RESEARCH
4.0
INTERIM REPORTELLIOT JONESNICOLINA KALANTERYBEN GLOVEROCTOBER 2019

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2

CONTENTS
EXECUTIVE SUMMARY

PAGE 4INTRODUCTION

PAGE 5SECTION ONE: TOOLS ANDTECHNIQUESPAGE 8SECTION TWO: BROADER EFFECTS
OF TECHNOLOGY ON THE RESEARCHPROCESS

PAGE 13

SECTION THREE: REPLACEMENT OF
THE RESEARCHER

PAGE 15

SECTION FOUR: CHALLENGES

PAGE 17

NEXT STEPSPAGE 19

APPENDIXPAGE 21

REFERENCES

PAGE 23

3

EXECUTIVE
SUMMARY
The world of research is rapidly changing. The past
20 years have seen huge developments in the way
research is conducted, with many research tasks
dramatically changed. There is a growing consensus
that we are at the start of a 4th industrial revolution,with the rise of the Internet of Things, 3-D printing,nanotechnology, biotechnology, 5G, new forms ofenergy storage and quantum computing.2

We find that:

It is evident that research has been changed bythese new technologies and in particular thegrowing use of machine learning and other artificial
intelligence techniques to augment the work of
research staff and alter their experience of research.
In some cases, it may even replace them altogether.
However, the rapid pace at which AI is developing
means that there are many unanswered questions
about exactly how technology is changing research.
What will the long-term effects be? How should
policy makers approach the increasing use of AI in
research?
This paper describes the progress of AI throughhistory and how it has led researchers to develop
a range of techniques including natural language
programming and computer vision. We thenexamine these techniques and their practicalapplication across different scientific fields, andthe ways in which this has augmented research
processes. This leads us to question the exact roleof AI and the extent to which it can replace humanresearchers. We look at both the challenges posedby the advancement of AI and the challenges that
may hinder this progress. Finally, we raise questions
to be considered for the future of AI in research
and for policy making in the second phase of thisresearch project.

1•

Artificial intelligence and robotics havemade significant progress since the Second
World War. The exponentially increasing
computational capacity of computers has, overthe last decade, enabled the use of machinelearning and artificial neural networks whichhave dramatically increased the potential of
artificially intelligent systems.

•

Across many fields and industries, researchis adopting a range of tools and techniques
enabled by artificial intelligence, including:natural language processing, computer vision,
automated monitoring, automated experiment
selection and the prediction of physical systems.

•

There is an open question as to whether
this automation of research will free up andempower researchers to be more creative andproductive, or whether it will instead replacethem entirely. This may vary from field to field.

•

It is also uncertain how long the current rateof progress in artificial intelligence can keep
up due to hardware limitations. Further, thecosts involved may be prohibitive for all but
the richest countries and companies to deploy
these technologies in research.

From this, we have a number of questions about
how best to forecast the impact of the convergenceof 4th industrial revolution technologies, including
artificial intelligence, on research and what policy
interventions may be needed to ensure the mostbeneficial outcomes for researchers, their output,
and society at large. We will consider thesequestions in detail in the second phase of thisresearch project.

Schwab, 2016

4

INTRODUCTION
There is a growing consensus that we are at thestart of a 4th industrial revolution, a period of
rapid technological change that will mean suddenchanges in society and the economy. This will
include a transformational impact on the world of
research. Some technologies predicted to be partof the revolution include the Internet of Things, 3-D
printing, nanotechnology, biotechnology, 5G, newforms of energy storage and quantum computing.2

reasoning could be performed systematically in thesame manner as solving a system of equations. At
the same time, Charles Babbage was drawing up
designs for the first general-purpose computer, theAnalytical Engine, and Ada Lovelace was designing
the first computer algorithm.
By the 1940s, Alan Turing set out a formal modelof computing, even imagining the possibilities
of simulating intelligence with computers. In the
1950s, these ideas were brought together to build
the first electronic computers and primitive robots.
The first use of the term ‘artificial intelligence’ isoften credited to John McCarthy in 1956, with theDartmouth Summer Research Project on Artificial
Intelligence.

One class of 4th industrial revolution technologies
that stands out as likely to be particularly impactfulis that of artificial intelligence, and deep machinelearning in particular, due to their position as a
general purpose technology.3 A general purpose
technology is one which can be applied across
many sectors, can enable other technologies andwhich is rapidly improving. Previous industrial
revolutions have had their own general purpose
technologies, e.g. electricity or the internal
combustion engine in the 2nd, which havebeen one of the main reasons why such periods
constitute revolutions rather than evolutions.

From the 1950s to 1970s, the foundations werelaid for many tools and techniques to be used inresearch, which we will explore later in this report.
These include: using computer vision to recognise
individual letters; natural language processing, a
way of processing language as actually used rather
than set commands; and artificial neural networks,
which attempt to replicate the structure of thebrain. However, by the 1980s, these had still nottranslated into practical applications and interest in
artificial intelligence waned.

Artificial intelligence is, broadly, a set ofcomputational technologies that aim to sense,
learn and reason about their physical or virtualenvironment, and take action based on that. While
the rate of progress in artificial intelligence has
been erratic, there have been significant advances
since the Second World War and particularly overthe last decade. These advances mean that manyindustries, including research, need to understandthe potential impact this technology will have on
how they operate.

The latest wave of artificial intelligencedevelopment began in the late 1990s. The
beginning is perhaps best marked by IBM’s DeepBlue becoming the first computer to beat areigning world chess champion, Garry Kasparov,in 1997.5 IBM yet again took the limelight in 2011,
winning the quiz show ‘Jeopardy!’ with its question
answering system, Watson. These feats wereachieved thanks to improvements in computing
hardware and a significant amount of engineering
manpower on the particular problems.

Stanford University’s One Hundred Year Study on
Artificial Intelligence outlines a brief history of how
we got to the artificial intelligence of today.4 In the
18th century, Thomas Bayes provided a framework
for reasoning about the probability of events, andin the 19th, George Boole showed that logical2
3
4
5

Today, artificial intelligence is an increasingly

Schwab, 2016
Cockburn, Henderson, & Stern, 2018, p. 14
Stone et al., 2016
Ray, 20185

influential part of our daily lives and is developing
at a rapid pace, largely due to the revival ofmachine learning and neural networks. This is due
to an interlocking trifecta of ubiquitous connectivity
and development of data-sharing infrastructure;
increases in access to computational power via thecreation of GPUs and cloud-based computational
power6; increasing access to large-scale data
through the creation of massive labelled data
sets, typified by ImageNet7, and cloud-based data
storage. These approaches, pioneered in the 80s,
were only in the 2010s able to start surpassing
human performance in certain tasks and be
deployed at scale.

was significant enough that OpenAI decided
to break with norms in artificial intelligencedevelopment and limit the model’s release due
to security and safety concerns.13
Experts in artificial intelligence, when surveyed,
believe that development has sped up over thecourse of their careers and gave a 50% chance of
unaided machines being able to accomplish every
task better and more cheaply than humans within
45 years and a 10% chance of it occurring withinnine years.14
There are many scenarios envisaging what a futurewith increasingly capable artificial intelligence
will look like. The more conservative predictions
envision a world where vast swathes of modern
work, from truck drivers to lawyers, are replaced bynarrow machine learning systems, each individually
tuned to vastly outperform humans at a specificproblem.15 More outlandish projections, like thosemade Robin Hanson, speculate about a world
where whole-brain emulations, virtual cell-bycell copies of the human mind, vastly outnumber
the biological and dominate the economy.16
Nick Bostrom outlines the potential for a worlddominating Superintelligence that goes from nonexistent to exceeding humanity in every domain inmere weeks.17

Some applications in consumer products have beenpart of our lives for years. Face filters on Snapchat
and Instagram already rely on machine learningmodels.8 Since 2016, Google Translate has beenusing neural networks to improve translation toand from English and live-translate text.9 Facebook
uses facial recognition models to suggest who totag in photos.10 The last year alone has seen many
significant developments in artificial intelligence
research:
•

•

•

6
7
8
9
10
11
12
13
14
15
16
17

Google has developed a system called Duplex
which can engage in real-time natural language
conversations using a synthesised voice nearly
indistinguishable from a human. Currently,
Duplex is being used to make reservations andscreen calls, but it has obvious wider potential,from replacing call centres to offering medical
advice.11

Whatever you may think of the plausibility of any
particular scenario, all of them suggest that thedevelopment of artificial intelligence will lead to aradical upheaval across society, from the way wework, to how we interact, to how decisions about
our health, education, politics, and more are made.

Deepmind, now famous for AlphaGo, usedneural networks to take the top spot in theinternational protein folding competition. Being
able to predict protein folding more accurately
will improve our ability to diagnose anddevelop novel treatments for diseases believed
to be caused by misfolded proteins, such as
Alzheimer’s or cystic fibrosis.12

We expect that artificial intelligence will alsotransform the world of research. Some of thistransformation will come through directly
augmenting the ability of researchers, increasing
the speed, scale and complexity of analysis they
can undertake, by allowing the classification andanalysis of large sets of unstructured data or
automating elements of an experiment. However,also significant is how artificial intelligence may
automate much of the routine and administrative
work of research, freeing up researchers to focus
more on creativity, or improving networking and

OpenAI unveiled a predictive text generation
model able to output paragraphs of coherent
and relevant text from a single sentence
prompt. This model’s potential to generatelarge-scale misinformation or abusive content
Mayo, Punchihewa, Emile, & Morrison, 2018
Gershgorn, 2017Le, 2018
Turovsky, 2016
Simonite, 2017
Leviathan & Matias, 2018
Evans et al., 2018
Wu, Amodei, Amodei, Clark, & Brundage, 2019
Grace, Salvatier, Dafoe, Zhang, & Evans, 2017
Frey & Osborne, 2017; Susskind & Susskind, 2017
Hanson, 2016
Bostrom, 20166

recommendations so that researchers can find theirperfect collaborators much more easily. Eventually,
we might even see some research staff completely
displaced by artificial intelligence and needing tofind new roles in the research ecosystem.
This report considers how the speed and scalethat artificial intelligence and robotics offer is likely
to reshape our approach to research, primarily
through a comprehensive review of the relevant
academic and industry literature. The second
phase of this research project will then consider
how best to forecast the impact of the convergenceof 4th industrial revolution technologies, including
artificial intelligence, on research and what policy
interventions may be needed to ensure the mostbeneficial outcomes for researchers and wider
society.

7

SECTION 1 TOOLSAND TECHNIQUES
Researchers have developed a range of AI methods
to augment their research practices. We look at
the most prevalent systems and their practicalapplications across many fields, from biomedicine
to astronomy. AI tools and techniques are shownto enable researchers to conduct research moreaccurately and efficiently.
This chapter provides an overview of the following
tools and techniques:•

Natural Language Processing•

Image Classification•

Object detection•

Automated data monitoring•

Automated experiment selection•

Simulation and prediction of physical systems•

Parallelisation•

Content generation

Further examples of uses for these tools andtechnologies can be found in the appendix of thisreport.

However, recent years have seen advancements
in natural language processing which is enabling
researchers to understand and synthesise at scale
research not necessarily written in formal language.
Researchers at the Lawrence Berkeley National
Laboratory are using the Word2Vec algorithm to siftthrough scientific papers for connections humans
have missed in finding potential thermoelectricmaterials. After collecting a vocabulary of 500,000
words from millions of abstracts relating to materialscience, the abstracts were fed to the algorithm,
which uses machine learning to analyse therelationships between words. The algorithm canthus link words together and define concepts.
It has been able to predict an effective material
by scanning scientific papers from years before
it was discovered. The tool can be retrained on
the literature of any subject and easily be madeapplicable to other disciplines.19
Researchers at MIT have similarly developed
an NLP tool that can read scientific papers andproduce a short summary in plain English.20 This canbe used by researchers to scan a large number of
papers and get an understanding of what they say.
The developers’ approach also has the potential
to be applied to machine translation and speech
recognition.

NATURAL LANGUAGE PROCESSING
Natural Language Processing (NLP) is theprocessing and analysis of unstructured language
data, essentially enabling computers to understandhuman language.
A significant amount of scientific knowledge isincreasingly stored in structured databases, e.g.
molecular structures, properties of materials.
However, many theoretical insights and the
majority of the work in the arts and humanities
are still stored in book and journal articles,
comprised of natural language. In 2009, King et
18
19
20

al.18 observed that computers were changing theway that research was described and reported.
Developments such as the Semantic Web andcontrolled ontologies were enabling the progressfrom the use of natural language to more formal
language.

However, NLP is not confined to the sciences- researchers in the arts and humanities are
increasingly making use of NLP tools to advancetheir inquiries. Researchers at Lancaster University
have used Tagtog, an AI platform that utilises NLP
and machine learning, to annotate and extract

R. D. King et al., 2009
Tshitoyan et al., 2019
Dangovski, Jing, Nakov, Tatalović, & Soljačić, 20198

information from historical documents that relate toearly colonial Mexico.21 A series of questionnaires
conducted in the late 16th century, known as ‘The
Relaciones Geográficas’, are the primary source
materials for the project. The documents run to
thousands of pages, containing rich historicalinformation.

of research findings. Second, it could significantly
assist those fields in the arts, humanities and social
sciences for which translation is often required; forexample, historians examining primary sources in adifferent language could quickly and cheaply accessa translation.
COMPUTER VISION

These documents are extremely complex tounderstand, partly because they are written in
Spanish and a number of indigenous languages.
Previously it would take scholars years, perhaps
decades, to fully understand just a small section.
However, the use of computational techniques suchas NLP can allow for much quicker analysis, creating
the potential for previously unimaginable research
opportunities.

Computer vision refers to computers’ ability to ‘see’
and understand digital images. It is used to classify
images into different categories or to detect andidentify objects in an image.
Image Classification
One of the main challenges for image classificationis the presence of technical noise that interfereswith the desired data in images. Recursion
Pharmaceuticals25 uses machine learning toobtain better data for classification. The RxRx1
dataset shows different levels of technical noise
between over 50 batches of microscopy images.
By comparing the batches, machines learn todistinguish the noise from the underlying biology.
Machines are then able to disentangle the technicalnoise from the biological data so that the images
can be accurately classified. Recursion thus expects
AI to enhance our understanding of how drugs
interact with human cells and is using machinelearning on biological datasets to accelerate drug
discovery. Machine disentanglement of batch
effects is applicable across the field of biology.

NLP is also driving further advances in the field of
linguistics. Professor David Bamman, a scholar ofComputational Humanities and Social Sciences at
UC Berkeley, uses NLP tools to identify sarcasm
on Twitter.22 This allows researchers to understandmore about what enables sarcasm in conversation,in particular, the importance of interpersonal
characteristics.
However, these developments are not limited
to academia: some of the most significant
developments are to be found in the commercial
sector. Uber, for example, has developed the Plato
Research Dialogue System. A platform for building,training and deploying conversational AI agents. It
acts as a testbed - for Uber and the wider researchcommunity - to evaluate new algorithms and quickly
prototype and deploy conversational agents,
spurring further research advances in this field.23
The platform has been built to be accessible byboth those with a limited technical background andmore advanced researchers.
Facebook AI has achieved a number ofbreakthroughs in NLP using both supervised andsemi-supervised learning techniques that assist
with translation. For example, Facebook’s RoBERTa
recently outperformed human baselines in several
cases, including English-German translation.24

In a Science magazine survey of young scientists
reflecting on the use of AI in research, Noah F.
Greenwald from the Dana-Farber Cancer Instituteagrees that a key difficulty in large data sets is
separating signal from the noise. In cataloguingthe mutations present in a group of cancer patient
samples, a huge amount of data is produced, withthe goal of identifying the mutations that play animportant role in tumour biology. The signal fromthe mutations must be accurately interpreted.
Machine learning identifies patterns within the data
to systematically remove the noise and achieve
more accurate findings.26

Easily accessible and effective translation tools
could significantly enhance the conduct of research.
First, it could allow researchers to access papers not
published in their native language, boosting thescope for wider sharing and quicker dissemination

Again, it is important to note that many of thedevelopments relating to image classification
have been driven by commercial actors. Google,
for example, has developed many of the leading
image classification models and is now using these

21
22
23
24
25
26

Bellamy, 2019
Berkeley School of Information, 2015
Papangelis, Wang, Namazifar, & Khatri, 2019
Ott et al., 2019
RXRX, 2019
Musib et al., 20179

technologies to improve our understanding of thebrain.

use of high-resolution satellite images with machinelearning to detect and count seal populations in the
Antarctic.

The structure of networks in the brain remains
largely unknown due to difficulties in imaging andsubsequent 3D reconstruction. However, Google
is using its technologies to address this: the brain
is imaged in extremely high resolution with amicroscope before several image analysis problems
are solved to produce a diagram of the imaged
brain.27

Xubin Pan from the Chinese Academy of Inspection
and Quarantine31 notes that quick and accurate
species identification is important in ecology andecosystem conservation, but most people do notknow enough about species morphology to achievethis. With support from AI, a computer or mobile
device can be used to accurately identify a species
and to discover invasive or endangered species
in real-time. Deepmind32 has developed such atool using the Snapshot Serengeti dataset, whichfeatures millions of photos of around 50 animal
species. This trains machine learning models toautomatically detect, identify and count animals to
help with animal conservation in the Serengeti.

Object detection
Machine learning is widely used in astronomy
to help researchers handle huge data flows.
Telescopes can scan millions of stars and generate
too much data for astronomers to handle
themselves. The next generation of telescopes28
will use AI to automatically detect objects in theirown images. Advances in image recognition andfaster, less expensive computing power have madealgorithms useful to more researchers. AI canalso be used to coordinate telescopes, allowing
hundreds of telescopes around the world to work
together when detecting transient events. It can
extract and analyze data much faster and more
precisely and can treat data with more consistency
than humans are able to.

AUTOMATED DATA MONITORINGThe Internet of Things (IoT) is the networkof virtually connected devices, including lab
equipment and sensors. It can quickly provide
solutions to problems that researchers would
otherwise need to address by directly monitoring
experiments for an extended period of time.
For example, IoT devices were used to find out why
a high performance liquid chromatography machineat a Sunovion Pharmaceuticals33 lab was giving
erratic readings for a drug analysis. The researchers
placed a temperature sensor next to the machine
which connected to the cloud, and an electronic lab
notebook recorded the temperature readings overa few days. This revealed that the building’s climate
control system had been blowing hot and cold air
at the machine at a specific time of day, and theresearchers were able to fix the problem. The IoTmodel can also be used to automatically deposit
large amounts of data from devices into the cloud,and to control experiments remotely.

The Palomar Transient Factory (PTF)29 is a telescopeused for time-domain surveys to study how
astronomical objects change over time. PTF detects
intriguing objects and feeds the information toanother dedicated telescope to follow up. It isdifficult to maintain human researchers at every
hour of the day to monitor the sky for intriguing
events, so PTF uses machine learning to identify
objects and trigger a follow up in the same night.
In the Science magazine survey, Feng Wang fromthe Chinese Academy of Forestry states thatmachine learning and unmanned aerial vehicles
are revolutionising environment monitoring.
Investigating wildlife and vegetation status would
previously require extensive surveys of an area.
Now, aerial vehicles can capture high-resolution
images of hard-to-reach places, and quickly findvegetation types, area and wildlife count, andactivity. These results have proven to be more
precise than the estimates made with the traditional
method. An example is the Alan Turing Institute’s30
27
28
29
30
31
32
33
34

Jeffrey M Perkel’s article34 focuses on the IoT’s rolein monitoring freezers, ensuring that a lab’s precious
research samples can be looked after remotely. Thiscan give researchers peace of mind and restore
their work-life balance. An issue with the IoT may
be that its automated data gathering overlooks
nuances between disciplines of how data should becollected. There are also security and privacy issues
to consider, as cloud provider companies such as

Google AI, 2019
Snyder, 2018
Musib et al., 2017
The Alan Turing Institute & The Royal Society, 2019
Musib et al., 2017
Jean-baptiste Alayrac et al., 2019
Olena, 2018
Perkel, 201710

Google and Amazon could have access to the vast
amounts of research data stored in the cloud.

SIMULATION AND PREDICTION OF PHYSICALSYSTEMSOne of Harrer et al.’s35 main causes of drug trial
failure is the inability to monitor patients effectively
during trials. In order to be monitored, patientsare required to keep a record of their medication
intake and bodily responses themselves. This islaborious and can often result in patients droppingout of a trial. To solve this, wearable sensors andvideo technology can be used along with machinelearning and deep learning to record patient data
in real time and automate the monitoring process.
Through this, AI has the potential to increase trial
success rates, reducing drug development costs
and putting more drugs on the market.

AI models can be trained to make data predictions
based on the patterns and sequences found intheir data input. This can be used to simulate andpredict the structure and properties of new physicalsystems.
Predictive models have proven to be very useful ingenomic research. Moritz Gerstung’s team38 at theEuropean Bioinformatics Institute uses algorithms
to find the best treatment for leukaemia patients.
The full bone-marrow transplant treatment has ahigh risk of complications and should only be given
to patients with the deadliest forms of the disease.
Gene-sequencing algorithms are therefore used to
predict the severity of different forms of leukaemia
by comparing DNA variants and their outcomesin patients. Doctors are then able to give bettertargeted treatments. In general, predicting a drug’s
effect on cancer cells indicates whether individual
patients will benefit from it. Gene-sequencing
also highlights the genetic similarities between
cancers in different sites (e.g. the breast cancer
gene BRCA1 has been found in the prostate) andgives suggestions for how a drug that works for
one cancer site may work for another. Drugs will be
increasingly focused on the genetics of a cancer
rather than its site.

AUTOMATED EXPERIMENT SELECTION
Previously, the time and cost involved in carryingout an experiment meant that researchers had tobe fairly confident they would get a useful result
before dedicating the resources. Machines canuse their data input to select the most promisingexperiments to perform for optimum productivity
and cost efficiency.
Naik et al.36 have utilised the ‘active learning’
method of machine learning in drug screening
experiments. Not all experimentation options canbe carried out due to cost and time restraints,
raising the problem of which experiments tochoose. In the active learning method, a computer
selects which experiments to perform using a
predictive model based on simulations of theexperiments. Naik et al. found that machineslearned to give accurate predictions more rapidly
when the machine selected experiments using its
own algorithm. Using active learners thus enables
researchers to carry out more useful experiments
and make accurate predictions in less time.

Biologists interested in determining protein
shapes are increasingly turning to AI methods.
Deepmind’s39 AlphaFold is a deep learning
approach that focuses on the problem of modellingshapes from scratch. They have achieved a highdegree of accuracy in predicting the physicalproperties of a protein. The ability to predict theshape of a protein is fundamental to understanding
its role in the body and is also useful for diagnosing
and treating diseases such as Alzheimer’s,
Parkinson’s, Huntington’s and cystic fibrosis, which
are thought to be caused by misfolded proteins.
An understanding of protein folding may also help
in protein design, for example for biodegradable
enzymes which help reduce pollutants like plastic
and oil. Acquiring more knowledge about theshapes of proteins through simulations and models
also opens up new possibilities for drug discovery
while reducing the costs of experimentation.

The Alan Turing Institute37 also aims to decrease
research costs and improve outcomes by using
existing datasets to choose which experiments
to perform. New techniques will be applied toplant science, researching how plants integrate
environmental signals to determine how much to
grow. This might help to predict how plants may
respond to climate change.

The Tokyo Institute of Technology40 in aiming to
35
36
37
38
39
40

Harrer, Shah, Antony, & Hu, 2019
Naik, Kangas, Sullivan, & Murphy, 2016
Ezer, 2018
Chivers, 2018
AlphaFold, 2018
Wu, et al., 201911

discover polymers with high thermal conductivity,
have utilised the method of ‘transfer learning’,
through which machines are able to discovermaterials with desired properties even from avery small data set. The study used data fromPoLyInfo, which despite being the largest databaseof polymers in the world, still has limited data on
the heat transfer properties of polymers. Machinelearning models were pre-trained on proxy
properties with sufficient data and learnt features
applicable to the target task. Machines then used
the learnt features with the small datasets and wereable to make accurate predictions. The technique
led to the identification of thousands of promising
polymers, representing a breakthrough for fastand cost-effective machine learning methods formaterial design.

we can learn a lot from huge batches of data.
CONTENT GENERATION
Thus far we have largely focused on how newtechnologies and tools can assist researchers withanalysis, from recognising objects to analysing
ancient documents. But it is also useful to highlight
how AI can be used to generate new content itself.
SciNote have developed AI-based software,
Manuscript Writer, that provides researchers witha first draft of a manuscript or paper, based on the
researcher’s own data.43 Manuscript Writer is able togenerate the Introduction, Materials and Methods,
Results and References sections of a manuscript;
the Discussion section is left for the researcher
to complete. SciNote argue that the programme
could significantly reduce the time needed toprepare a manuscript, allowing researchers to focus
more on analysing data and preparing for the next
experiment, not preparing manuscripts. However, it
is important to flag that concerns have been raisedaround plagiarism, claims which SciNOTE reject.44

In training a machine learning tool to predict
chemical properties, Google41 has similarly foundthat models are most useful when they are able togeneralise past the invariances in data. The teamhas therefore developed a model that is able tofocus on the underlying symmetries in images.

Google’s AI systems have proven their ability toboth enhance a low-quality image’s resolution andfill in gaps in that image.45 Google researchers
trained their system on low-resolution images ofcelebrities and bedrooms. The AI system works
across two fronts. First, the system compares the
low-resolution image to high-resolution images,
determining what is in the image. Then, researchers
use PixelCNN, an AI system, to add pixels to theimages based on what it knows from other images.
The results from both processes are then combined
to produce a final image.

PARALLELISATION
Parallel computing processes data simultaneously,
so that many problems can be solved at the sametime. It can divide large problems into smaller
problems for simultaneous processing. The trainingof AI systems can be limited by their ability toprocess a large data input, but parallelisation allows
the system to process and learn from more data inless time.
Amodei, Kaplan and McCandish42 examine theinterference of technical noise when using AI
to process a large amount of data in parallel.
Researchers have increasingly been using data
parallelism to speed up neural network training,
splitting large batches of data across machines.
This is used for tasks such as image classificationand language modelling. Although the aim is toincrease efficiency, the paper argues that larger
batches of data are not always more useful if
there is significant technical noise. The gradient‘noise scale’ quantifies the signal-to-noise ratio
and predicts the largest useful batch size in a new
domain. When the noise scale is small (meaning
there is not much variation in the amount of noise
among data), looking at a lot of data in parallel
becomes redundant. When the noise scale is large,
41
42
43
44
45

Of course, it is important to note that the images
created are estimates by the machine - they
are not yet accurate portrayals. Therefore, theirusefulness for research purposes at present - forwhich accuracy is required - is likely to be limited.
However, as the model becomes increasingly
accurate, such tools may hold great promise forresearchers. For example, low-quality photos
from the past could be restored to higher quality,
assisting historians and other researchers analysing
them. It could also perhaps, one day, help art
historians restore damaged works of art.

Dahl, 2017
McCandlish, Kaplan, & Amodei, 2018
SciNote, 2017
McCook, 2017
Burgess, 201712

SECTION 2 BROADEREFFECTS OFTECHNOLOGY ON THE
RESEARCH PROCESSTechnological advances are not only changing
the way that research is conducted but also howdata is captured, shared and evaluated. AI has
the potential to augment the research publication
process and promote collaboration.

The research organisation Nesta47 is part of thistrend, promoting open data sharing to maximise
the public benefit of research. The Innovation
Mapping team believes that in order to create asustainable research system that produces realsocietal benefits, we need to make use of new data
sources in measuring the progress of research.
They are developing Rhodonite, a set of network
science algorithms that measures combinatorialchanges in research topics over time. Nesta aims
to join this tool with other research and innovation
indicators in an open source software package,
which academics could use to compare researchmethods and results.

OPEN ACCESS AND DATA-SHARINGAn Elsevier46 study claims that the ease of data
sharing means that open access platforms will thrivein the future. Funders will increasingly promote
open access research and push the creation of
OA platforms, meaning that that research outputs
are available freely, without restrictions like cost
barriers or copyright constraints. The study expects
journal publication to become less important indetermining the career progress of a researcher,and research will be assessed more against societal
impact standards.

RESEARCH FUNDING
AI-assisted technologies could help speed up thebid writing process for researchers. At present
these tasks can be extremely time-consumingwith significant administrative burdens, taking
researchers away from research. However, as
discussed in section one, innovations in natural
language processing mean machines are able toanalyse unstructured data, such as written content
for bids, and generate content itself.

Elsevier’s forecast is for research to increasingly
involve broad, international collaboration.
Feedback and content will be easy to share online.
A challenge is sharing information in a way thateveryone involved can understand, as collaborators
may come from a wide variety of disciplines. A
condition for funding may be publishing findings
immediately on an open platform, with a lot ofwork going into preparing the content for open
publication. Another requirement may be making
findings accessible to everyone; scientific research
will have to be digestible for a non-scientificaudience.
46
47
48

The process of bid evaluation could also be
substantially sped up. Software can be used toautomatically filter out applications that do not haveessential criteria or are unfinished.48 This can leave
more time for tasks that are harder to automate,such as the qualitative review of bids.

Mulligan & Herbert, 2019
Richardson, 2019
Keriann Strickland, 201813

WizeHive, for example, automates backend administrative burdens for grant fundingorganisations, including the collection, review, andselection of applications.49 Other software systems,
such as Instrumentl, automate the search process
for grant seeking organisations by automaticallymatching organisations with grant opportunities,
significantly reducing the amount of time
researchers have to spend searching for funding.50

will ever be entirely automated: the process relies
on and is arguably at its best when drawing on
human expertise and creativity. Despite this, somesee the automation of the peer review process as athreat to the integrity of the research.55
SPARC (Scholarly Publishing and Academic
Resources Coalition) - an organisation that works
towards an open access model for academic
publishing - have raised concerns about thepotential impact of automation on the journal
market. Primarily, they are concerned that journals
with greater financial resources may be in a better
position to invest in new technologies and radicallyincrease their competitive advantage, attracting thehighest calibre academics and, in turn, dominating
the market.56 Tools or functions such as search andaccess, streaming and accelerating the editing
and peer review, personal recommendations andsharing tools could be invested in and developedby publishers to ensure they have a competitive
advantage over cheaper or free journals.57

These technologies could lead to a radicallydifferent funding ecosystem. AI-assisted technology
could both write and review bids, substantiallyreducing the level of human input required.
PEER REVIEW
Today, humans are at the centre of the peer review
system for academic papers. It is useful to consider
how the opportunities presented by the 4th
industrial revolution could make this system more
effective.
The current review process can be incredibly timeconsuming. However, an automated system that
reviews data standards and other methodologicallylaborious elements of the review process could free
up time for other more qualitative tasks, such as
ensuring the research sits in the broader context.51

AUTOMATED ACADEMIC OVERSIGHT
These changes would undoubtedly have wider
implications for research. The outcomes would
depend on how the artificial intelligence is trained,
and what it learns from previous data. There isa dangerous potential for misaligned incentives
occurring, for example, if a reinforcement learning
system’s goal is to get published rather than to
be correct, for grant funding this could lead toacademics focusing on easy research areas thathave a shorter time frame, lower running costs
or less risk. For peer-review, risks could include
falsifying data that is difficult to detect by current
statistical tests and ways of gaming the system toget published.

For example, Elsevier uses AI system StatReviewer
to ensure the statistical analysis and methodologicalunderpinnings of a paper are sound.52 Automation
could also save time by ensuring the expertise of
researchers is best matched with particular papers:
another time-consuming task for researchers.
Existing technology can often only detect exact
matches in text, reducing the technology’s ability
to spot all instances of plagiarism. However, AI
language processing is able to detect wholesentences and paragraphs that sound similar. AI
can also detect incomplete reporting, inappropriate
statistics and data fabrication. Automating the
publishing process can allow researchers to share
their findings much quicker.53

At a macro-level, if poorly executed, AI could
degrade the diversity and quality of publishedresearch. There is the possibility of an AI arms race,
where organisations with investment, skills andresources battle to produce the ‘best AI’. Further,the best AI would not necessarily be the one that
produced the highest quality and most valuable
research, but the one that ensured academics were
published or made the research easiest to conduct.

Nonetheless, it should be noted that theseapplications do not come without threat. Indeed,some have questioned whether one day we could
have a “peer-less” review.54 It should be noted,
however, it is unlikely that the peer review process
49
50
51
52
53
54
55
56
57

WizeHive, 2019
Andre, 2018
Chen, 2019
Heaven, 2018
Burley et al., 2017
Shanahan, 2016
Times Higher Education, 2016
Aspesi et al., 2019, p. 20
SPARC, 201814

SECTION 3
REPLACEMENT OF THE
RESEARCHER
AUGMENTATION VERSUS SUBSTITUTION

researchers, possessing ‘superhuman’ reasoning
abilities. The robot allows faster scientific discovery
by running thousands of hypotheses in parallel,
cheaper experimentation by selecting economicallyefficient experiments, and improved data sharing
and reproducibility. Robots are also more easily
produced and trained than human researchersand can work constantly without requiring rest.
King views the increased productivity of scientificresearch as AI’s main economic importance;
research is the biggest driver of economic growth,and faster growth is achieved by more productive
research.

The automation of research tasks using AI has
vastly improved productivity in scientific research,with research being carried out faster and moreaccurately than ever before. Further advancements
in AI see machines emulating human decisionmaking abilities, calling into question whether
researchers themselves will be replaced bymachines.
Heard’s article58 explains how AI can create ‘afully robotised scientist’ as effective as a humanresearcher. The mechanisation of repetitive tasks
has historically allowed for greater productivity andreliability through the removal of human error, and
it is increasingly useful to automate the scientificresearch process to cope with large data problems.
The extent to which experiments can be automatedhas been constrained by the necessity for humanintervention at the decision-making level when afuture strategy must be defined. Yet, the use of
AI can allow a ‘robot scientist’ to emulate humandecision-making abilities. Machine learning cananalyse outputs from previous experiments andthus decide inputs for the following experiments.
In King et al.’s experiment, the robot scientist‘Adam’ achieved comparable predictive accuracy
with a group of undergraduate students acting
as decision-maker. It outperformed the naive
strategies.59 The combination of biologicallaboratory machinery and analytic automation isshown to produce a machine with the ability tocarry out experiments like a human researcher.

In researching biological systems, the complexityof the systems necessitates the recordingof experimental metadata in as much detailas possible. King et al.61 document that inexperiments carried out by their robot scientist,
metadata is produced as a natural by-product ofexperimentation. As all experiments are executed
automatically by the computer, it is possible tocapture and record all parts of the process.
The robot scientist ‘Eve’62 at the University ofCambridge has aided the discovery of a new
anti-malarial drug. Researchers had previously
noted that triclosan, a common antibacterial
agent, inhibits the growth of the malaria parasite
during the blood stage. Eve was then used in ahigh throughput experiment, finding that triclosan
inhibits the malaria parasite enzyme Plasmodium,and may be able to target malaria at both theblood and the earlier liver stage of the disease. Eve
was developed to speed up the drug discovery
process by automatically developing and testing

King60 in fact presents the robot scientist AI system
as more effective and productive than human
58
59
60
61
62

Heard, 2004
R. King, 2018
R. King, 2018
R. D. King et al., 2009
Bilsland et al., 2018; University of Cambridge, 201815

hypotheses to explain observations.

question of efficiency, and as AI drastically improves
efficiency, asking the right questions becomes
irrelevant.

Reproducibility is key for the credibility andusefulness of scientific research, yet a 2016 surveyof 1500 researchers revealed that 70% could not
reproduce their colleagues’ findings. Commercial
contract research organisations (CROs) such
as Transcriptic and Emerald Cloud Lab offer asolution with robotic labs. Robotic labs contain andautomate the entire experimental process, withrobot scientists remotely controlled by researchers
to carry out all tasks. Up to 89% of biomedical
papers use methods supported by biomedical labs,
particularly for basic research.63

AI has the potential to achieve breakthroughs
simply through the ‘brute-force approach’ ofgenerating and verifying as many hypotheses as
possible. Automated experiment selection systems
such as Naik et al.’s ‘active learning’ method showthat AI can close the experimentation loop and isable to make decisions on the course of research.
It need not make discoveries in the same waythat human researchers would. Kitano calls for ascientific revolution on the scale of the industrial
revolution and predicts that AI will have major
implications for medicine and human lives.65

These labs have the potential to increase
consistency in experimentation. Robotic arms’
capacity for manual dexterity and fine motor
control is improving and the arms can performmovements much faster and in exactly the sameway each time, thus reducing potential variation
between trials in an experiment. They could alsooffer more detailed logs of exactly how each stepof the experiment was carried out. Combined,
these could improve transparency and reduce
issues of quality control and thus have the potential
to improve reproducibility. However, if theexperimental robotics are controlled by machinelearning systems, they could become even moreclosed black boxes, as we currently lack the ability
to interrogate the reasoning or causal links behindcurrent machine learning.

A 2016 Stanford University study asserts that themeasure of success for AI is the value created forhuman lives, so AI will only be successful if humans
are able to use and adapt to the technology.66
Hence the advancement of AI is more likely
to occur in gradual developments rather thanunexpected jumps in techniques.

Robotics also offers the ability to undertake
experiments that humans’ biological limits prevent
us from currently undertaking. With sufficientprecision and miniaturisation, they could help
undertake nano-scale experiments. Further, if theserobots can be taken out of the lab and either be
remote controlled or embodied with sufficientdecision-making capabilities, they could offer the
possibility to undertake research in places humans
can’t go, e.g. at the bottom of the ocean, in highlevels of radiation or in deep space.
Kitano64 has high expectations for the future of AI,
predicting major discoveries in biomedicine thatare ‘worthy of a Nobel Prize’. Many researchers
question whether AI has the ‘intuition’ to ask theright questions leading to major discoveries. But
Kitano points out that asking the ‘right questions’is considered important because of limitations on
resources, as experiments are subject to budgets
and time restrictions. It is therefore, in fact, a
63
64
65
66

Groth & Cox, 2017
Kitano, 2016
Naik et al., 2016
Stone et al., 201616

SECTION 4
CHALLENGES
CHALLENGES POSED BY THE ADVANCEMENTOF AUTOMATED RESEARCH

a woman that appears healthy to doctors. In thisinstance, even if the computer’s predictions areshown to be accurate, doctors must decide whatcourse to take without understanding exactly why
the woman is at risk.

In a 2018 survey of AI researchers, 50% forecasted
that high-level machine intelligence (HLMI) would
be achieved within 45 years. HLMI is achieved whenmachines can accomplish every task better andmore cheaply than human workers. HLMI has thepotential to spark explosive technological progress,
with AI systems quickly becoming vastly superior tohumans in all tasks. The survey also acknowledges
the risks associated with this; researchers gave a
15% probability to a bad or extremely bad outcome(e.g. ‘human extinction’).67

De Ridder questions the concern that the
automation of research roles will make researchers‘mere servants to the robotic overlords’.71 He
concludes that the increased speed and scope ofexperimentation will, in fact give researchers moretime and resources, allowing them to have more
freedom and creativity in their ideas.
Wider data sharing has been noted as a benefitof AI in research. However, a paper by The Royal
Society questions the viability of ‘open data’, whenin practice modern data sets are often too large
and complex for people to access or understand.72
As the size of data sets grows, there will be few
researchers able to download them. The amount ofdata used in and produced by AI research requires
a physical infrastructure with sufficient computing
power. AI may therefore not be the best solution toaccessibility issues in research.

Deep-learning algorithms allow humans totake a step back in analysing information, as
computers are given the task of finding meaningful
relationships in the data. Sarah Webb notes that as
researchers become more distant from the analysis,
they lose control of the classification process
and may not be able to explain exactly what analgorithm is doing; the systems are black-boxed.68
While an algorithm is able to make outstanding
predictions, it is still a challenge for the programmer
to understand how it came to these predictions.
This echoes Chomsky’s 2011 critique of machinelearning methods: approximating unanalysed data
does not constitute scientific success, as while we
may be able to predict a phenomenon, this does
not necessarily mean we understand it.69

CHALLENGES TO THE ADVANCEMENT OF
AUTOMATED RESEARCH
The significant challenge for any AI model is thatthe results given by algorithms are only as good as
the quality of the data input. Particularly for deeplearning, a very high quantity and quality of data isrequired. While automation is often seen to remove
human bias and preconceptions from research,
the training data for algorithms can be skewed, forexample, if only data from northern Europeans isused in a genomic study. This creates embedded
biases in the model, which will be reflected in
predictions. Casey Greene, computational biologist

Davide Castelvecchi discusses the black box
problem further.70 Computers can pick up on
trends and information that humans are unable tosee, and programmers must do the equivalent ofneuroscience to understand how their AI models
have reached their answers. In the instance of deeplearning being used to identify breast cancer inmammograms, a machine may detect cancer in
67
68
69
70
71
72

Grace et al., 2017
Webb, 2018
Ridder, 2018
Castelvecchi, 2016
Ridder, 2018
The Alan Turing Institute & The Royal Society, 201917

at the University of Pennsylvania, suggests thatcomputers should therefore not be allowed tomake decisions without humans validating theirpredictions. He states that “thinking of thesemethods as a way to augment humans is better
than thinking of these methods as replacing
humans”.73

However, Ryan Carey comments in the AI Impacts
blog that this trend cannot continue for more thananother 10 years due to cost.76 Unless the cost ofcompute drastically decreases, experiments willgrow too large to be affordable by anyone but the
US or Chinese governments. Moore’s law, whichstates that the processing power of computers
doubles every two years, has become difficult toachieve. The size of transistors in computer chips
must continuously decrease so that more of themcan be used, but Intel, the world’s largest supplierof computer chips, expected in 2016 that transistors
could only shrink for another five years.77

The progress of automation in social science fields
still faces many barriers, for example, the lackof standardisation in research. Social scientists
have historically not focused on standardising
their findings, instead representing their data
differently from study to study, creating difficulties
for automated data discovery. Yarkoni et al. suggest
establishing field-wide conventions for the basicorganisation of data, following the example of theInter-university Consortium for Political and Social
Science, which requires data to be curated tocertain standards.74 Terms are rarely clearly definedin social sciences in contrast to natural sciences,posing a challenge for language processing tools.
The use of controlled vocabularies and ontologies
may solve this.

Bloom et al. show that this has already put research
productivity in decline.78 Research efforts are
increasing across industries, as the number of
researchers now required to double the densityof computer chips has increased by a factor of 18
since the conception of Moore’s law in 1965. The
research productivity rate should have increased bya similar factor yet Bloom et al. calculate that it hasremained stable. With larger research efforts lessable to achieve higher levels of productivity, there isless potential for new ideas. This can be expected
to impact economic growth, which is reliant on thecreation of ideas.

Yarkoni et al. view the impact of automation in
research as uneven. Natural sciences have been
the most rapid adopters of automation techniques,as their highly-structured data and more formal
specification lend themselves more easily to thecurrently available tools. The social sciences andhumanities have adopted automation to a lessdegree, as techniques developed for naturalsciences do not necessarily easily adapt toqualitatively different kinds of investigation.

Ben Garfinkel offers an alternate perspective,
suggesting that we have so far been
underestimating the amount of computing power
being used.79 This would mean that we have been
overestimating the returns it gives us. So, thegrowth of compute may therefore not contribute asmuch to the progress of AI as anticipated.

Finally, a key problem for the future of AI is theconstraints on computing power. Amodei and
Hernandez find that the main factor determining
how much power an AI model has correlates to theamount of computing power used to train it, the“compute”.75 The total amount of compute is not
the same as compute per model because limits onparallelisation constrain how big a model can beor how effectively it can be trained. The amountof compute that can be effectively used at once totrain models has increased by a factor of 10 eachyear as researchers have found a way to use more
chips in parallel or use custom hardware that allows
more operations to be performed per second.
This has enabled researchers to use increasingly
powerful AI models for greater research tasks.
73
74
75
76
77
78
79

Webb, 2018
Yarkoni et al., 2019
Amodei & Hernandez, 2018
Carey, 2018
Simonite, 2016
Bloom, Jones, Van Reenen, & Webb, 2019
Garfinkel, 201818

NEXT STEPS
Clearly, these technologies are going to have atransformational impact on research, though exactly
what that transformation will look like and who will
benefit is still uncertain.

tools? Will traditional universities be overtaken
in research by large technology companies?
•

Going forward, we are going to examine what
the future of research might look like as thesetechnologies mature. We will explore potential
scenarios, try to forecast what is most likely, andthen explore what policy options could lead to themost beneficial outcomes for the field of researchand society more widely. Below are sets of open
questions we have across forecasting and policythat we will look to explore in the second phase ofthis research project.

POLICY QUESTIONS
Research Finance•

What alternative career paths are created in the
research lifecycle by the uptake of automatedresearch practices and how can they berecognised?

•Will academia be able to afford thecomputational power necessary to competewith technology companies on AI-enabledresearch? Should governments provide moreshared access to computational power?

FORECASTING•

What are the barriers to adoption of thesetechnologies in academia?

•

Will automation have a greater impact on juniorversus senior research staff? What effect will it
have on career trajectories in research?

••

What is the role of routine work in the creationof data sources, e.g. digitising documents orlabelling images, in the automation of research?
Will the greatest gains to academic productivity
come from the automation of administrative
tasks freeing up researchers to focus on their
work? What would be the impact of automating
grant writing?

•Will being less hands-on with experimentation
actually free up time for theorising? Or will
being disconnected from trial and error makeacademics less creative?

•

Research Excellence

How does the raw availability of physicalresources fit into the automation ofexperimentation in the natural sciences?
Is automated experimentation limited byphysical timeframes? Will the simulation andprediction of physical systems overtake tangible
experiments?

•

Will the blackboxing of AI experiments limit thedevelopment of human scientific knowledge?

•

How can AI-assisted peer review help maintainresearch standards?

•

Is the Research Excellence Framework (REF)
in its current form suitable for AI-generated
papers and research?

•

What risks does automated research pose tomaintaining excellent research standards?

•

Should research evaluation encourage or
discourage the use of AI to generate research
papers?

•What implications arise from using
automated processes in research planningand management? For example, if a funderuses AI-assisted technology to evaluate bids,
should researchers have a competing AI in this
scenario?

Knowledge Exchange between Academia,
Industry and Wider Society

Do universities have sufficient digital
infrastructure to utilise these new AI and robotic

19•

How can we ensure that the benefits ofcommercially undertaken research are fully felt
by wider society?

•How can we encourage greater collaboration

between academia and technology companies?
•

How can AI-assisted peer review reduce the lagtime between research and publication?

•

How can researchers be supported to movefreely between industry and academia, or
occupy both sectors simultaneously?

•

What are the key lessons academia can learn
from industry use of 4th industrial revolution
technologies?

•

such as the Research Data Alliance, be built on
to ensure the international sharing of expertiseand learning related to automated research?

Is there a role for automated research
determining the most significant fields ofresearch that can aid significant societal andglobal challenges?

Expertise and Capabilities Needed andDeveloped by Research Sector•

What structures, flows and funding instruments
need to be available to support automatedresearch? Is a different balance between capitaland current funding needed?

•

Is training young researchers in computing andcoding as well as natural sciences the mosteffective way to prepare them for an automatedfuture? In the short-term? In the long-term?

•

What are the major barriers to UK-based
research organisations being able to recruit
leading talent to undertake AI-related research?

•

How can we sufficiently invest in universities’
digital infrastructure to ensure they canundertake world-leading automated research?

•

What features must AI-based infrastructure haveto promote excellent research with integrity?

Research Integrity and Ethics•

Should we restrict or enable AI autonomous
decision making in academic contexts, such as
medical treatment?

•

Do we need stronger data protection rules
for the storage or sharing of human researchsubjects’ data in the cloud?

•How can we ensure that those working in fields
related to AI are sufficiently aware of the ethicaldilemmas and challenges they may face in theirwork?

Research Organisational Strategy and Culture•

How can the work of international organisations,

20•

Would establishing universal conventions
for recording and sharing data (in particularformats, with controlled ontologies, open
access etc) help to make data accessible tomore researchers, make results more useful or
improve reproducibility rates?

•

Is the invisible labour and work of technicians,
data labellers and other research support staff
being properly appreciated?

APPENDIX
NATURAL LANGUAGE PROCESSINGCOMPUTER VISION

Linguamatics has been developed as a data mining
tool using NLP.80 It is used to extract informationfrom databases such as medical records, which
typically contain unstructured text and numericalinformation. The language in medical records canbe nuanced; for example, information on patients’histories of smoking may be written as ”doesn’t
smoke” or ”quit cigarettes”. Numbers can be
expressed in different ways; 1kg may also be
written as one kilogram. Linguamatics normalises
concepts from structured and unstructured text
and can recognise different terms that represent
the same data. It has been particularly useful indrug development, as a search for certain terms
in databases can retrieve specific information to
help predict the success of drug trials. NLP allows
researchers to obtain existing natural language data
for their research that would otherwise be hard tofind.

According to Ken Dutton-Regester, Stanford
researchers have developed a deep learning tool
to help doctors distinguish between benign andcancerous tumours, which is able to classify skincancers as accurately as a panel of dermatologists.82
Automated classification has the potential
to improve the consistency and specificity of
categorisation. He suggests that smartphone
applications could be developed to use thisalgorithm and provide low-cost, universal access todiagnostic care.
The Alan Turing Institute is exploring the use of AI
to characterise materials from X-ray imaging. It is
common for materials to be analysed by having
X-ray beams guided through them, creating ascattering pattern that can show their internal
structure. Researchers are usually required to
examine the scattering patterns, but there is thepotential for machine learning models to analyse
the patterns and automatically determine thestructural information of the materials.83

Harrer et al.81 suggest that NLP can be used in
automating clinical drug trials to raise success rates.
The second half of the long drug development
process for any single drug is dedicated to clinical
trials. When a trial fails, the investment into thetrial and the preclinical development is lost. Oneof the main causes of failed trials is suboptimal
patient cohort selection. AI approaches like NLP
and computer vision algorithms can be used to
examine medical records and patient data in
different formats, automating the process of patient
selection. These methods may be able to findpatients who are more capable of responding totreatment and more suitable for a trial.

Morello et al. present SPINN, a machine learning
tool that automates the identification of pulsars(a type of star) in large surveys.84 Modern pulsar
surveys are too large for visual inspection byhumans. Similarly, Lochner et al. have developed amodel to automate the classification of supernova
images, and Banerji et al. have trained a model toclassify different types of galaxies.85
SIMULATION AND PREDICTION OF PHYSICALSYSTEMSA project by the Alan Turing Institute and theJohn Innes Centre86 is exploring the possibility of

80
81
82
83
84
85
86

Dutton, 2018
Harrer et al., 2019
Musib et al., 2017
The Alan Turing Institute & The Royal Society, 2019
Morello et al., 2014
Banerji et al., 2010; Lochner, McEwen, Peiris, Lahav, & Winter, 2016
The Royal Society and The Alan Turing Institute, 201921

using machine learning to model and predict thebiosynthesis process of the triterpene molecule inplants. Triterpenes are a valuable class of natural
plant products, used across health, agriculture, andindustrial sectors.
Rigoberto Medina Andres mentions in the Sciencemagazine survey that plant biologists use machinelearning to predict how proteins interact, which
can be used to improve crop yield during drought
conditions.87
Training AI models to be accurate requires a hugedata input that can be difficult for humans togather. Reyes and Maruyama suggest that dataobtained by machine simulations can be used aslabelled data input to train other machine learningmodels. This relies on the accuracy of the simulateddata, as well as the machine learning model’s ability
to generalise past any ‘artificial features’ in thesimulated data, so that it is also able to analysereal world images.88 For example, a machine could
be trained to detect defects from an image ofa material using simulated data of the material
structure. Real images are noisy and have limited
resolution, and a machine trained on pristine
simulated data may not be able to look past theseinterferences.

87
88

Musib et al., 2017
Reyes & Maruyama, 201922

REFERENCES
AlphaFold. (2018, December 2). AlphaFold: Using
AI for scientific discovery. Retrieved 20 September
2019, from Deepmind website: https://deepmind.
com/blog/article/alphafold

J., Carrasco, M. P., Fotoran, W. L., … Oliver, S. G.
(2018). Plasmodium dihydrofolate reductase is asecond enzyme target for the antimalarial action of
triclosan. Scientific Reports, 8(1), 1038. https://doi.
org/10.1038/s41598-018-19549-x

Amodei, D., & Hernandez, D. (2018, May 16). AI
and Compute. Retrieved 20 September 2019, from
OpenAI website: https://openai.com/blog/ai-andcompute/

Bloom, N., Jones, C., Van Reenen, J., & Webb, M.
(2019). Are Ideas Getting Harder to Find? https://doi.org/10.3386/w23782

Andre, L. (2018, September 13). 20 Best Grant
Management Software in 2019. Retrieved 23
September 2019, from Financesonline.com website:
https://financesonline.com/grant-management/

Bostrom, N. (2016). Superintelligence: Paths,
dangers, strategies. Oxford, United Kingdom ; New
York, NY: Oxford University Press.

Aspesi, C., Allen, N., Crow, R., Daugherty, S.,
Joseph, H., McArthur, J., & Shockey, N. (2019).
SPARC Landscape Analysis. Retrieved from https://
sparcopen.org/our-work/landscape-analysis/

Burgess, M. (2017, February 8). Criminals beware!
Google’s AI can now identify faces from heavily
pixellated images. Wired UK. Retrieved fromhttps://www.wired.co.uk/article/google-brain-aipictures-blur

Banerji, M., Lahav, O., Lintott, C. J., Abdalla, F.
B., Schawinski, K., Bamford, S. P., … Vandenberg,
J. (2010). Galaxy Zoo: Reproducing Galaxy
Morphologies Via Machine Learning. Monthly
Notices of the Royal Astronomical Society,406(1), 342–353. https://doi.org/10.1111/j.13652966.2010.16713.x

Burley, R., Moylan, E., DeVoss, C. C., Chabrol,E., Norman, F., Newton, A., … Culley, T. (2017).
What might peer review look like in 2030? (p. 15).
Retrieved from BioMed Central/Digital Science
website: http://events.biomedcentral.com/
wp-content/uploads/2017/04/SpotOn_Report_
PeerReview-1.pdf

Bellamy, K. (2019). Workshop—Exploring AI for
Humanities Research. Retrieved 20 September
2019, from DECM Project website: https://www.
lancaster.ac.uk/digging-ecm/2019/02/exploring-aiworkshop/

Carey, R. (2018, July 10). Interpreting AI Compute
Trends. Retrieved 20 September 2019, from AI
Impacts website: https://aiimpacts.org/interpretingai-compute-trends/
Castelvecchi, D. (2016). Can we open the blackbox of AI? Nature News, 538(7623), 20. https://doi.
org/10.1038/538020a

Berkeley School of Information. (2015, September1). New Professor David Bamman is a Scholar ofComputational Humanities and Social Sciences.
Retrieved 20 September 2019, from UC Berkeley
School of Information website: https://www.ischool.
berkeley.edu/news/2015/new-professor-davidbamman-scholar-computational-humanities-andsocial-sciences

Chen, H.-W. (2019, March 10). The Impact of AI onPeer Review. Retrieved 23 September 2019, from
Medium website: https://medium.com/digitalpublishing-strategy/the-impact-of-ai-on-peerreview-628f1318b18c
Chivers, T. (2018, October 3). Big data hastransformed how we do science. Retrieved

Bilsland, E., Vliet, L. van, Williams, K., Feltham,23

20 September 2019, from World Economic
Forum website: https://www.weforum.org/
agenda/2018/10/how-big-data-is-changingscience/

Google AI. (2019). Connectomics. Retrieved
20 September 2019, from Google AI website:
https://ai.google/research/teams/perception/connectomics/

Cockburn, I. M., Henderson, R., & Stern, S.
(2018). The Impact of Artificial Intelligence on
Innovation (Working Paper No. 24449). https://doi.
org/10.3386/w24449

Grace, K., Salvatier, J., Dafoe, A., Zhang, B., &Evans, O. (2017). When Will AI Exceed Human
Performance? Evidence from AI Experts.
ArXiv:1705.08807 [Cs]. Retrieved from http://arxiv.
org/abs/1705.08807

Dahl, G. (2017). Predicting Properties of Moleculeswith Machine Learning. Retrieved 20 September
2019, from Google AI Blog website: http://
ai.googleblog.com/2017/04/predicting-propertiesof-molecules-with.html

Groth, P., & Cox, J. (2017). Indicators for the useof robotic labs in basic biomedical research: A
literature analysis. PeerJ, 5, e3997. https://doi.
org/10.7717/peerj.3997

Dangovski, R., Jing, L., Nakov, P., Tatalović, M., &Soljačić, M. (2019). Rotational Unit of Memory: A
Novel Representation Unit for RNNs with Scalable
Applications. Transactions of the Association for
Computational Linguistics, 7, 121–138. https://doi.
org/10.1162/tacl_a_00258

Hanson, R. (2016). The age of Em: Work, love,and life when robots rule the Earth (First Edition).
Oxford: Oxford University Press.
Harrer, S., Shah, P., Antony, B., & Hu, J. (2019).
Artificial Intelligence for Clinical Trial Design. Trends
in Pharmacological Sciences, 40(8), 577–591.
https://doi.org/10.1016/j.tips.2019.05.005

Dutton, G. (2018, January 1). Big Pharma
Reads Big Data, Sees Big Picture. Retrieved 23
September 2019, from GEN - Genetic Engineeringand Biotechnology News website: https://www.
genengnews.com/magazine/306/big-pharmareads-big-data-sees-big-picture/

Heard, N. A. (2004). Technology in Genetics:
Automating the scientific process. Heredity, 93(1),
6–7. https://doi.org/10.1038/sj.hdy.6800463
Heaven, D. (2018). AI peer reviewers unleashedto ease publishing grind. Nature, 563, 609–610.
https://doi.org/10.1038/d41586-018-07245-9

Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green,T., Qin, C., … Senior, A. (2018). De novo structure
prediction with deep-learning based scoring.

Jean-baptiste Alayrac, Sam Blackwell, Joao
Carreira, Reena Chopra, Sander Dieleman, Brian
McWilliams, … Michal Zielinski. (2019, August 8).
Using machine learning to accelerate ecologicalresearch. Retrieved 20 September 2019, from
Deepmind website: /blog/article/using-machinelearning-to-accelerate-ecological-research

Ezer, D. (2018). Data-driven experiment design.
Retrieved 20 September 2019, from The Alan
Turing Institute website: https://www.turing.ac.uk/
research/research-projects/data-driven-experimentdesign
Frey, C. B., & Osborne, M. A. (2017). The futureof employment: How susceptible are jobs tocomputerisation? Technological Forecasting
and Social Change, 114, 254–280. https://doi.
org/10.1016/j.techfore.2016.08.019

Keriann Strickland. (2018, December 13). 6 Easy
Ways to Automate Your Grants Review Process.
Retrieved 23 September 2019, from Submittable
Blog | Submission Management Software website:
https://blog.submittable.com/easy-ways-toautomate-your-grants-review-process/Garfinkel, B. (2018, December 18). Reinterpreting“AI and Compute”. Retrieved 20 September 2019,from AI Impacts website: https://aiimpacts.org/
reinterpreting-ai-and-compute/King, R. (2018, March 26). Better, Faster, Smarter?
The automation of science. Retrieved 23 September
2019, from The OECD Forum Network website:
https://www.oecd-forum.org/users/91064-ross-king/posts/31642-better-faster-smarter-the-automationof-science

Gershgorn, D. (2017, July 26). The data thattransformed AI research—And possibly the world.
Retrieved 20 September 2019, from Quartz
website: https://qz.com/1034972/the-data-thatchanged-the-direction-of-ai-research-and-possiblythe-world/

King, R. D., Rowland, J., Oliver, S. G., Young, M.,
Aubrey, W., Byrne, E., … Clare, A. (2009). The
Automation of Science. Science, 324(5923), 85–89.

24https://doi.org/10.1126/science.1165620

will the world of research look like 10 years fromnow? Retrieved from Elsevier/Ipsos MORI website:
https://www.elsevier.com/connect/elsevier-researchfutures-report

Kitano, H. (2016). Artificial Intelligence to
Win the Nobel Prize and Beyond: Creating
the Engine for Scientific Discovery (p.
11). Retrieved from Association for the
Advancement of Artificial Intelligencewebsite: https://pdfs.semanticscholar.org/
a926/9acd143b340071a4562b8194334daf9da4d9.
pdf

Musib, M., Wang, F., Tarselli, M. A., Yoho, R., Yu,
K.-H., Andrés, R. M., … Sharafeldin, I. M. (2017).
Artificial intelligence in research. Science, 357, 28–
30. https://doi.org/10.1126/science.357.6346.28
Naik, A. W., Kangas, J. D., Sullivan, D. P., & Murphy,
R. F. (2016). Active machine learning-driven
experimentation to determine compound effects
on protein patterns. ELife, 5, e10047. https://doi.
org/10.7554/eLife.10047

Le, J. (2018, July 22). Snapchat’s Filters: How
computer vision recognizes your face. Retrieved 20
September 2019, from Medium website: https://
medium.com/cracking-the-data-science-interview/snapchats-filters-how-computer-vision-recognizesyour-face-9907d6904b91

Olena, A. (2018, June 1). Bringing the Internet ofThings into the Lab. Retrieved 20 September 2019,from The Scientist Magazine® website: https://www.the-scientist.com/bio-business/bringing-theinternet-of-things-into-the-lab-64265

Leviathan, Y., & Matias, Y. (2018, May 8). Google
Duplex: An AI System for Accomplishing RealWorld Tasks Over the Phone. Retrieved 20
September 2019, from Google AI Blog website:
http://ai.googleblog.com/2018/05/duplex-aisystem-for-natural-conversation.html

Ott, M., Auli, M., Ng, N., Edunov, S., Levy, O.,
Singh, A., & Stoyanov, V. (2019, August 14). New
advances in natural language processing to better
connect people. Retrieved 20 September 2019,from https://ai.facebook.com/blog/new-advancesin-natural-language-processing-to-better-connectpeople/

Lochner, M., McEwen, J. D., Peiris, H. V., Lahav,
O., & Winter, M. K. (2016). PHOTOMETRICSUPERNOVA CLASSIFICATION WITH MACHINELEARNING. The Astrophysical Journal Supplement
Series, 225(2), 31. https://doi.org/10.3847/00670049/225/2/31

Papangelis, A., Wang, Y.-C., Namazifar, M., & Khatri,
C. (2019, July 16). Introducing the Plato Research
Dialogue System: A Flexible Conversational AI
Platform. Retrieved 20 September 2019, from Uber
Engineering Blog website: https://eng.uber.com/
plato-research-dialogue-system/

Mayo, H., Punchihewa, H., Emile, J., & Morrison,J. (2018). History of Machine Learning. Retrieved
20 September 2019, from AI in Radiology website:
https://www.doc.ic.ac.uk/~jce317/history-machinelearning.html

Perkel, J. M. (2017). The Internet of Things comesto the lab. Nature News, 542(7639), 125. https://doi.org/10.1038/542125a

McCandlish, S., Kaplan, J., & Amodei, D. (2018,
December 14). How AI Training Scales. Retrieved
20 September 2019, from OpenAI website: https://openai.com/blog/science-of-ai/

Ray, S. (2018, December 1). History of AI. Retrieved
20 September 2019, from Medium website: https://
towardsdatascience.com/history-of-ai-484a86fc16ef

McCook, A. A. (2017, November 9). Newly released
AI software writes papers for you—What could
go wrong? Retrieved 23 September 2019, from
Retraction Watch website: https://retractionwatch.
com/2017/11/09/newly-released-ai-software-writespapers-go-wrong/

Reyes, K. G., & Maruyama, B. (2019). The machinelearning revolution in materials? MRS Bulletin, 44(7),530–537. https://doi.org/10.1557/mrs.2019.153
Richardson, G. (2019, August 7). Towards an open
science of science. Retrieved 23 September 2019,from Nesta website: https://www.nesta.org.uk/blog/
towards-an-open-science-science/

Morello, V., Barr, E. D., Bailes, M., Flynn, C. M.,
Keane, E. F., & van Straten, W. (2014). SPINN:
A straightforward machine learning solution to
the pulsar candidate selection problem. Monthly
Notices of the Royal Astronomical Society, 443(2),1651–1662. https://doi.org/10.1093/mnras/stu1188

Ridder, D. de. (2018). Artificial intelligence in thelab: Ask not what your computer can do for you.
Microbial Biotechnology, 12(1), 38–40. https://doi.
org/10.1111/1751-7915.13317

Mulligan, A., & Herbert, R. (2019). Report: What

25

RXRX. (2019). RXRX.AI. Retrieved 20 September
2019, from RXRX website: https://www.rxrx.ai/

Times Higher Education. (2016, September 20).
Robot-written reviews fool academics. Retrieved
23 September 2019, from Times Higher Education(THE) website: https://www.timeshighereducation.
com/news/robot-written-reviews-fool-academics

Schwab, K. (2016). The fourth industrial revolution(First U.S. edition). New York: Crown Business.
SciNote. (2017). Manuscript Writer Generates
Draft of Your Scientific Manuscript. Retrieved 20
September 2019, from SciNote website: https://scinote.net/manuscript-writer/

Tshitoyan, V., Dagdelen, J., Weston, L., Dunn,
A., Rong, Z., Kononova, O., … Jain, A. (2019).
Unsupervised word embeddings capture latentknowledge from materials science literature.
Nature, 571(7763), 95–98. https://doi.org/10.1038/
s41586-019-1335-8

Shanahan, D. (2016, May 23). A peerless review?
Automating methodological and statistical review.
Retrieved 23 September 2019, from Research inprogress blog website: https://blogs.biomedcentral.
com/bmcblog/2016/05/23/peerless-reviewautomating-methodological-statistical-review/

Turovsky, B. (2016, November 15). Found intranslation: More accurate, fluent sentences in
Google Translate. Retrieved 20 September 2019,from Google website: https://www.blog.google/
products/translate/found-translation-more-accuratefluent-sentences-google-translate/

Simonite, T. (2016, May 13). The foundation of thecomputing industry’s innovation is faltering. Whatcan replace it? Retrieved 20 September 2019, fromMIT Technology Review website: https://www.
technologyreview.com/s/601441/moores-law-isdead-now-what/

University of Cambridge. (2018, January 18). AI‘scientist’ finds that toothpaste ingredient may
help fight drug-resistant malaria. Retrieved 23
September 2019, from University of Cambridge
website: https://www.cam.ac.uk/research/news/aiscientist-finds-that-toothpaste-ingredient-may-helpfight-drug-resistant-malaria

Simonite, T. (2017, December 19). Facebook Can
Now Find Your Face, Even When It’s Not Tagged.
Wired. Retrieved from https://www.wired.com/
story/facebook-will-find-your-face-even-when-itsnot-tagged/

Webb, S. (2018). Deep learning for biology. Nature,554, 555–557. https://doi.org/10.1038/d41586-01802174-z

Snyder, A. (2018, January 11). How astronomers
are using AI to study the universe—Fast. Retrieved
20 September 2019, from Axios website: https://www.axios.com/astronomers-are-using-ai-to-studythe-vast-universe-fast-1515610061-451926e4-02f94460-9f68-a7489e7c6577.html

WizeHive. (2019). Awards Management Software for
Submissions & Judging. Retrieved 23 September
2019, from https://www.wizehive.com/awards
Wu, J., Amodei, D., Amodei, D., Clark, J., &Brundage, M. (2019, February 14). Better Language
Models and Their Implications. Retrieved 20
September 2019, from OpenAI website: https://openai.com/blog/better-language-models/

SPARC. (2018). SPARC: Setting the Default toOpen. Retrieved 23 September 2019, from SPARC
website: https://sparcopen.org/who-we-are/Stone, P., Brooks, R., Brynjolfsson, E., Calo, R.,
Etzioni, O., Hager, G., … Teller, A. (2016). 2016
Report | One Hundred Year Study on Artificial
Intelligence (AI100). Retrieved 20 September 2019,from https://ai100.stanford.edu/2016-report

Wu, S., Kondo, Y., Kakimoto, M., Yang, B., Yamada,H., Kuwajima, I., … Yoshida, R. (2019). Successful
application of machine learning in the discovery of
new polymers. Retrieved 20 September 2019, from
ScienceDaily website: https://www.sciencedaily.
com/releases/2019/07/190719102133.htm

Susskind, R. E., & Susskind, D. (2017). The future of
the professions: How technology will transform thework of human experts. Oxford: Oxford University
Press.

Yarkoni, T., Eckles, D., Heathers, J., Levenstein,M., Smaldino, P., & Lane, J. I. (2019). Enhancing
and accelerating social science via automation:
Challenges and opportunities [Preprint]. https://doi.
org/10.31235/osf.io/vncwe

The Alan Turing Institute, & The Royal Society.
(2019). The AI Revolution in Scientific Research.
Retrieved from https://royalsociety.org/-/media/
policy/projects/ai-and-society/AI-revolution-inscience.pdf?la=en-GB&hash=5240F21B56364A000
53538A0BC29FF5F
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