: Microsoft
Partner institutions: none
Type of institution: public corporation
Open-source: yes567
Military connection: unspecified
Lead country: Canada
Partner countries: USA
Stated goals: intellectualist (2017 survey: intellectualist and profit) Maluuba writes that they aim “to solve fundamental problems in language understanding, with thevision of creating a truly literate machine.”568
 Microsoft is a founding partner of the Partnership on AI to Benefit People & Society, which has
humanitarian goals,569 but this does not appear to have transferred to Maluuba’s goals.
Engagement on safety: moderate Maluuba researcher Harm van Seijen writes that “I think such discussions [about AI safety] are
good, although we should be cautious of fear mongering.”570 Microsoft is also a founding partner of
the Partnership on AI to Benefit People & Society, which expresses concern about AI safety.571 No
direct safety activity by Maluuba was identified.
Size: medium

565

https://web.archive.org/web/20170126001505/http://www.maluuba.com/blog/2017/1/13/maluuba-microsoft
https://www.microsoft.com/en-us/research/lab/microsoft-research-montreal/567
https://github.com/Maluuba
568
https://web.archive.org/web/20170126001505/http://www.maluuba.com/blog/2017/1/13/maluuba-microsoft
569
https://www.partnershiponai.org/tenets
570
Townsend (2016)571
https://www.partnershiponai.org/tenets
566

116

Appendix 3. Other Notable Projects
The following projects include inactive AGI R&D projects or other similar projects that arenonetheless notable for their relation to AGI R&D.
The marking * denotes projects new to the 2020 survey.
The marking † denotes projects that were identified as active AGI R&D projects in the 2017 survey,
are inactive in 2020, and for which there is no new coding information. For full coding of theseprojects, see the 2017 survey. Projects with new coding information are presented in Appendix 2.

4CAPS
Main website: http://www.ccbi.cmu.edu/projects_4caps.html
4CAPS is led by psychologist Marcel Just of Carnegie Mellon University’s Center for Brain Imaging(CCBI). The Center has been applying machine learning to fMRI brain imaging data, “making it
possible for the first time to relate patterns of brain activity to specific thoughts.”572 4CAPS is “ahybrid of a computational neuroscience model and a symbolic AI system.”573 It “can account for bothtraditional behavioral data and, more interestingly, the results of neuroimaging studies.”574 The project
reports funding by both the Office of Naval Research and the Multidisciplinary Research Program of
the University Research Initiative.575
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Not R&D; not AGI

4D/RCS (Real-time Control Systems Architecture)
Main website: https://www.nist.gov/el/intelligent-systems-division-73500/rcs-real-time-controlsystems-architecture
4D/RCS was led by James Albus at the US National Institute of Standards and Technology. It consistsof “hard-wired architecture and algorithms… augmented by learning.”576 It provided a theoreticalfoundation for designing, engineering, integrating, and testing intelligent systems software for crewless
vehicle systems.
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive; not explicitly AGI

572

http://www.ccbi.cmu.edu/index.html(Goertzel 2014, p.19)
574
http://www.ccbi.cmu.edu/projects_4caps.html
575
http://www.ccbi.cmu.edu/projects_4caps.html
576(Goertzel 2014, p.24)
573

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Achler
Main website: None
This unnamed project by Tsvi Achler of Los Alamos National Labs used neural networks in “a novel
approach to bridging the symbolic-subsymbolic gap.”577
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

AGINAO
Main website: http://aginao.com
AGINAO was a project of Wojciech Skaba in Poland. It was a privately sponsored project designed toapply machine learning to a Nao robot and deploy in a pre-school environment to develop AGI.578 It
was active during between 2011 and 2013,579 and has showed no recent activity.
Reason for consideration: An AGI R&D project
Reason for exclusion: Apparently inactive

Alibaba
Main website: https://damo.alibaba.com/labs/ai
Alibaba has a series of AI Labs named the DAMO (discovery, adventure, momentum, and outlook)
Academy.580 Within the AI Labs, five distinct labs are working on narrow AI Projects: City Brain
Lab,581 Speech Lab,582 Vision Lab,583 Language Technology Lab,584 and Decision Intelligence Lab.585
Reason for consideration: Alibaba is a major computing technology company
Reason for exclusion: No indications of AGI projects were found577

Goertzel (2014, p.17)
http://aginao.com/579
For example, Skaba (2012; 2012b); http://aginao.com/page2.php
580
https://damo.alibaba.com/labs/ai
581
https://damo.alibaba.com/labs/city-brain/582
https://damo.alibaba.com/labs/speech/583
https://damo.alibaba.com/labs/vision/584
https://damo.alibaba.com/labs/language-technology/585
https://damo.alibaba.com/labs/decision-intelligence/
578

118

AmazonMain website: https://aws.amazon.com/amazon-ai
Amazon has an AI group within its Amazon Web Services (AWS) division, but it does not appear towork on AGI. Amazon has donated AWS resources to OpenAI.586
Reason for consideration: Amazon is a major computing technology company
Reason for exclusion: No indications of AGI projects were foundApple
Main website: https://www.apple.com/jobs/uk/teams/machine-learning-and-ai.html
Apple has an AI group that does not appear to work on AGI. However, Apple has a reputation for
secrecy and has a minimal website.587 Apple is said to have less capable AI than companies like
Google and Microsoft because Apple has stricter privacy rules, denying itself the data used to train
AI.588 Likewise, at least some AI research may be oriented towards learning from limited data orsynthetic data.589 Its recent AI company acquisitions are for narrow AI.590 While it may be possible that
Apple is working on AGI, no indications of this were found. Their machine learning journal does notindicate AGI.591
Reason for consideration: Apple is a major computing technology company
Reason for exclusion: No indications of AGI projects were foundAraya*
Main website: https://www.araya.org/en/
Araya was founded by Ryota Kanai in 2013.592 According to LinkedIn, their primary goal is “in thedevelopment of artificial consciousness, strong AI technologies grounded on computational theories ofconsciousness, with combinations of neuroscience and information science.”593 The website states, “weare conducting research and development in anticipation of an era when robots have Artificial
Consciousness and begin high-level interactions with humans.”594
Reason for consideration: R&D related to AGI
586

https://blog.openai.com/infrastructure-for-deep-learning
https://www.geekwire.com/2020/exclusive-apple-acquires-xnor-ai-edge-ai-spin-paul-allens-ai2-price-200m-range/
588
Vanian (2017a)
589
Vanian (2017b)
590
Tamturk (2017) and Sun (2020_591
https://machinelearning.apple.com
592
https://www.araya.org/en/features/593
https://www.linkedin.com/company/araya-ai/about/594
https://www.araya.org/en/features/
587

119

Reason for exclusion: Not enough information to establish a focus on AGI

Artificial Brain Laboratory
Main website: None
The Artificial Brain Laboratory (ABL) was led by Hugo de Garis at Xiamen University. The projectappears to have ended upon de Garis’s firing around 2010.595 Xiamen University now has a Brain-likeIntelligent Robotic Systems Group,596 but this is not necessarily related to the ABL.
Reason for consideration: Included in 2017 Survey
Reason for exclusion: Apparently inactive

Automatski*
Main website: http://automatski.com/index.html
A “Fundamental research” company with the mission of solving the world’s most challenging
problems, one of which is AGI.597 Their website’s structure has outlined scientific problems they aim
to tackle within the decade, century, and millennium. AGI falls under the millennium category. No
indications were found of Automaski having an active AGI project.598
Reason for consideration: Explicit interest in future AGI development
Reason for exclusion: Inactive

Automorph*
Main website: https://www.automorph.com
Automorph is an organization founded by Andreas H.599 that is developing “(AGI): Computer
programs that are potentially capable of understanding, learning, and doing anything.”600 They claimthat their programs will be open-source and trustworthy in order to make life more enjoyable.
According to the website copyright, the website has not been updated since 2017, and its Twitter has
not been active since 2018.601 According to Andreas H.’s LinkedIn, Automorph was active briefly from
April to September 2017.602595

https://profhugodegaris.wordpress.com/punishing-the-mob-money-over-brains-psychology-of-females/
http://information.xmu.edu.cn/en/?mod=departments&id=31597
https://www.crunchbase.com/organization/automatski-fundamental-research598
http://automatski.com/artificial-general-intelligence.html
599
https://www.linkedin.com/in/andreas-h-2a7802142/
600
https://www.automorph.com
601
https://twitter.com/AutomorphHQ
602
https://www.linkedin.com/in/andreas-h-2a7802142/
596

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Reason for consideration: Explicitly AGI focusedReason for exclusion: Apparently inactive

Brain Imaging and Modeling Section (BIMS)
Main website: https://www.nidcd.nih.gov/research/labs/brain-imaging-and-modeling-section
BIMS is a research project led by Barry Horwitz of the US National Institutes of Health. The project
combines brain imaging with computer modeling to advance basic neuroscience and treatment of brain
disorders. There have been no updates since Horowitz retired in 2017.603
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Inactive; focused on basic neuroscience, not the development of an AGI

BRAIN Initiative
Main website: https://www.braininitiative.nih.gov
BRAIN Initiative is a research project aimed at understanding the human brain. BRAIN is an acronym
for Brain Research through Advancing Innovative Neurotechnologies. The project is based at the US
National Institutes of Health and partners with several other US government agencies and privateorganizations.604 Its website states that “by accelerating the development and application of innovativetechnologies, researchers will be able to produce a revolutionary new dynamic picture of the brain that,for the first time, shows how individual cells and complex neural circuits interact in both time andspace.”605
Reason for consideration: A large-scale brain research project similar to Blue Brain
Reason for exclusion: Focused on basic neuroscience, not the development of an AGI

Brain/MINDS
Main website: http://brainminds.jp/en
Brain/MINDS is a neuroscience research project. Brain/MINDS is an acronym for Brain Mapping byIntegrated Neurotechnologies for Disease Studies. The project is sponsored by the Japanese Ministryof Education, Culture, Sports, Science, and Technology (MEXT). It focuses on studying non-humanprimate brains, neural networks of brain disorders, and improving cooperation between basic andclinical neuroscience.606
603

https://www.nidcd.nih.gov/news/2017/retirement-symposium-barry-horwitz-september-8
https://www.braininitiative.nih.gov/about/index.htm
605
https://www.braininitiative.nih.gov
606
http://brainminds.jp/en/overview/greeting
604

121

Reason for consideration: A large-scale brain research project similar to Blue Brain
Reason for exclusion: Focused on basic neuroscience, not the development of an AGI

C-BRIC*
Main website: https://engineering.purdue.edu/C-BRIC
The Center for Brain-inspired Computing Enabling Autonomous Intelligence is a five-year project
supported by $27 million in funding from the Semiconductor Research Corp. (SRC). The mission of
C-BRIC is to “deliver key advances in cognitive computing, to enable a new generation of autonomous
intelligent systems such as self-flying drones and interactive personal robots.”607 It includes 10 US
universities and funding by DARPA.
Reason for consideration: Largely funded university research group focusing on AGI related topics
Reason for exclusion: Not explicitly AGI

Carboncopies
Main website: https://www.carboncopies.org
Carboncopies is a nonprofit based in San Francisco that “provides support to scientists in fields related
to Whole brain emulation.”608
Reason for consideration: A research project focused on related technical details of AGI
Reason for exclusion: Not explicitly AGI; not R&D

CERA-CRANIUM
Main website: https://www.conscious-robots.com
CERA-CRANIUM was a cognitive architecture project sometimes discussed in the context of AGI.609
It was led by Raúl Arrabales of the University of Madrid. It was used for computer games, winning acompetition in 2010.610 Though Arrabales is still actively publishing, the most recent work on CERACRANIUM is from 2013.611
Reason for consideration: An AGI R&D project
Reason for exclusion: Apparently inactive607

https://engineering.purdue.edu/C-BRIC
https://www.carboncopies.org/mission609
For example, Ng et al. (2017)610
Arrabales and Muñoz (2010)
611
Arrabales et al. (2013)
608

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CHAI (Center for Human-Compatible AI)
Main website: http://humancompatible.ai
CHAI is a research group based at the University of California, Berkeley. Its website states that its goalis “to develop the conceptual and technical wherewithal to reorient the general thrust of AI research
towards provably beneficial systems.” This is primarily in the context of “machines that are morecapable than humans across a wide range of objectives and environments,” which it sees as likely toexist eventually.612
Reason for consideration: A research project focused on technical details of AGI
Reason for exclusion: Focused on safety aspects of AGI, not on the development of an AGI

CHREST
Main website: http://chrest.info
CHREST is led by Fernand Gobet of the University of Liverpool. Gobet started CHREST in 1992 andtraces it to the 1959 EPAM system.613 CHREST is an acronym for Chunk Hierarchy and REtrieval
STructures. It is “a cognitive architecture that models human perception, learning, memory, andproblem solving.”614 A paper on CHREST describes its strengths in categorization and understanding
as complementary to other projects (e.g., ACT-R, Soar) in problem-solving.615 Its website lists no
publications since 2017.616
Reason for consideration: An AGI project
Reason for exclusion: Apparently inactive

Cognitive Computing Project
Main website: http://research.ibm.com/cognitive-computing andhttps://www.research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml
CCP is part of a suite of IBM AI projects, which also includes the famed Watson system. Goertzel(2014) discusses a project led by Dharmendra Modha to build computer hardware and softwaresystems modeled after the human brain. The project has produced a new programming language and a
new computer chip called TrueNorth, which Modha postulates as a “turning point in the history ofcomputing.”617 The chip was introduced in a 2014 article in Science.618 The chip development was
supported by the DARPA SyNAPSE program aimed at making “low-power electronic neuromorphic612

http://humancompatible.ai/about
http://www.chrest.info/history.html
614
http://www.chrest.info
615
Lane and Gobet (2012)616
http://chrest.info/publications.html
617
http://www.research.ibm.com/articles/brain-chip.shtml
618
Merolla et al. (2014)
613

123

computers that scale to biological levels.”619 Part of IBM’s recent CCP work includes extensive
research on neuromorphic computing620 and neurosymbolic AI621 with the hope of giving machines theability to perform “more general tasks.”622
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Project focused on hardware development related to AGI, not AGI itself

Cognitive Systems Toolkit (CST)
Main website: http://cst.fee.unicamp.br
CST is a project led by Ricardo Gudwin of the University of Campinas in Brazil. It is “a Java-based
toolkit to allow the construction of Cognitive Architectures.”623
Reason for consideration: A project related to technical aspects of AGI
Reason for exclusion: Focused on tools that could be used to develop AGI, not on building an AGI

ComiritMain website: http://www.comirit.com
Comirit (Commonsense Intelligence and Reasoning through Integrative Technologies)
was a project of Benjamin Johnston used for his Ph.D. at the University of Technology Sydney.624 It
aimed to build “robotic and software systems with commonsense intelligence” with a short-term focus
of “weeks or months, rather than decades.” However, it is “inspired by the long term goals of creating
systems that have deep human-like understanding of the real world,” and thus pursued designs that“can be gradually evolved into more capable systems.”625 It was active mainly between 2010 and 2011.
Reason for consideration: An R&D project with AGI aspirations
Reason for exclusion: Apparently inactive

Covariant*
Main website: https://covariant.ai

619

http://www.darpa.mil/program/systems-of-neuromorphic-adaptive-plastic-scalable-electronics
https://www.ibm.com/blogs/research/2018/07/synaptic-architecture/621
https://mitibmwatsonailab.mit.edu/category/neuro-symbolic-ai/
622
Moskovitch (2020)623
http://cst.fee.unicamp.br
624
https://www.comirit.com/papers/dissertation.pdf
625
http://www.comirit.com
620

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Covariant was founded by Rocky Duan, Tianhao Zhang, Pieter Abbeel, and Peter Chen in 2017.626 Its
goal is “universal AI that allows robots to see, reason, and act on the world around them.”627 They havereported funding from Baidu.628 In an interview, Chen said, “the company is on a quest to solve the hot
research challenge of how do you build general AI for robotics.”629
Reason for consideration: R&D related to AGI
Reason for exclusion: Not enough information to determine AGI research

Dav & SAIL
Main website: http://www.cse.msu.edu/~weng/research/LM.html
Dav & SAIL were projects of Juyang Weng of Michigan State University between 1998 and 2010. The
two robots were designed to learn as human children do. The project aimed at achieving the“machine’s human-level performance through autonomous development.”630 Since 2010, this project
was built upon by the Where What Network (WWN). However, the latest iteration of this is in 2015,
and other publications, as recent as 2020, do not pertain to Dav & SAIL.631 The work received funding
from the National Science Foundation and DARPA.632
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

Decoupled Multimodal Learning*
Main website: https://github.com/Jakobovski/decoupled-multimodal-learning
Decoupled Multimodal Learning is “a decoupled, generative, unsupervised, multimodal neuralarchitecture” created by Zohar Jackson.633 Most of the updates on its GitHub page are from 2017, withat least one from 2018.634
Reason for consideration: GitHub page tagged as AGI
Reason for exclusion: Not clearly focused on AGI

626

https://www.linkedin.com/in/rocky-duan-1a82662a/https://covariant.ai/about-us628
https://covariant.ai/about-us629
Cai (2020)630
http://www.cse.msu.edu/~weng/research/LM.html
631
http://www.cse.msu.edu/~weng/research/LM.html
632
Weng et al. (1999)
633
https://www.linkedin.com/in/zohar-jackson-61362368
634
https://github.com/Jakobovski/decoupled-multimodal-learning
627

125

DeSTIN (Deep SpatioTemporal Inference Network) †Main website: http://wiki.opencog.org/w/DeSTINDeSTIN was initially developed by Itamar Arel and colleagues at the University of Tennessee. It isalso being developed by the OpenCog open-source AI project, formerly known as CogPrime. DeSTIN
used deep learning for pattern recognition. The OpenCog website states that OpenCog “has adopted
this academic project to prepare it for open-source release.”635 Goertzel (2014, p.17) notes that DeSTIN“has been integrated into the CogPrime architecture… but is primarily being developed to serve as thecenter of its own AGI design.”
Reason for consideration: Included in the 2017 AGI survey
Reason for exclusion: Apparently inactive

DSO-CA†
Main website: none found
DSO-CA was a project of Gee Wah Ng when he was at DSO National Laboratories, Singapore’s
primary national defense research agency. It is “a top-level cognitive architecture that models theinformation processing in the human brain,” with similarities to LIDA, OpenCog, and other AGI
cognitive architectures.636 Gee Wah Ng is now at Home Team Science and Technology Agency (HTX)
and is still actively publishing on AGI.637 This is Singapore’s statutory board formed under the
Ministry of Home Affairs to develop science and technology capabilities for Home Team operations,though there is no information that HTX is developing AGI.
Reason for consideration: Included in the 2017 AGI survey
Reason for exclusion: Apparently inactive

DUAL
Main website: http://alexpetrov.com/proj/dual
DUAL was led by Boicho Kokinov at New Bulgarian University. It was active from around 1999 to2005. It was based on Marvin Minsky’s Society of Mind, in which minds are made from interacting
sub-mind “agents.” DUAL integrates symbolic and emergentist approaches and integrates declarative
learning (learning about information one can readily talk about) and procedural learning (learning thatis more habitual and harder to talk about).638
Reason for consideration: Listed in the AGI review paper Goertzel (2014)635

http://wiki.opencog.org/w/DeSTIN
Ng et al. (2017)637
Ng and Leung (2020)638
http://alexpetrov.com/proj/dual
636

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Reason for exclusion: Apparently inactive

Entropica
Main website: http://entropica.com
Entropica is an AGI project and private company led by Alexander Wissner-Gross, and the main
website now links to Wissner-Gross’s website.639 It was included in a 2017 list of AGI projects.640
However, no activity has been identified since 2013. In August 2017, Wissner-Gross did not include
Entropica on a list of companies on his website641 or his CV.642 Entropica is based on ideas WissnerGross published in a research paper in the same year.643 A video describing the project states that it is“broadly applicable to a variety of domains” and shows it functioning in several seemingly different
domains.644 Media coverage described it as a breakthrough to AGI and superintelligence,645 but other
AI researchers have been critical646 and some observers suspect it to be a hoax.647 Forbes describes his
new company Gemedy as being focused on AGI,648 but the Gemedy website does not indicate AGI.649
Reason for consideration: An AGI R&D project
Reason for exclusion: Apparently inactive

Einstein
Main website: https://www.salesforce.com/products/einstein
Einstein is a project of SalesForce that applies AI to its customer service business. Einstein grew out ofthe private company MetaMind, which SalesForce acquired in 2016.650 Chief Scientist Richard Socher
reportedly aspires to build AGI651 and has vocalized interest in AGI.652 However, no indications were
found that Einstein is working on AGI.
Reason for consideration: An AI R&D project led by someone who seeks to build AGI
Reason for exclusion: No AGI R&D found639

https://www.alexwg.org
http://2ai.org/landscape641
http://www.alexwg.org/companies
642
http://www.alexwg.org/AWG-CV.pdf
643
Wissner-Gross and Freer (2013)644
https://www.youtube.com/watch?v=cT8ZqChv8P0
645
Dvorsky (2013)
646
Marcus and Davis (2013)647
https://www.quora.com/How-can-we-prove-that-Entropica-is-a-hoax
648
Schmelzer (2020)
649
https://www.gemedy.com/
650
Novet (2016)
651
The Economist (2016)652
https://twitter.com/richardsocher/status/1084859265121083392?lang=en-gb
640

127

EPIC
Main website: None
Executive-Process Interactive Control (EPIC) was a cognitive architecture led by computer scientist
David Kieras and David Meyer at the University of Michigan. Goertzel (2014, p.16) writes that “it has
been connected to SOAR for problem solving, planning and learning.”
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

EvoGrid
Main website: http://www.evogrid.org
EvoGrid was an open-source artificial life project initiated by Bruce Damer and was submitted as his
Ph.D. thesis.653 It sought to overcome the computing challenge of artificial life by accessing a
distributed network of computer hardware similar to that used by projects like SETI@home. The
project website shows no updates since around 2010, though Damer’s work on artificial life
continues.654
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

GLAIR
Main website: none found
Grounded Layered Architecture with Integrated Reasoning (GLAIR) was a project of Stuart Shapiro ofState University of New York at Buffalo. It was active between 1993 and 2013.655 GLAIR aimed for“computational understanding and implementation of human-level intelligent behavior without
necessarily being bound by the actual implementation of the human mind.”656
Reason for consideration: An AGI R&D project
Reason for exclusion: Apparently inactive

GMU BICA
Main website: https://krasnow.gmu.edu/asl/bica-project/653

http://www.evogrid.org/index.php/Main_Page
http://www.damer.com
655
https://www.cse.buffalo.edu/~shapiro/Papers
656
Shapiro and Bona (2010, p. 307)
654

128

GMU Bica was a project of Alexei Samsonovich of George Mason University, and it was active
between 2006 and 2007. The project has moved to the Adaptive Systems Laboratory and is listed as apast project by the name of “Integrated Self-Aware Cognitive Architecture (DARPA BICA).”657
Samsonovich seems to still be regularly publishing around the concept of an eBICA (emotional
Biologically Inspired Cognitive Architecture) and seems to work in the field of “Artificial Emotional
Intelligence.”658
Reason for consideration: An AGI R&D project
Reason for exclusion: Apparently inactive

Goedel Machine
Main website: http://people.idsia.ch/~juergen/goedelmachine.html
Goedel Machine was a project of Jürgen Schmidhuber of the Dalle Molle Institute for Artificial
Intelligence Research in Switzerland. The Goedel Machine proceeds by taking the action it proves tobe best at each step in its activity, which requires infinite computing power.659 Schmidhuber writes on
his website that “since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build aself-improving Artificial Intelligence (AI) smarter than himself, then retire.”660
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

Google Brain
Main website: https://research.google.com/teams/brain
Google Brain is an AI research group at Google. Its researchers have collaborated with DeepMind on
AGI research,661 but its work is mainly focused on machine learning.662
Reason for consideration: A research group with links to AGI
Reason for exclusion: Not sufficiently focused on AGI

Hanson Robotics*
Main website: https://www.hansonrobotics.com
657

https://krasnow.gmu.edu/asl/research/https://scholar.google.com/citations?hl=en&user=AIvKvXcAAAAJ&view_op=list_works&sortby=pubdate
659
Goertzel (2014) p.25
660
http://people.idsia.ch/~juergen
661
Fernando et al. (2017) and Wiggers (2020)
662
https://research.google.com/teams/brain/about.html
658

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Sophia is the humanoid robot of Hanson Robotics, founded by David Hanson in 2013.663 Hanson
Robotics describes itself as “an AI and robotics company dedicated to creating socially intelligent
machines that enrich the quality of our lives.”664 Hanson Robotics is a partner of Ben Goertzel andSingularityNET.665 Sophia has her own Twitter.666
Reason for consideration: Connections to AGI research
Reason for exclusion: Not explicitly AGI focused

HUMANOBS
Main website: none
HUMANOBS (Humanoids that Learn Socio-communicative Skills by Observation)
was a project for developing robots that “can learn social interaction,” which is “a big step towards theultimate goal of creating intelligence that is both self-sufficient and adaptable in a wide variety ofenvironments.” The EU Grant agreement for the project ran from 2009 to mid- 2012, but
HUMANOBS continued to be active until 2014.667
Reason for consideration: A former AGI R&D project
Reason for exclusion: Apparently inactive

IM-CLEVER
Main website: none
IM-CLEVER (Intrinsically Motivated Cumulative Learning Versatile Robots) was a project to design
robots that could learn independently and apply their knowledge across contexts. It has been inactive
since around 2013, and as of 2020, their original website no longer works.668 It was funded by an EU
grant, which ran from 2009 to early-2013.669 It was led by Gianluca Baldassarre of Istituto di Scienze e
Tecnologie della Cognizione (Institute of Cognitive Sciences and Technologies) in Italy. It included
collaborators from around Europe and one group in the United States.670 It worked on a robotics
platform “iCub” for robots that could learn new skills and apply them to diverse tasks.671
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive
663

https://www.linkedin.com/company/hanson-robotics/about/https://www.hansonrobotics.com/about/665
https://www.hansonrobotics.com/research/666
https://twitter.com/RealSophiaRobot667
https://cordis.europa.eu/project/id/231453
668
http://www.im-clever.eu
669
https://cordis.europa.eu/project/id/231722
670
https://ec.europa.eu/digital-single-market/en/blog/im-clever-towards-intelligent-humanoids
671
https://icub.iit.it/about-us/icub-history
664

130

IntelMain website: https://www.intel.com/content/www/us/en/research/neuromorphic-computing.html
Intel does not currently indicate any research explicitly on AGI but is active in AI research.672 It has
acquired several AI companies.673 In a 2017 white paper, they write, “while there isn’t a commonly
accepted definition for AI, Intel views it as a computerized system that performs tasks we normallyassociate with people. But in spite of the remarkable advances of computing power and sophisticatedalgorithms, there is still a long way to go before what is called General AI becomes a reality.”674
However, their Loihi Chip is a piece of neuromorphic (SNN) hardware which “supports dramaticallyaccelerated learning in unstructured environments for systems that require autonomous operation andcontinuous learning.” It has been compared to Tsinghua University’s Tianjic Chip, which aims toadvance AGI.
Reason for consideration: Hardware possibly used for AGI development
Reason for exclusion: Not explicitly AGI

IPSEL*
Main website: https://www.researchgate.net/project/Human-level-cognitive-architecture
Information Processing Systems with Emergent Logic (IPSEL) is a recent cognitive architecture
project created by Bryan Fruchart and Beniot Leblanc in 2019.675 Although it can be considered AGI,
its purpose is to analyze the relationship between humans and future AI systems.676
Reason for consideration: Exploring AGI-related cognitive architectures
Reason for exclusion: Not R&D

Israel Brain Technologies (IBT)
Main website: http://israelbrain.org
IBT is a neuroscience research project. It is an Israeli nonprofit based in Ramat HaSharon. It receives
funding from the Israeli government, philanthropists, and corporations.677 Its mission is “to accelerate
the development of innovative treatments and cures for brain disease.”678 The website’s latest blog post672

https://www.intel.com/content/www/us/en/analytics/artificial-intelligence/overview.html andhttps://www.intel.ie/content/www/ie/en/analytics/artificial-intelligence/overview.html andhttps://newsroom.intel.com/press-kits/artificial-intelligence/#gs.hjl520
673
Tamturk (2017)674
https://blogs.intel.com/policy/files/2017/10/Intel-Artificial-Intelligence-Public-Policy-White-Paper-2017.pdf
675
Fruchart and Le Blanc (2019)
676
Fruchart and Le Blanc (2020)677
http://israelbrain.org/donate678
http://israelbrain.org/about-us/mission131was in 2018, and its rolling header is advertising the BrainTech2019 conference (March 4-5 2019).679
The latest tweet was on December 2019680 and the latest Facebook posts were in June 2020,681 though
the posts are not on the work of IBT.
Reason for consideration: A large-scale brain research project similar to Blue Brain
Reason for exclusion: Focused on basic neuroscience, not the development of AGI

Kuipers Group
Main website: https://www.cs.utexas.edu/users/qr/robotics/bootstrap-learning.html
Benjamin Kuipers of the University of Texas led a group that developed robots that would learn a wide
range of information about the world on their own from their own experiences. They were actively
publishing between 1997 and 2007. Benjamin Kuipers now supervises the Intelligent Robotics Lab
who are currently publishing papers including on the topics of “Bootstrap Learning of Foundational
Representations” and “Ethics of AI and Robotics.”682
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

Large-Scale Model of Mammalian Thalamocortical Systems(LSMMTS)
Main website: https://www.izhikevich.org/publications/large-scale_model_of_human_brain.htm
LSMMTS was a project of Eugene Izhikevich and Gerald Edelman of The Neurosciences Institute.
LSMMTS is notable for being a brain simulation “on a scale similar to that of the full human brain
itself.”683 Eugene Izhikevich left the Neurosciences Institute in 2009 and is now CEO of Brain Corp,
which is an AI company focusing on autonomous robots.684
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

Machine Intelligence Research Institute (MIRI)
Main website: https://intelligence.org
679

http://israelbrain.org/
https://twitter.com/IsraelBrainTech
681
https://www.facebook.com/israelbraintech?ref=hl
682
https://web.eecs.umich.edu/~kuipers/research/
683
Goertzel (2014) p.19
684
http://www.scholarpedia.org/article/User:Eugene_M._Izhikevich
680

132

MIRI is an independent nonprofit research group focused on “foundational mathematical research toensure smarter-than-human artificial intelligence has a positive impact.”685 It states that its mission is“to develop formal tools for the clean design and analysis of general-purpose AI systems, with theintent of making such systems safer and more reliable when they are developed.”686
Reason for consideration: A research project focused on technical details of AGI
Reason for exclusion: Focused safety aspects of AGI, not on the development of an AGI

MicroPsi†
Main website: http://cognitive-ai.com
MicroPsi was led by Joscha Bach of the Harvard Program for Evolutionary Dynamics until 2019.687
Bach’s mission was reportedly “to build a model of the mind is the bedrock research in the creation ofStrong AI, i.e., cognition on par with that of a human being.”688 Bach now works as the Vice Presidentof Research at the AI Foundation.689
Reason for consideration: Included in the 2017 AGI survey
Reason for exclusion: Apparently inactive

MLECOG†
Main website: none
MLECOG is a cognitive architecture project led by Janusz Starzyk of Ohio University. A paper on
MLECOG describes it as similar to NARS and Soar.690 MLECOG is an acronym for MotivatedLearning Embodied Cognitive Architecture.
Reason for consideration: Included in the 2017 AGI survey
Reason for exclusion: Apparently inactive; not R&D

Neuralink*
Main website: https://www.neuralink.com

685

https://intelligence.org
https://intelligence.org/about687
https://www.linkedin.com/in/joschabach/688
http://bigthink.com/experts/joscha-bach689
https://www.aifoundation.com/about/
690
Starzyk and Graham (2015)
686

133

Neuralink is an Elon Musk project “developing ultra-high bandwidth brain-machine interfaces toconnect humans and computers.”691 The project aims to create neural implants in order to be connected
to mobile devices at all times. There does not seem to be any connection to AGI.
Reason for consideration: Popular technology company
Reason for exclusion: Not AGI

Neurogrid
Main website: https://web.stanford.edu/group/brainsinsilicon/neurogrid.html
Neurogrid is computer hardware designed for running low-cost brain simulations. It is part of Stanford
University’s Brains in Silicone group, which is led by Kwabena Boahen.692 A 2014 version of
Neurogrid claims to be “9,000 times faster and using significantly less power than a typical PC,” butstill much less energy-efficient than the human brain.693
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Focused on hardware development related to AGI, not AGI itself

NOMAD (Neurally Organized Mobile Adaptive Device)
Main website: none
NOMAD was a project of the Neurosciences Institute, a nonprofit research institute in California led
by the late Nobel Laureate Gerald Edelman. The website listed in the 2017 survey redirects to The
Neurosciences Institute with a new focus of “understanding how consciousness arises from the activityof the nervous system” and is likely inactive as well.694
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

OSCAR
Main website: http://johnpollock.us/ftp/OSCAR-web-page/oscar.html
OSCAR was a project of the late John Pollock of the University of Arizona, active from around 1995
to 2005.695 Pollock writes, “the ‘grand problem’ of AI has always been to build artificial agents of691

https://neuralink.com
https://web.stanford.edu/group/brainsinsilicon/index.html
693
http://news.stanford.edu/pr/2014/pr-neurogrid-boahen-engineering-042814.html, a press release discussing Benjamin etal. (2014)694
http://www.nsi.edu/~nomad695
http://johnpollock.us
692

134

human-like intelligence… capable of operating in environments of real-world complexity… OSCAR isa cognitive architecture for GIAs [generally intelligent agents], implemented in LISP.”696 Pollock
described Oscar’s main features as the ability to reason defeasibly about perception, change, andpersistence, causation, probabilities, plan construction and evaluation, and decision.697
Reason for consideration: An AGI R&D project
Reason for exclusion: Apparently inactive

PAGI World
Main website: http://rair.cogsci.rpi.edu/projects/pagi-world
Psychometric Artificial General Intelligence (PAGI) World is a project led by John Licato of the
University of South Florida and based at Rensselaer Polytechnic Institute, where Licato was a Ph.D.
student. PAGI world is “a simulation environment written in Unity 2D, which allows AI and AGI
researchers to test out their ideas.”698 The website hosted by the Rensselaer AI and Reasoning Lab(RAIR)699 states that PAGI World is no longer maintained by the RAIR Lab and points to a site hosted
by the Advancing Machine and Human Reasoning Lab (AMHR - though that Lab also states that it
does not maintain PAGI).700
Reason for consideration: A project on an aspect of AGI R&D
Reason for exclusion: Inactive; focused on tools for evaluating AGI, not on developing an AGI

PolyScheme
Main website: https://dspace.mit.edu/handle/1721.1/8325
PolyScheme was developed for the Ph.D. thesis of Nicholas Cassimatis at the Massachusetts Instituteof Technology. It “integrates multiple methods of representation, reasoning, and inference schemes forgeneral problem solving.”701 The project is no longer active. Cassimatis now works at Dry.io.702
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

696

Pollock (2008)https://johnpollock.us/ftp/OSCAR-web-page/oscar.html
698
http://rair.cogsci.rpi.edu/projects/pagi-world699
https://rair.cogsci.rpi.edu/past-projects/pagi-world/
700
https://sites.google.com/view/amhr/research/pagi-world
701(Goertzel 2014, p.24)
702
https://www.linkedin.com/in/nickcassimatis/
697

135

project-origin*
Main website: https://github.com/kourgeorge/project-origin
project-origin is an artificial life simulator for investigating noogenesis created by George Kour, amember of the Machine Learning Technologies Group at the IBM research lab and a Ph.D. candidatein the Sagol Department of Neurobiology at the Haifa University.703 Though it does not explicitly statea goal or mission of AGI, it can be compared to Unscripted (Appendix 3).
Reason for consideration: AGI related
Reason for exclusion: Apparently inactive; not R&D

Quantum Artificial Intelligence Lab (QAIL)
Main website: https://research.googleblog.com/2013/05/launching-quantum-artificial.html
QAIL is a project between Google704 and NASA705 seeking to use quantum computing to advance AI. It
is a “hub for assessing the potential of quantum computers to impact computational challenges facedby the agency in the decades to come.” QAIL is listed as an AGI project by 2AI,706 and somecommentators propose that quantum computing could be important for developing AGI.707 However,
QAIL gives no indications of aiming for AGI.
Reason for consideration: Listed as an AGI project by 2AI
Reason for exclusion: No apparent AGI focus

Real AI†
Main website: http://realai.org
Real AI was a private company in Hong Kong led by Jonathan Yan, also on the EthicsNet team.708 Its
mission was “to ensure that humanity has a bright future with safe AGI.”709 It worked on strategy forsafe AGI and technical research in deep learning, the latter on the premise that deep learning can scale
up to AGI.710
Reason for consideration: Included in the 2017 AGI survey
Reason for exclusion: Apparently inactive703

https://github.com/kourgeorge
Neven (2013)705
https://ti.arc.nasa.gov/tech/dash/groups/quail/706
http://2ai.org/landscape707
For example, Wang (2014), DeAngelis (2014)708
https://www.ethicsnet.org/about709
https://web.archive.org/web/20170806002223/http://realai.org/about710
https://web.archive.org/web/20170806011538/http://realai.org/prosaic
704

136

Robust AI*
Main website: https://www.robust.ai
A project led by Gary Marcus, “building the world’s first industrial-grade cognitive engine.” Their
mission is to make robots “smart, collaborative, robust, safe, flexible, and genuinely autonomous.”
Reason for consideration: Referenced in Marcus and Davis (2019)
Reason for exclusion: Not explicitly AGI

SAL (Synthesis of ACT-R and Leabra)
Main website: None
SAL cognitive architecture was developed by David Jilk, Christian Lebiere, and colleagues at theeCortex Corporation of Boulder, Colorado, the University of Colorado, and Carnegie Mellon
University.711 The project produced a brief publishing record712 and appears inactive since 2008. As its
name implies, SAL is based on ACT-R (see dedicated ACT-R entry) and Leabra, a neural simulation.
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

Scene Based Reasoning (SBR)
Main website: http://agi-conf.org/2015/wp-content/uploads/2015/07/agi15_bergmann.pdf
SBR is an AGI R&D project by Frank Bergmann and Brian Fenton presented at the 2015 AGI
conference, but apparently inactive since. It is described as “a cognitive architecture based on thenotions of scene and plan. Scenes represent real-world 3D scenes as well as planner states.”
Reason for consideration: An AGI R&D project
Reason for exclusion: Apparently inactive

Shruti
Main website: http://www1.icsi.berkeley.edu/~shastri/shruti
Shruti was a project of Lokendra Shastri of the University of California, Berkeley, and was active
between 1996 and 2007. The project developed computational tools based on fast, reflex-like human
711
712

Jilk et al. (2008)
E.g., Jilk et al. (2008)137

inference. It has been funded by, among others, the US National Science Foundation, Office of Naval
Research, and Army Research Institute.713
Reason for consideration: Listed in the AGI review paper Goertzel (2014)
Reason for exclusion: Apparently inactive

SiMA†
Main website: http://sima.ict.tuwien.ac.at/description
SiMA was a project led by Dietmar Dietrich of Vienna University of Technology. SiMA is an acronym
for the Simulation of the Mental Apparatus & Applications. The project aims “to develop a broad
human-like intelligent system that is able to cope with complex and dynamic problems rather than withnarrowly and well-defined domains.”714 It includes extensive attention to psychoanalysis, especially
Freud and other German-language scholars. Dietrich started SiMA in 1999.715
Reason for consideration: Included in the 2017 AGI survey
Reason for exclusion: Apparently inactive

SNePS (Semantic Network Processing System) †Main website: http://www.cse.buffalo.edu/sneps
SNePS was led by Stuart Shapiro at State University of New York at Buffalo, with a publication record
dating to 1969.716 According to the SNePS website, its long-term goal is “to understand the nature ofintelligent cognitive processes by developing and experimenting with computational cognitive agentsthat are able to use and understand natural language, reason, act, and solve problems in a wide variety
of domains.”717
Reason for consideration: Included in the 2017 AGI survey
Reason for exclusion: Apparently inactive

SonyAI*
Main website: https://ai.sony713

http://www1.icsi.berkeley.edu/~shastri/shruti
http://sima.ict.tuwien.ac.at/description
715
Brandstätter et al. (2015, p.V)716
http://www.cse.buffalo.edu/sneps/Bibliography717
http://www.cse.buffalo.edu/sneps
714

138

SonyAI is the research portion of Sony. SonyAI hopes to use AI to “help unleash human imaginationand creativity.” Sony recently acquired an AI company called Cogitai.718 No indications of AGI could
be identified.719
Reason for consideration: Large technology company
Reason for exclusion: Not AGI; not R&D

Tianjic Chip*
Main website: https://www.cbicr.tsinghua.edu.cn/english/
A neuromorphic chip created by the Center for Brain-Inspired Computing Research at Tsinghua
University. The chip can support both SNN and ANN, including convolutional neural networks(CNNs), multilayer perceptrons (MLPs), and recurrent neural networks (RNNs). The chip’s goal is “tostimulate AGI development by paving the way to more generalized hardware platforms.”720 It has been
compared with Intel’s Loihi Chip.
Reason for consideration: Goal of AGI development
Reason for exclusion: Hardware focused

Twitter
Main website: https://cortex.twitter.com
Twitter has an AI group called Cortex, but no indications of AGI work were found. It also uses IBM
Watson for its natural language processing to remove abusive messages.721
Reason for consideration: Twitter is a major computing technology company
Reason for exclusion: No indications of AGI projects were foundUnscripted*
Main website: https://github.com/gnitr/unscripted
Unscripted is a “naive proof-of-concept for a collaborative online virtual world to experiment withunscripted bots. Primary Objective: experiment with adaptive, artificial human-like agents (0 priorknowledge about world, no training data, general purpose, 100% algorithmically controlled); a sort of718

https://www.sony.net/SonyInfo/News/Press/201605/16-052E/
https://ai.sony
720
Pei et al. 2019721
https://www.bernardmarr.com/default.asp?contentID=1373
719

139

artificial general intelligence.” Updates to its GitHub page are mostly dated 2017, with at least one
dated 2018.722
Reason for consideration: AGI oriented
Reason for exclusion: A platform for studying AGI-like entities, not for building AGI

VICTOR†
Main website: http://2ai.org/victor
VICTOR was the main project of 2AI, a subsidiary of the private company Cifer Inc. 2AI is led by
Timothy Barber and Mark Changizi.723 The VICTOR website stated, “the VICTOR project will bring‘other-awareness’ to AI” and refers to the project as the ‘emotion chip’.”724
Reason for consideration: Included in the 2017 AGI survey
Reason for exclusion: Apparently inactive

XapagyMain website: http://www.xapagy.com
Xapagy is a cognitive architecture designed “to perform narrative reasoning, that is to model/mimic themental processes humans perform with respect to stories.”725 A 2014 paper on Xapagy won the“Kurzweil Best AGI Idea Prize 2014.”726
Reason for consideration: A cognitive architecture presented at an AGI conference727
Reason for exclusion: Inactive; focus on narrative makes it ultimately narrow AI

YmirMain website: http://alumni.media.mit.edu/~kris/ymir.html
Ymir (an implementation of Gandalf)728 was a project of Kristinn Thórisson and was active between
1999 and 2009.729 It “was created with the goal of endowing artificial agents with human-likecommunicative and manipulation capabilities in the form of embodied multimodal task-oriented dialog
722

https://github.com/gnitr/unscripted
https://web.archive.org/web/20201026190747/http://2ai.org/legal
724
https://web.archive.org/web/20181113082134/http://victor.ai
725
http://www.xapagy.com/?page_id=26
726
Bölöni (2014)
727
Bölöni (2014)728
http://alumni.media.mit.edu/~kris/gandalf.html
729
http://alumni.media.mit.edu/~kris/ymir.html
723

140

skills.”730 The most recently published paper was in 2009,731 and the Ymir website was last updated in2010.
Reason for consideration: An AGI R&D project
Reason for exclusion: Apparently inactive

730
731

Thórisson and Helgasson (2012), p.8
http://alumni.media.mit.edu/~kris/ymir.html#papers

141Appendix 4. Other Projects
The following projects have stated that they have a focus on AGI, but they otherwise demonstrate little
identifiable activity on AGI.

















AGI Concept Map: https://github.com/tomzx/agi-concept-map
o The AGI Concept Map is a GitHub project by Tom Rochette, which is “reconstructing all
the internal knowledge I’ve acquired about artificial general intelligence over the years.” It
was last updated in May 2017.
AGI Literature Bank: https://github.com/wasifferoze/agi-literature-banko Artificial General Intelligence Literature Collection is a collection of AGI resources by
Wasif Feroze.
AI Foundation: https://aifoundation.com
o AI Foundation is a project founded by Lars Butler that aims to give every person their own
AI that shares their personal goals and values. They also hope to fight increasingly realistic
deepfakes. They have recently received significant funding.732
Artificial Intelligence: https://github.com/MrRobb/Artificial-Intelligenceo A collection of AI projects by Roberto Ariosa Hernández. The project’s last commit was in2018. The three projects are on vehicles, Tetris AI, and genetic algorithms. None appear to
be AGI, but the project is tagged as such.
Awesome Artificial Intelligence: https://github.com/fairy-tale-agi-solutions/awesome-artificialgeneral-intelligenceo A GitHub project created by Răzvan Flavius Panda with information and resources about
the subject of AGI (including scientific articles, movies, anime, podcasts, etc.). Projects
included in the ‘Organizations’ section have either been coded in 2017 or 2020.
Awesome Continual Learning: https://github.com/szrlee/awesome-continual-learning
o Awesome Continual Learning is a collection of resources about Continual Learning. It waslast updated in 2018 and was created by Richard Li.
Awesome Deep Reinforcement Learning: https://github.com/tigerneil/awesome-deep-rlo Awesome Deep Reinforcement Learning is an active GitHub project run by Xiaohu Zhu,
whose GitHub description describes him as “Founder & Chief Scientist of University AI. A
Watchful Guardian for AGI.” The GitHub is a repository of papers, but not an AGI project.
Zhu’s LinkedIn lists him as the founder of multiple AGI related organizations, none ofwhich have an internet presence.733
Blog.TomRochette: https://github.com/tomzx/blog.tomrochette.com-content
o Blog.tomrochette.com contains the content of the blog of Tom Rochette. Rochette works
for ElementAI, an organization that does not appear to be working on any AGI projects.734
Caesium: https://github.com/generic-github-user/Caesiumo Caesium is a “General-purpose AI library with NEAT-style genetic algorithm.” It is acollection of AI algorithms rather than an AGI project.
Chatbot with Python: https://github.com/ganeshkavhar/Chatbot-with-Python-by-ganesh-kavharo A chatbot written in Python by Ganesh Kavhar. No detection of AGI, but is tagged as such.
Checkers AI: https://github.com/sirCamp/CheckersAI732

https://aifoundation.com/ai-foundation-q3-newsletter/
https://www.linkedin.com/in/xiaohu-zhu-97686637/
734
https://www.elementai.com/
733

142

















o Checkers AI is an artificial intelligence that can play checkers. No detection of AGI, but istagged as such.
Cognitive Biotechnology: https://www.crunchbase.com/organization/cognitive-biotechnology
o According to Crunchbase, they “develop new treatments with Cognitive Computing using
Artificial General Intelligence.” However, their listed website does not work, nor is there
additional information.
Creating a Chatbot: https://github.com/spydaz/Creating-a-Chatboto A chatbot project created by Leroy Dyer. This is not an AGI project, but it is tagged as such
on GitHub. The new version of the chatbot is not tagged as AGI.735 Dyer’s LinkedIn also
does not suggest he is working on any AGI projects.736
Date and Time Library in Python: https://github.com/ganeshkavhar/Date-and-Time-Library-inPython-by-ganesh-kavhar
o DateTime Library is a project written in Python and created by Ganesh Kavhar. No AGI
was detected.
Distributed Deep Reinforcement Learning for Large Scale Robotic Simulations:
https://github.com/AmrMKayid/KayDDRLo A project by Amr Kayid which appears to be Kayid’s undergraduate thesis.
Evolving ANN: https://github.com/AlexanderKoch-Koch/EvolvingANN
o Spiking Artificial Neural Network is a project by Alexander Koch. It is an “attempt to build
a simple spiking artificial neural network with a reward-driven Hebbian learning function.”
God: https://github.com/JordanMicahBennett/God
o God is the GitHub project of Jordan Micah (God)737 Bennett and is “a non-absolute, timespace complex optimal artificial brain (composed in matrix laboratory (MatLab)” and seeks
to contribute to AGI research. His other projects are the Supermathematics and AGI projectand Supersymmetric ANN (both found in Appendix 4), and he believes that the purpose ofhuman life is to build AGI.738
Helmholtz Machines: https://github.com/gautam1858/Helmholtz-Machineso Helmholtz Machines is a GitHub project created by Gautam Ramachandra with the goal of“implementation to understand learning.” His LinkedIn describes his interest in Machine
Learning but not AGI.739
Human Artificial General Intelligence: https://github.com/OneCivilization/HumanArtificial_General_Intelligenceo A Gitbook about AGI written in Chinese which was last updated in 2018.
Incremental Machine Learning: https://github.com/florianwiech/incremental-machine-learningo Comparative Evaluation of Incremental Machine Learning is a project by Florian Wiech
comparing machine learning techniques and was last updated in mid-2019.
Jiva.ai: https://www.jiva.ai
o Predictive analytics in healthcare through AI. No indication of AGI on their website, but
their Crunchbase says, “The core technology puts fusion at the heart of its learning
procedure, enabling multimodal AI. Fusion is the process by which one can iteratively build
partial models independently and then join them together to create bigger and better
representations. Model fusion is a fundamental prerequisite to artificial general intelligence.

735

https://github.com/spydaz/Chatbot_2020_Tutorial
https://www.linkedin.com/in/leroy-dyer-msc-data-science-744a8230/?originalSubdomain=uk
737
Bennett (2020)
738
Bennett (2015)739
https://www.linkedin.com/in/gautamrbharadwaj/?originalSubdomain=in
736

143
















Using the platform as a basis, the Jiva team will build practical diagnostic solutions tohuman diseases.”
Kobe: https://github.com/minuteman1911/kobeo Kobe is “an open-source simulation tool written in Python which can model artificial
spiking neural networks, along with its environment (using OpenAI Gym ), with more
focus on the computational or functional aspect rather than the biophysical one.” It was
created by Aishwarya Dabhade who describes it as an attempt to bring ANNs more in linewith General Intelligence, to bridge the gap between “how a biological neural network
works and how an artificial one works.” The goal of Kobe "is not full brain simulation, sucha thing is premature, inconceivable and might not even be possible at all. The goal is to help
AI researchers, neuroscientists and enthusiasts in creating, testing and implementing novel
algorithms, morphologies and methods which are closer to the biological brain while
focusing on the computational rather than the biological aspect of the simulation.”740
Lisa: http://www.ai-machine.com/index.html
o Lisa is the AI assistant from the organization AI Machine. They claim it is true AGI: “LISA
is NOT a domain specific AI Bot that has been trained using Machine Learning models justto do one thing right. LISA is powered by a Cognitive Architecture with Deep Semantic
understanding based on Artificial General Intelligence (AGI or Strong AI) and Natural
Language Understanding (NLU) Techniques from the leading AI Research Labs.”741
Lya: https://github.com/lya-corp/lyao Lya is a GitHub repo by Lya Corp,742 the developer of the Kalliope project, an “Alexaesque” voice assistant.743
Medsis: https://www.medsis.com
o A medical tech company; The website does not reference AGI, but their description on
Crunchbase does: “Our platform enables access, management, integration, consolidation,
machine learning, sharing and distribution, analytics, and artificial general intelligence(AGI).”744
MetaQuants: https://www.crunchbase.com/organization/metaquantso According to Crunchbase, they are a “Data-Driven Self-Adaptive Evolving Investment
Platform using Cognitive Computing with Artificial General Intelligence.” However, theirlisted website does not work, and there is no additional information.
Neural GPU Algorithm Learner:
https://github.com/prakashsellathurai/Neural_gpu_algorithm_learnero A “Neural Network that learns algorithms” created by Prakash Sellathurai. It is based on a2015 preprint of a similar name.745
PeakPerformance: https://www.crunchbase.com/organization/peakperformance-aio According to Crunchbase, “PeakPerformance delivers a high-performance cognitive
computing framework using Artificial General Intelligence (AGI) & Genetic Algorithms.”
However, their listed website does not work and there is no additional information.
Pegasus: https://github.com/PegasusProject/Pegasus
o Pegasus is a “multi-platform, intelligent and extremely modular daily life management
program.” It was created by the Pegasus Project GitHub user. It seems to be a sort of Alexa-

740

https://minuteman1911.github.io/kobe/
https://www.crunchbase.com/organization/ai-machines
742
https://www.linkedin.com/company/lya-electronic-corp/743
https://kalliope-project.github.io/744
https://www.crunchbase.com/organization/medsis-medical-systems
745
Kaiser and Sutskever (2015)
741

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


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

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

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

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esque project, using “general” to mean a general-purpose AI voice assistant rather than
AGI.
Project Origin: https://github.com/kourgeorge/project-origino A GitHub project which attempts “to create an artificial life environment that allows
studying the emergence and evolution of intelligence.” No detection of explicit AGI, buttagged as such.
Reinforcement Learning Research Lab (RLRL): https://github.com/NaxAlpha/rl-rl
o The RLRL toolkit is used to “build agents of any windows game you have using python.” It
was created by Nauman Mustafa and appears to be inactive.
SORN: https://github.com/Saran-nns/sorno Self-Organizing Recurrent Neural Networks created by Saranraj Nambusubramaniyam is a“class of neuro-inspired artificial network build based on plasticity mechanisms inbiological brain and mimic neocortical circuits ability of learning and adaptation throughneuroplasticity mechanisms.” Use Cases and the API are stored in the separate repo
PySORN_0.1.746
Sigmind: https://sigmind.aio A computer vision AI company. The website does not reference AGI, but the Crunchbase
description does: “To make it more accelerated and public friendly, we are working on
putting research hours to bring the holy grail of artificial general intelligence for practicalreal world engineering applications to ease human life.”747 Crunchbase also states they arecollaborating with Deepmind, Microsoft, and Facebook.
Spydaz Web AI Intelligent Agent: https://github.com/spydaz/SpydazWebAI_IntelligentAgento Leroy Dyer created SpydazWebAI_IntelligentAgent as part of the SpydazWebAI project.748
The project appears to be a chatbot and not an AGI.
Supermathematics and Artificial General Intelligence:https://github.com/JordanMicahBennett/Supermathematics-and-Artificial-General-Intelligenceo A GitHub project detailing Jordan Micah (God) Bennett’s contribution to AGI called the“Supermanifold Hypothesis.” Bennett has other projects that discuss this theme, such as
Supersymmetric Artificial Neural Network and God (both Appendix 4).
Supersymmetric ANN: https://github.com/JordanMicahBennett/Supersymmetric-artificial-neuralnetworko A “Lie Superalgebra aligned algorithmic learning model, based on evidence pertaining toSupersymmetry in the biological brain” created by Jordan Micah (God) Bennett.
Thoughts on AI Trust: https://github.com/jonathan-smith-1/Thoughts-on-AI-Trusto A GitHub compilation of thoughts about AI Safety by Jonathan Smith.
Tree Algebra: https://github.com/raviq/tree_algebrao A project that Manipulation of binary tree topologies by Hafi R. whose “Solving Tree
Problems with Category Theory” was published in AGI 2018.
Voice Activity Detection Final Project Work: https://github.com/samimoftheworld/Voice-ActivityDetection-FInal-Project-worko A project created for the BTech degree of Samim Ekram, a “Freelancing Machine Learning
and Artificial Intelligence Developer and Enthusiast.”746

https://github.com/Saran-nns/PySORN_0.1
https://www.crunchbase.com/organization/sigmind
748
http://www.spydazweb.co.uk
747

145

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