# Artificial Intelligence in Science and Society: the Vision of USERN

Tommaso Dorigo, Gary D Brown, Carlo Casonato, Artemi Cerdà, Joseph Ciarrochi, Mauro Da Lio, Nicole D'souza, Nicolas R Gauger, Steven C Hayes, Stefan G Hofmann, et al.

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## To cite this version:

Tommaso Dorigo, Gary D Brown, Carlo Casonato, Artemi Cerdà, Joseph Ciarrochi, et al. Artificial Intelligence in Science and Society: the Vision of USERN. IEEE Access, 2025, 13, pp.15993-16054. 10.1109/access.2025.3529357. hal-04904623

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# Artificial Intelligence in Science and Society: the Vision of USERN

TOMMASO DORIGO<fnref n="1" />,<fnref n="2" />,<fnref n="3" />, (Ed.), GARY D. BROWN<fnref n="4" />,<fnref n="3" />, CARLO CASONATO<fnref n="5" />, ARTEMI CERDÀ<fnref n="6" />,<fnref n="3" />, JOSEPH CIARROCHI<fnref n="7" />, MAURO DA LIO<fnref n="8" />, (Member, IEEE), NICOLE D'SOUZA<fnref n="9" />,<fnref n="10" />, NICOLAS R. GAUGER<fnref n="11" />, STEVEN C. HAYES<fnref n="12" />,<fnref n="13" />,<fnref n="3" />, STEFAN G. HOFMANN<fnref n="14" />, ROBERT JOHANSSON<fnref n="15" />, MARCUS LIWICKI<fnref n="1" />, FABIEN LOTTE<fnref n="16" />,<fnref n="3" />, JUAN J. NIETO<fnref n="17" />,<fnref n="3" />, GIULIA OLIVATO<fnref n="5" />, PETER PARNES<fnref n="18" />, GEORGE PERRY<fnref n="19" />,<fnref n="3" />, ALICE PLEBE<fnref n="20" />, IDUPULAPATI M. RAO<fnref n="21" />,<fnref n="22" />,<fnref n="3" />, NIMA REZAEI<fnref n="23" />,<fnref n="3" />, FREDRIK SANDIN<fnref n="1" />, ANDREY USTYUZHANIN<fnref n="24" />,<fnref n="25" />, GIORGIO VALLORTIGARA<fnref n="26" />, PIETRO VISCHIA<fnref n="27" />,<fnref n="3" />, and NILOUFAR YAZDANPANAH<fnref n="23" />,<fnref n="3" />

Corresponding author: Mauro Da Lio (e-mail: mauro.dalio@unitn.it).

**ABSTRACT** The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in the foreseeable future. In this review, we first attempt to establish clear definitions of intelligence and consciousness, then provide an overview of AI's state of the art and its applications. A discussion of the implications, opportunities, and liabilities of the diffusion of AI for research in a few representative fields of science follows this. Finally, we address the potential risks of AI to modern society, suggest strategies for mitigating those risks, and present our conclusions and recommendations.

**INDEX TERMS** Artificial intelligence, scientific research, science ethics, computer science, physics, medicine, psychology, mathematics, geography, agriculture.

<footnote n="1">¹Lulea University of Technology, Lulea (Sweden)</footnote>

<footnote n="2">²INFN - Sezione di Padova, Padova (Italy)</footnote>

<footnote n="3">³Universal Scientific Education and Research Network</footnote>

<footnote n="4">⁴Bush School of Government and Public Service, Texas A&M University College Station, Texas (USA)</footnote>

<footnote n="5">⁵Faculty of Law, University of Trento, Trento (Italy)</footnote>

<footnote n="6">⁶Soil Erosion and Degradation Research Group, Departament de Geografia. Universitat de València, Valencia (Spain)</footnote>

<footnote n="7">⁷Australian Catholic University, Institute of Positive Psychology and Education, Sydney (Australia)</footnote>

<footnote n="8">⁸Department of Industrial Engineering, Università degli Studi di Trento, Trento (Italy)</footnote>

<footnote n="9">⁹Department of Neuroscience, College of Humanities, Arts, and Social Sciences (CHASS), University of California Riverside, (USA)</footnote>

<footnote n="10">¹⁰Systems Neural Engineering Laboratory, University of Southern California Viterbi School of Engineering (USA)</footnote>

<footnote n="11">¹¹Chair for Scientific Computing, University of Kaiserslautern-Landau (RPTU), Kaiserslautern (Germany)</footnote>

<footnote n="12">¹²Department of Psychology, University of Nevada, Reno, Nevada (USA)</footnote>

<footnote n="13">¹³Institute for Better Health, Santa Rosa, California (USA)</footnote>

<footnote n="14">¹⁴Department of Psychology, Philipps University Marburg, Marburg (Germany)</footnote>

<footnote n="15">¹⁵Department of Psychology, Stockholm University, Stockholm (Sweden)</footnote>

<footnote n="16">¹⁶Inria Center at the University of Bordeaux / LaBRI, Talence (France)</footnote>

<footnote n="17">¹⁷Galician Centre for Mathematical Research and Technology (CITMAga) and Department of Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Santiago de Compostela (Spain)</footnote>

<footnote n="18">¹⁸ArcTech Learning Lab, Pervasive and Mobile Computing, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå (Sweden)</footnote>

<footnote n="19">¹⁹Department of Neuroscience, Developmental and Regenerative Biology, University of Texas at San Antonio, San Antonio, Texas (USA)</footnote>

<footnote n="20">²⁰Department of Computer Science, University College London, London (United Kingdom)</footnote>

<footnote n="21">²¹International Center for Tropical Agriculture (CIAT), Cali (Colombia)</footnote>

<footnote n="22">²²International Centre of Insect Physiology and Ecology (ICIPE), Nairobi (Kenya)</footnote>

<footnote n="23">²³Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran (Iran)</footnote>

<footnote n="24">²⁴Constructor University, Bremen (Germany)</footnote>

<footnote n="25">²⁵Institute for Functional Intelligent Materials, National University of Singapore, Singapore (Singapore)</footnote>

<footnote n="26">²⁶Centre for Mind/Brain Sciences, University of Trento, Rovereto (Italy)</footnote>

<footnote n="27">²⁷Universidad de Oviedo and ICTEA, Oviedo (Spain)</footnote>
# I. INTRODUCTION

## A. THE FUTURE OF INTELLIGENCE

Thanks to a series of groundbreaking discoveries in exoplanet- etology, during the past thirty years we have gradually come to realize that planets similar to our own, where we may speculate that there be a significant possibility of emergence of carbon-based life forms such as those inhabiting Earth, are relatively common, with more than ten thousand candidates cataloged as of January 3, 2024¹. Indeed, a handful of such candidates have already been identified within 120 light years of distance from us, and their number keeps growing as we improve our detection technologies. A recent estimate com- bining data from Kepler and Gaia missions using Bayesian inference [1] assesses in the range of 0.17 to 0.83 the number of planets in the habitable zone with masses between 1.0 and 1.75 Earth masses and orbital periods around stars of type F, G, or K between 237 and 500 days (see Fig. 1). Even though affected by significant uncertainties, these numbers imply that several billion earth-like planets exist in our galaxy alone.

<figure><img src="image_3.png" /><figcaption>**FIGURE 1.** Inferred occurrence rate density ($\Gamma_{\oplus}$) of habitable-zone planets estimated by various studies. Displayed posterior densities for orbital periods of 237-500 days and different radius ranges refer to radii $Rp = 1 - 1.75 R_{\oplus}$ (where $R_{\oplus}$ is the Earth radius) in solid black, and $Rp = 0.75 - 1.5 R_{\oplus}$ in dotted black. Reprinted from [1].</figcaption></figure>

On the other hand, geological studies indicate that planet Earth formed about 4.6 billion years ago [2], when it with- stood gradual accretion through gravitational interactions from a disk of debris in orbit around our early Sun. Although less precisely, geological rock records indicate that only four billion years later did pluricellular life start to flourish on it [3], when a wealth of different creatures progressively inhabited its seas and later its lands. On such a time scale, the emergence of biological intelligence –which we here generically associate with biological life forms endowed with acquired self-awareness and entertaining communication and craftsmanship skills<fnref n="2" />– is a very recent phenomenon and might constitute only a brief parenthesis in the history of our planet. In light of the above considerations, the Fermi paradox (the

contrast between the high probability of life emergence and the absence of its evidence, famously introduced by Enrico Fermi to question the hypothesis of widespread life in the uni- verse) appears to have no objection to this line of reasoning.

When considering the universe from the point of view of its intelligence content, armed with estimates of the number of habitable Earth-like planets and knowledge of what happened on our planet until now, we are forced to assess what phenom- ena have the potential to cause mass extinctions. Many rea- sonably well-understood events of cataclysmic nature, from collision with asteroids and comets to solar flares, super- volcano eruptions, or nearby supernovae explosions, as well as slower evolutionary processes such as planetary motion instabilities, should then be assessed for their expected rates, which contribute to reduce the expected duration of our life span as a species, while also providing possible “restart” conditions to make the environment more suitable for life evo- lution. One must add several potential anthropogenic occur- rences to those phenomena, including nuclear or biological warfare, climate change, and ecological collapse.

In the category of anthropogenic threats of relevance, an emerging one is the willful or serendipitous generation of artificial general intelligence (AGI), which develops goals of its own which are misaligned with the survival of humanity [4], [5]. A wealth of scientific fiction novels and movies have considered such a dystopian scenario, which, however, remains a highly speculative and hypothetical one, whose likelihood is impervious to estimation; we leave it aside here, as we will come back to it in more detail *infra* (Sec. V and Sec. VI). Yet even at the level at which we know it today, and in parallel to its offer of tremendous opportunities for progress, artificial intelligence (AI) is already a potential contributor to significant existential risks for humankind: for instance, its vicious use for the pollution of the information ecosys- tem with falsehood, undermining the basis of trust among individuals and nations and the concept/process of people electing their national leaders based on personal beliefs and preferences (since the opinion of people can be systemati- cally biased using closed-loop AI systems and Internet-wide platforms). Various other consequences of the technological advancements in applied AI that have already taken place similarly constitute clear and present dangers today; we will examine them in the second half of this document (Sec. IV, Sec. V, and Sec. VI).

The probability of anthropogenic catastrophes is generally agreed to be much larger than that of natural extinction- level events and, therefore, contributes more significantly to reducing the expected life span of intelligent life on Earth, but it is harder to estimate. We can still summarize the situation by saying there is qualitative evidence that the rate of catas- trophic phenomena is significant. This makes the lifetime of our species bounded from above.

Barring the possible yet arguably unlikely achievement of widespread colonization of nearby habitable planets by human beings, which would make intelligent life significantly more robust to global existential threats of both anthropogenic

<footnote n="1">¹A comprehensive list is in https://exoplanets.nasa.gov/discovery/exoplanet-catalog/.</footnote>

<footnote n="2">²A more detailed discussion of the meaning of the term “biological intelligence” is offered in Sec. II.</footnote>
and natural origins, and using inductive reasoning in assum-
ing that the history of our planet is not too uncommon, we
must therefore come to terms with the idea that biological
intelligence might be a comparatively rare occurrence in the
universe: billions of Earth-like planets exist, yet the existence
of intelligent life forms is likely only an ephemeral phe-
nomenon on each of them. A different conclusion may instead
be reached on AGI. In the past few decades, we have been
lulled to perceive the rise in non-biological intelligence as a
slow process by sitting on the initially linear-looking slope of
what could soon manifest as an exponential curve. From the
development of the theoretical underpinnings of AI in the first
half of the twentieth century to the coming of age of powerful
computers toward the end of the millennium, and then from
the rise of smartphones to today's widespread AI systems, we
were given the time to get accustomed to each new advance-
ment without suffering a cultural shock. However, things are
bound to change very soon, as the exponential trend in AI
capabilities, diffusion, and overall impact on society are all
becoming manifest. This has also revitalized discussions that
experts have been having off and on for over sixty years about
the possibility of creating AGI systems endowed with self-
consciousness and general capabilities that would quickly
transcend human intelligence, and it has also brought a wealth
of arguments to those who argue about the likelihood of that
scenario.

Although the topic is still controversial, the most poignant question today appears not if but when AGI systems will be produced. While opinions still differ widely, the majority view seems to be today that, whether it will be in 20 or 50 more years, AGI will arise on Earth: it has started to feel like an evolutionary necessity whose fuel is the enormous empowerment and profit it would guarantee to its creators and owners. And since AGI may be able to transcend most of the existential risks that biological intelligence is subjected to and is vastly more fit to endow itself with the means to become an interplanetary phenomenon, it is certainly not unreasonable to conclude –using again the hypothesis that Earth be a typical planet– that *the most common substrate of intelligence in the universe is artificial, and not biological*.

Despite its speculative nature, we deem the above observa-
tion highly significant, as it alone provides a substantial rea-
son to view the future of artificial intelligence with heightened
interest and concern: it is a phenomenon with a potentially
transformative impact on different scales of space and time.
This insight underscores the necessity for intensified scrutiny
and engagement with AI developments. While we believe AI
to be a logical and inevitable outcome of human evolution,
how such a development unfolds remains partly within our
control. Therefore, the consequences of our actions at this
critical juncture are undeniably enormous.

B. RECENT IMPACTS OF ARTIFICIAL INTELLIGENCE

Over the past decade, we have witnessed many disruptive
advancements in the capabilities of automated systems that

display intelligent behavior<fnref n="3" />. In Sec. IV and V of this article we describe in detail the impact these systems have had on scientific research and our society, respectively. Here, to introduce the topic, we may single out the one among these developments that is perhaps the most surprising, besides being the most recent: the advent of large language models. In the matter of a couple of years, these systems have made a transition from being a topic of experimental research to settling as a centerpiece that fills an enormous void in the space of computer applications, effectively offering themselves as go-to oracles capable of providing detailed and relevant answers to almost any conceivable query. The impact that large language models are having in scientific research is highly significant, to the point that their assistance in the writing of scientific articles has turned from being a joke to becoming an acceptable, formally disciplined practice<fnref n="4" />.

The acceleration that artificial intelligence has displayed over the past few years is most evident in market-driven applications, where it is powered by the enormous profits that the development of new AI systems may obtain; this is causing concern over the lack of relevant regulations. The situation is more controllable in scientific research due to two factors acting as regulatory brakes on applying new AI technologies. Firstly, progress is slowed by the absence of substantial funding sources that drive profit-oriented research. Secondly, many research areas that could withstand rapid acceleration and paradigm shifts, such as biology, medicine, and genetics, are under strict scrutiny by supervisory bodies. This results in the innovation rate aligning with these fields' characteristic approval cycles. The situation is, however, complex, as these bodies are not acting at a global level, and their power is limited. Under these circumstances, monitoring the situation by looking into the reality of public research across scientific disciplines appears crucial. Such a survey may inform us of better ways to handle the future challenges ahead of us. As a paradigm, we mention the recently issued EU Artificial Intelligence Act (from now on "AI Act"), the world's first comprehensive AI law [6], which starts addressing issues that will become more and more relevant in the coming years.

C. ABOUT THIS WORK

In this document, the production of which was organized
by the Universal Scientific Education and Research Network
5, we offer our views on the matter by discussing the
implications of the current state of affairs in the development
of AI for humankind, focusing in particular on scientific
research, where we cover some of the most relevant areas
of developments with our collective expertise as researchers.
The purpose of this work is twofold. First, we wish to offer

<footnote n="3">³For a careful discussion and assessment of what can be qualified as such, see Sec. II</footnote>

<footnote n="4">⁴E.g., many journals today explicitly request authors to declare whether their work has been produced with the help of large language models—implying that the practice is acceptable as long as it is made manifest.</footnote>

<footnote n="5">⁵The Universal Scientific Education and Research Network, https://usern.org.</footnote>
a state-of-the-art survey on using AI tools for research. For this purpose, we examined a selected and representative set of scientific disciplines, considering the transformations already brought about by introducing AI techniques and forecasting their future evolution. Additionally, this text aims to spark a broad discussion on the opportunities and challenges posed by AI algorithms and systems in scientific research and human society by discussing their virtuous and nocuous or malignant uses. We do this with the long-term goal of bringing the topic to greater attention within the scientific community and keeping it firmly under scrutiny.

The article was written by surveying the advisory board of USERN, which comprises 600 scientists in 22 disciplines. The goal was to identify a team of experts who could provide an overview of the ongoing developments in AI and their impact on different disciplines (from a user perspective). The authors were asked to examine the challenges and opportunities that AI developments bring to their field of expertise and to summarize them in subsections of the manuscript. We then collaborated on these drafts to reach a consensus on the topics they covered. Special attention was given to identifying specific risks that AI developments may introduce in scientific research, as we believe we are well-positioned to make these assessments and that this is a valuable goal for our work.

We begin in Sec. II to establish definitions of intelligence and the related question of self-consciousness. In Sec. III we provide an overview of AI's state of the art and discuss potential future developments in several application areas. In Sec. IV we discuss the implications of AI for scientific research in a few selected fields. In Sec. V we consider the potential risks of AI to modern society, the strategies we might employ to mitigate those risks, and the benefits of its virtuous use for humanity and the environment. Finally, we present our conclusions and recommendations in Sec. VI.

## II. DEFINITIONS OF INTELLIGENCE AND CONSCIOUSNESS

Before addressing the status and issues of artificial intelligence in science and society, we need to agree on what we mean by intelligence. The term is highly overloaded, as it may refer to a large number of different and independent abilities and competencies; indeed, its exact definition has kept generations of scholars entertained [7], including attempts at quantitative definitions indeed focusing on the extent to which an agent can be successful across a complex set of different competences [8].

### A. INTELLIGENCE

For this work, we may agree that, in general, intelligence is the ability to solve problems. From a biological point of view, the environment poses various problems to organisms, from finding a nest to face a predator, from orienting in space to finding a mate [9]. Thus, we can expect the evolution of adaptive specializations in the realm of intelligent abilities. This is because the logical demands associated with different problems could imply a certain degree of functional incom-

patibility. Consider, e.g., the logical demands related to spatial learning in food-storing birds as opposed to learning the features of the mother hen and siblings by imprinting. The latter must be limited to exposure to a specific period in life (critical period) and must be resistant to forgetting. Otherwise, the young animal would risk learning about inadequate objects or forgetting about the features of the mother and siblings [10], [11]. Food-storing birds, however, must not exhibit the same constraints in learning. Otherwise, there would be limitations to the places where food caching can be done depending on the period, and if memory would resist forever, it would prove impossible to erase the memory of the particular location of a food cache even when it has already been used, and it is now empty.

On the one hand, it is apparent that these functional incompatibilities, which are relative to the coherent constraints associated with a particular kind of problem more than to its implementation in a specific kind of substance, should be adhered to by any type of intelligence, biological or artificial. On the other hand, it is important to stress that there are also classes of problems for which we can expect no specialization but rather common and shared mechanisms [12]. As an analogy, artificial intelligence methods increasingly rely on massive, curated experiences, general pattern recognition algorithms, and modularized methods. Generalized adaptive processes are the case for associative, non-associative, and relational learning mechanisms, whose role is to allow an organism to figure out the causal structure of an environment in terms of either statistical regularities in the sequence of appearance of single events (habituation and sensitization), in the temporal relationship between two or more recurring events (Pavlovian and operant conditioning), or in the semantically and culturally attributed relations among events (multi-dimensional relational reasoning) [13]. Indeed, evidence suggests that biological organisms rely on similar neurobiological processes for these various forms of intelligent behavior. Leaving aside the question of the possible development of artificial intelligent systems from completely different substrates and with different bootstrapping mechanisms, one can reasonably assume that any form of artificial intelligence should also rely on similar and shared principles for non-associative, associative, and relational learning.

Considering the above understanding, Pei Wang's approach in his paper "On Defining Artificial Intelligence" [14] argues that intelligence, whether in humans or artificial systems, fundamentally entails adaptation with insufficient knowledge and resources. Wang stresses that the primary marker of intelligence is not just problem-solving *per se* but the ability to adapt to new and changing environments with limited information and capabilities. This perspective aligns with the biological examples provided, where different species evolve distinct cognitive strategies to handle their unique environmental challenges. Yet, all exhibit a form of adaptiveness that is central to intelligence.

Further, Wang proposes that any system exhibiting intelligence —whether organic or synthetic— must inherently oper-
ate under conditions of uncertainty and resource constraints. This notion resonates with the adaptive specializations observed in biological organisms, as each species' intelligence is shaped not just by the problems they solve but by how they optimize their cognitive processes within the limits of what is biologically feasible for them. Similarly, in artificial systems, this means designing algorithms that do not merely solve tasks but continuously learn and adjust to new data under the practical constraints of time and computational power.

Wang's conceptualization of intelligence also underscores the importance of a system's capacity to handle novel situations without pre-encoded solutions. This flexibility is crucial in biological and artificial contexts and is achieved through mechanisms that allow ongoing learning and adaptation rather than relying solely on hardcoded inflexible rules. Therefore, in creating artificial intelligence, it becomes imperative to develop systems that can dynamically learn from their environment in a manner analogous to biological systems, which continuously adapt through evolutionary pressures.

Thus, integrating Wang's theoretical framework into our understanding of intelligence may provide a more robust model that bridges the divide between biological and artificial systems. It suggests that the core of intelligence lies in the adaptive capacity to utilize limited resources and knowledge to navigate and optimize within a complex, changing environment. This holistic view can guide future research and development in artificial intelligence to focus not just on specific tasks but on creating systems capable of general adaptability and learning, reflecting the true essence of intelligent behavior observed in nature.

Moreover, considering the principles of evolutionary optimization processes in AI development, we see that biological and artificial systems traverse a space of emergent phenomena driven by fundamental interactions. Biological evolution, for instance, has led to the emergence of phenomenal experiences which we still do not fully understand. Similarly, computing substrates possess emergent properties that may arise from complex interactions within hardware and software components. These properties can be nearly orthogonal to the abstract logic processes typically utilized, leading to unexpected behaviors that are not directly programmed.

The limits of these emergent phenomena remain unknown, posing both opportunities and risks. Evolutionary optimization in AI could potentially design behaviors that emerge from interactions beyond deterministic information processing. These processes may exploit unknown properties of the computational substrate, leading to behaviors that are not predictable or controllable using traditional methods. Such emergent behaviors could result in forms of decision-making or problem-solving abilities that we cannot fully understand or anticipate, posing significant risks to safety, security, and ethical standards.

Indeed, AI systems' autonomy (and sometimes adaptiveness) are elements also included in legal texts. Most notably, both the AI Act, a regulation that has been approved but not

yet entered into force, and the Organisation for Economic Cooperation and Development (OECD) [15] characterize AI systems as being machine-based systems that use inference to generate output from input and can influence their environment; they also show (and, in particular, according to article 3 of the EU AI Act, are actually “designed to operate with”) varying degrees of autonomy and (possibly) adaptiveness after deployment. Both organizations consider autonomy as the ability to behave independently and operate without human intervention. At the same time, adaptiveness refers to the system’s ability to continue evolving post-deployment due to its direct interactions with input and data.

The ability of an AI system to operate and modify its behavior independently of human instructions has become an object of attention by policymakers because of its ethical and legal implications, mostly revolving around human agency, autonomy, human oversight, accountability, and liability. Ultimately, if the core of intelligence lies in adapting to scarce information or capabilities, it is only proper that the regulation should account for these developments. Indeed, the (both moral and legal) responsibility for possible harmful effects on individuals or society, as well as the value-laden choice of which decision-making role to attribute to artificial intelligence systems [16] (including for which specific purposes and in which sectors), rests with humans [17].

## B. CONSCIOUSNESS

Consciousness is a polysemic word, but here we shall consider it as «phenomenal experience» [18], i.e., that it feels something to have mental states such as pain, seeing red, or smelling lavender [19], which is also usually referred to as the «hard problem» of consciousness [20]. So defined, awareness or “knowing” is a more evolutionarily continuous process based on the relative ability to respond to oneself and the internal and external environment and the regularities within and between them [21]. Various sensory and cognitive systems are relevant to awareness of that kind.

It is important to stress that not all cognitive activities need to be conscious. The issue of how and why humans and other organisms (or possibly artificial machines) do have consciousness cannot be equated with their having cognition; whatever sophisticated this cognition may be [22], [23]. There is plenty of evidence that advanced forms of cognition can be observed in the absence of consciousness [24], [25]. On the ethical side, note that we do not deny the possession of consciousness to human beings who, because of severe acquired or inherited disabilities, would appear unable of even the most elementary forms of cognitive activities [26]. Thus, the issue of consciousness in AI should be considered separately from evidence of how much intelligence there is in AI.

There is no agreement on the mechanisms underlying consciousness or the proper localization of its neural correlates. Some prominent theories, such as the Global Neuronal Workspace Theory (GNWT) [27] argue for a major participation of the parts of the brain that enable cognition, i.e., the
frontal areas of the cortex, whereas others, such as the inte-
grated information theory (IIT) [28], claim that consciousness
would depend on brain areas involved in perception, i.e., the
more posterior areas of the cortex. These theories are some-
times called “front-of-the-brain” versus “back-of-the-brain”
theories. However, neither of them adequately addresses the
issue of phenomenal experience. GNWT maintains that a re-
duced subset of the information we constantly process uncon-
sciously would be selected to pass through a bottleneck into a
conscious “workspace;” there, the information would be inte-
grated and broadcasted to other brain areas to make it globally
available for decision-making and learning [29]. This theory
does not address how conscious experience arises, which is
mainly centered on cognitive access by attention. IIT, on
the other hand, starts with five axioms about consciousness,
namely that consciousness (1) is intrinsic to the entity who has
it; (2) its composition is structured; (3) it is information-rich;
(4) it is integrated rather than reducible to components; and
(5) it is exclusive of other experiences. Then, the proponents
of the IIT theory tried to develop mathematical descriptions
to fit those axioms, but basically, the theory seems to deal
with a measure of information rather than with phenomenal
experience *per se*; criticisms have also been raised as to its
mathematical parts; e.g., see [30], [31].

Other scholars pointed to sub-cortical mechanisms for con-
sciousness, in particular concerning pain [32]–[37] or about
active movement with feedforward mechanisms mediated by
efference copies to distinguish sensory stimulation impinging
on an animal surface from that occurring by self-motion [38]–
[42]. These latter hypotheses would agree with the possibility
that consciousness could be observed in animals without a
mammalian cortex, such as birds —see [43]–[45]; or even in
organisms with quite different neural organization, such as
insects or invertebrates in general [46], [47].

These kinds of theories appear of particular interest for AI,
for they insist on the idea that to have consciousness, a body
is needed, and that initial forms of consciousness should be
represented by bodily reactions to stimulation (for instance,
in the format of efference copies allowing distinction be-
tween “what is happening to me” and “what is happening out
there”; see e.g., [41], [42]). If correct, these views suggest that
embodiment (more specifically, the development of bodily
reaction to sensory stimulation) would be more important
than possessing symbolic cognitive abilities to develop an
artificial consciousness.

1) Explanatory gap of the mind-body problem

The heart of the problem of consciousness is the issue of
subjective experience and awareness, which is different from
possession of cognitive abilities. It is difficult, if not impos-
sible, to offer a non-circular definition between these three
terms (subjective experience, awareness, and consciousness).
Despite this difficulty, it is evident that conscious beings
know what it feels like to be conscious. The subjective ex-
perience of what it is like to be in a particular consciousness
state (which is referred to as the qualia of consciousness)

cannot be reduced to the activity of the physical elements
(networks of nerve cells in the brain) that enable it. This
seemingly intractable mind-body problem is famously illus-
trated in Nagel's 1974 essay "What is it like to be a bat"
[48]. Chalmers [20] later described this as the hard problem
responsible for a gap between the physical world and sub-
jective experience. No theories of consciousness have been
able to address this so-called explanatory gap, and some do
not even acknowledge it. As this applied to AI, however,
a review of theories of consciousness becomes especially
interesting when they imply evolutionary and experiential
processes modeled in curated exposure to exemplars. Thus,
theories of consciousness that fail to be specific about the
kinds of evolutionary processes involved cannot readily be
used by AI methods to address consciousness *per se*. We will
review several theories before examining them in that light.

2) States of consciousness

It is widely accepted that consciousness is not a singular
entity. Instead, it encompasses various states, particularly
concerning experiential content, though not necessarily in
the presence or absence of experience. These states can be
categorized into two main types: global states and local states.
Global states, or levels, pertain to an organism's subjective
experience and are linked to arousal and behavioral respon-
siveness variations. Examples include wakefulness, dream-
ing, and sedation. On the other hand, local states pertain to
specific conscious perceptions, emotions, or thoughts, often
called conscious contents. These local states can be detailed at
varying levels of specificity, from basic perceptual elements
(e.g., the color red) to objects (e.g., a rose) or to comprehen-
sive multimodal perceptions (e.g., the beauty of a red rose).

3) Selfhood

One of the most remarkable features of the human species
is the concept of the self and its ability to project and con-
ceptualize itself in space and time. The self is a subset of
local states of consciousness, encompassing the experience
of selfhood, which includes emotions, intentions, volition,
free will, body ownership, explicit autobiographical memory,
and more. Self-consciousness, or the conscious awareness
of the self, requires advanced cognitive abilities, including
language, cultural understanding, cooperation, and empathy.
The capacity to attribute mental states to others (known as the
theory of mind) necessitates distinguishing between the self
and the non-self.

The self is a highly complex construct. In his Principles
of Psychology, William James (1890/1983) posited that self-
awareness implies that the self acts as both object and subject,
with an aspect of the self that knows (the knower) and an
aspect that seeks to be known. The latter is called the Me-
self, and the former the I-self. Aspects of the I-self include
self-awareness, self-agency (ownership of one's thoughts and
behaviors), self-coherence (perceiving oneself as a stable
entity), and self-continuity (perceiving oneself as the same
person over time). James identified aspects of the Me-self as
the material Me, the spiritual Me, and the social Me, noting that different social settings give rise to multiple social Me's.

Building on this foundation, we propose at least two aspects of the self: the core self, which is the relatively stable perception of one's persona, and the social self, which is more context-dependent and influenced by social role expectations [49]. This distinction is more than theoretical, as it provides new ways of understanding and treating emotional disorders associated with adaptations of the core self or the social self. As it applies to AI, if selfhood involves an acquired form of perspective-taking or meta-cognition, it may be essential to train large language models to respond to their problem-solving processes from a perspective or point of view rather than merely curating correct answers generated by humans.

### 4) Phenomenal and functional properties of consciousness

It is essential to distinguish between the phenomenal and functional properties of consciousness. The phenomenal properties refer to the experiential aspects of consciousness (the qualia or the subjective experience of what it is like to be conscious). In contrast, the functional properties relate to the role that conscious mental states play in an organism's cognitive processes. This functional aspect includes teleological functions (those shaped by evolution) and dispositional functions (the role a process plays within a larger system). For instance, consciously perceiving a red rose involves various potential functions, such as the ability to interact with it in multiple ways (e.g., touching, smelling, picking, painting, or stepping on it). Depending on the context, this may lead to the formation of an episodic memory of the event and enable the generation of a verbal report about the experience. While these properties are definitionally helpful, they do not give clear guides on approaching the problem of consciousness in AI.

### 5) Global workspace theories

Global Workspace Theories (GWTs) are modeled on the blackboard architecture of AI, where the blackboard serves as a central resource for specialized processors to share and receive information. For instance, being conscious of a red rose signifies a relationship between my cognitive system and the object (the rose), which may be selected for further verbal or nonverbal processing after that. I might smell the rose and remark, “Look at that beautiful rose.” The perception of the rose becomes conscious and globally accessible because the information can trigger a range of actions and thoughts.

The fundamental concept of GWTs is that sensory information gains access to consciousness when it is “broadcast” within an extensive neuronal workspace that spans higher-order cortical association areas, particularly emphasizing the prefrontal cortex [50]. This global workspace is accessible through a nonlinear network “ignition,” where recurrent processing amplifies and sustains neuronal representations [51]. Once ignited, signals are amplified, allowing them to enter the workspace and thus become conscious.

Conscious mental states are globally available to a wide range of cognitive processes, including attention, evaluation, memory, and verbal report [27]. That way, conscious states guide behaviors and cognitions in a flexible, context-dependent way and are related to specific cognitive processes, especially attention and working memory. Consciousness and working memory are intimately related because attended working memory items similarly use the global workspace [50]. Once again, GWTs appear to help define the problem space but not to refine it with an eye toward AI methods.

### 6) Integrated information theory (IIT)

IIT proposes that consciousness should be understood in terms of a complex system's cause-effect power, resulting in integrated information. The degree of consciousness (and integrated information) can be quantified as $\phi(\Phi)$, which measures how much information a system generates, compared with its parts independently. IIT assumes consciousness is widely distributed throughout nature, including in many non-biological systems, and might even occur in systems as simple as cell phones or computers [52], [53]. This approach seems to contradict philosophical arguments, including Searle's Chinese room thought experiment (see *infra*), and, in essence, “defines away” the problem of consciousness without solving it.

### 7) Higher order theories of consciousness

Global availability is just one aspect of consciousness [54]. Another key aspect is the cognitive system's self-referential capacity to monitor its processing, often called meta-cognition. Meta-cognition involves forming internal representations of one's knowledge and abilities. This higher-order level of processing differentiates it from first-order theories like GWT, leading to the term higher-order theories (HOTs).

HOTs propose that a mental state becomes conscious through these meta-representations, which target other representations. For example, the meta-representation “I am conscious of the red rose” refers to another representation (the rose), and this meta-representation constitutes conscious awareness. HOT has been applied to various domains, including visual experiences [55], emotional states like anxiety [56], and perceptual decisions [57]. HOTs emphasize anterior cortical regions, particularly the prefrontal cortex [58]. According to HOTs, some contents may remain non-conscious or unconscious if they are not the targets of appropriate meta-representational states. In contrast, other contents are necessarily conscious if accompanied by the right meta-representations.

HOTs rarely address global states of consciousness or the functions of consciousness [59]. Unlike many other theories, however, HOTs do appear to directly affect AI systems by suggesting that a second-order process of self-curation of exemplars may be key to generalized AI. In other words, it may not merely be the feedback of knowing when thinking is correct in its outcomes that is key; it is more being able
to self-generate that feedback from a consistent perspective or point of view and apply it to the process of the problem-solving itself. This kind of bi-phasic approach [21] has only recently begun to be modeled in AI learning systems, and it is not yet known if it will produce more rapid progress.

### C. CONSCIOUSNESS IN HUMANS, ANIMALS, AND AI

#### 1) Consciousness in animals

If we include the phenomenal aspect of the conscious qualia (i.e., what it is like to be conscious) in the definition of consciousness, it is impossible to ascertain whether animals are conscious. Inferring the experiential state of an animal based on its overt behavior results in anthropomorphism. Indeed, animals, humans, and AI share aspects necessary for consciousness, such as perception, attention, memory, etc. However, none of these aspects and processes alone or in combination necessarily result in a conscious experience, as defined earlier.

As noted by Dehaene, Lau, and Koider [54], thirsty elephants can determine the location of the nearest water hole and move straight to it from a distance of up to 50 km [60]. This amazing feat combines several skills to integrate information, including smell, sight, memory, etc. Nest-building behaviors in birds are similarly complex tasks that may appear to a human observer as a thoughtfully planned and future-oriented behavior to achieve a specific goal. However, even the most complex future-oriented set of behaviors in animals can be explained through evolutionary selection and retention. We are not birds, and we cannot rule out the possibility that birds are consciously aware of their actions, worrying about their offspring, calculating the time it takes to complete the nest in relation to the shift in the season while looking forward to the task completion. We are humans, which is what humans would do and feel if we were birds. But we are not birds. And we are not elephants. We are humans.

The pronounced frontal cortex is an evident distinctive feature of the human brain, and there is good evidence to suggest that it supports the capacity for multimodal convergence and integration of cognitive processes. A comparison between humans and nonhuman primates identified a distinctly human component in the ventrolateral frontal pole and showed differences in interregional interactions within the ventral lateral frontal cortex in the two species [61]. There is also evidence to suggest that the complexity of the pyramidal cell phenotype in the prefrontal cortices is uniquely linked to human cognitive processing [62]. Additionally, humans possess circuits in the inferior prefrontal cortex for verbally formulating and reporting information to others. The relevance of such neurobiological data to consciousness is an object of active research, and progress in this area as it occurs may indirectly impact the issue of consciousness in AI systems.

Human language is often considered one of the most evident signs of conscious perception because information has reached this level of mental representation [54]. As is true for specific brain structures and circuits, although language may not be required for conscious perception and processing, the

emergence of language may have resulted in a considerable increase in speed, ease, and flexibility. For example, damage to the primary visual cortex can lead to a phenomenon called blindsight. Patients with this neurological disorder report being blind in the affected visual field. Although they can localize visual stimuli in their blind field, they cannot report them.

#### 2) AI and consciousness

Examining the role of artificial intelligence (AI) is helpful to clarify the boundary conditions of human consciousness. Consciousness is not reducible to an information processing system operating on formal symbols (a strong version of functionalism), as illustrated by John Searle's classic Chinese room thought experiment. Imagine an English-speaking person who does not know the Chinese language being alone in a room. In this room is an English instruction manual for processing any incoming information. The person then receives some Chinese characters through a slot in the door and is asked to process them according to the English instruction manual to produce other Chinese characters as output. Given enough time, the person will be able to make the output based on the instruction manual without understanding any of the content of the Chinese writing. Based on the generated output, an outside observer who knows Chinese will mistakenly assume a Chinese speaker is in the room.

Just like human operators in the Chinese room, computers have been able to use syntactic rules following instructions to manipulate symbols. Still, they arguably did not “understand” the meaning of those symbols. With that evidence, it might be concluded that the human mind is not simply a computer-like computational or information processing system, disproving what Searle termed strong AI. However, the recent development of deep learning, large language models, and, in general, of systems with super-human capabilities to organize, translate, interpret text, write computer code on demand, and other complex tasks puts those conclusions to severe testing. Notwithstanding, the above example illustrates the difference between two types of information processing computations: the selection of information for global broadcasting (which is accomplished by the English speaker in the Chinese room) and the self-monitoring of those computations. Only the latter is associated with a subjective experience (the qualia of consciousness); whether that is something we can generate with AI is a topic of the present debate.

### III. STATE OF THE ART OF AI SYSTEMS IN FLAGSHIP TASKS AND PERSPECTIVES

In the previous section, we provided an overview of the complexity of defining consciousness, which is a central issue when considering artificial intelligence and the means we possess to understand its nature and limits. Recently, the emergence of Large Language Models (LLM) has brought the question of whether we can associate a form of consciousness with these systems. The answer at present is no, but given the fast development these systems are undergoing and the
plans –and a natural evolution of AI research focusing on LLMs– of enhancing them with sensory inputs and memory, the question will resurface soon, significantly hardened and complexified. In this section, we discuss these areas of AI development.

## A. LARGE LANGUAGE MODELS, TEXT GENERATION AND PROCESSING

Understanding and producing natural language has been one of the primary goals of AI for several good reasons. Firstly, it is widely agreed that language is the hallmark that makes humans unique among all other animal species, and it is the means through which our most advanced intelligence is expressed [63]–[67]. Furthermore, the idea from which AI then emerged, the famous article by Alan Turing [68], proposed linguistic conversation as the best arena in which to exercise –and eventually accredit– intelligence to a computer. This is why recent neural models, capable of successfully processing and producing natural language with remarkable similarity to humans, have generated a significant sensation. The performance of these models was also surprising, considering that just a few years earlier, approaching human language proficiency seemed like an unattainable chimera. Furthermore, an even more advanced form of language -the ability to build mathematical models and make predictions about the future evolution of complex systems- is where the front line of AI research has now moved. Given the strong economic incentive for success in that area, it is not far-fetched to predict breakthroughs shortly. However, rather than considering future developments, we discuss the state of the art in language processing below.

### 1) Traditional natural language processing and artificial neural networks

The enterprise to process natural language with computers (Natural Language Processing, NLP) began as early as the 1950s, with attempts to translate text involving notable pioneers such as Warren Weaver and Yehoshua Bar-Hillel. Early software for translating from Russian to English, like GAT-SLC (Georgetown Automatic Translation - Simulated Linguistic Computer) and SYSTRAN (System of Translation), was developed [69]. These early attempts revealed the subtle complications that separate the sequence of symbols in a written text from its meaning. They highlighted the infeasibility of machine translation without a thorough understanding and formal framing of the countless complications of human language.

In the 1960s, an essential contribution in this direction was made through the mathematical treatment of syntax initiated by Noam Chomsky [70], [71]. His work enabled the automatic derivation of the syntactic structure of simple sentences [72]–[74] and early attempts at basic language understanding [75]. Over the subsequent half century, NLP made remarkable progress, expanding the modeling of natural language to multiple aspects such as proposition semantics [76], anaphora resolution [77], lexical semantics [78], and discourse representation

[79]. However, integrating these pieces into a single system capable of processing language seemed impossible.

When artificial neural networks gained popularity in AI towards the end of the 1980s, intensive research developed on their use for natural language modeling [80]–[85]. The neural approach radically differs from NLP as it eschews Chomsky's theoretical framework and subsequent linguistic theories. The direction is radically empiricist, designing models that directly learn aspects of language from examples. The results aroused considerable interest in the cognitive and psychological field. However, the scope of these models was drastically limited to short, simple sentences and a reduced vocabulary, remaining far from natural language in its completeness.

A significant challenge in using artificial neural models for language processing stems from an apparent irreconcilable discrepancy between the two formats. Language is an ordered sequence of auditory signals or written symbols, while a neural layer is a real vector with a fixed dimension. Designing a transformation from words to numerical vectors that preserves aspects of the words' meaning is problematic. Representing words becomes more challenging when transitioning from single-word morphology to syntax. Moreover, feed-forward neural networks are static, making establishing a sense of ordering for multiple words in a sentence far from straightforward.

Various coding strategies were proposed for solving the representation issue, while the ordering issue was addressed using recurrent networks. However, none of these solutions was effective when moving from a small controlled language to complete language processing. Recurrent neural networks struggle to maintain relevance for words that are too distantly placed yet syntactically related. A slight improvement was made with Long Short-Term Memory (LSTM) [86] and Gated Recurrent Unit (GRU) [87].

On the other hand, neural networks are comfortable dealing with continuous signals. With the advent of deep learning, tasks like text-to-speech and speech-to-text conversion have been essentially solved [88], [89]. Systems like Google's Tacotron and Facebook's Wav2Vec have dramatically improved the fidelity and understandability of generated speech and transcriptions.

### 2) The Transformer architecture

The Transformer architecture, invented by the Google team [90], combines two effective strategies that address the crucial issues noted in the previous section. The first is the word embedding technique, introduced by Mikolov et al. [91], which learns from examples the optimal mapping from words to vectors of neural activity. Its primary feature is that the vectorial representation is meaningful, allowing for manipulation and yielding results consistent with aspects of lexical semantics.

The second strategy is a mechanism called “attention” [92]. This technique dynamically identifies relevant information and relationships among words in a sentence. The Transformer employs these strategies innovatively: on the
one hand, word embedding is learned while the entire neural model absorbs everything from corpora; on the other hand, the attention mechanism entirely replaces recursion, presenting all words along with their vectorial embedding simultaneously as input.

The Transformer architecture retains elements reminiscent of the autoencoder (see Sec. III), featuring both encoding and decoding components. Its seemingly straightforward task involves reproducing the sequence of words it encounters as input. The original version was designed for translation, with an encoder for the input text and a decoder for the text generated in a different language. A simplification was later adopted by GPT (Generative Pre-trained Transformer), consisting only of a decoder part, primarily for generating text by completing a given prompt [93]. The famous public interface ChatGPT is based on later models of the GPT family [94].

The primary innovation of the Transformer lies in a simple heuristic for gauging the relevance of each word in the sequence relative to the current context, leveraging four matrices known as key, query, value, and output. The key matrix transforms the incoming words within the sequence, while the query matrix transforms the words generated in the output. The scalar product of these two matrices modulates the outcomes of the other two matrices. The value matrix operates on the input sequence, while the output matrix affects the output sequence. This mechanism governs which information is extracted from the source words and how it is incorporated into the destination. The matrices are learned dynamically during the training process, in tandem with the conventional feed-forward neural weights of both the encoders and decoders. Moreover, the representation of words as neural vectors is not fixed from the outset but is gradually acquired through exposure to extensive corpora. In a discursive manner, the attention mechanism produces a vector where information from all the words preceding the current one is mixed, weighted according to how relevant the previous word is to the current one. Here, the synergy with the other fundamental mechanism of the Transformer comes into play: word embedding. The efficiency and completeness in capturing every relevant word information in a numerical vector, in every possible context of use, allow relying on simple operations of linear algebra to capture the meaning of the reciprocal syntactic and semantic relationships in a text.

In addition, the entire token expressed as an embedded vector is divided into H portions, called heads, and the identical mechanism described is applied separately to each head. Only in the end are the various portions rejoined. The idea is that an embedded vector combines different properties of a word, and specific categories (e.g., the tense of verbs or the gender and number of nouns and adjectives) always occupy the same portions of the vector. Therefore, it is convenient to process the network of relationships separately between the characteristics of the various words in the text.

Neural language models powered by the Transformer architecture can produce extensive essays comprising thou-

sands of words, conveyed in well-articulated language and enriched with substantial content. The sudden and unexpected appearance of models capable of fulfilling Turing's dream – conversing with human beings [95]– in addition to opening the doors to many practical applications, has sparked an intense theoretical debate. Questions are being raised about how much neural language models truly understand [17], [96]–[99]; what the appropriate methods are for investigating their nature and their potential [100]–[103]; whether they possess the ability to understand the minds of their users [104]–[106]; and even whether they possess consciousness [107], [108].

While AI has made significant strides in understanding and generating natural language, there remains ample scope for improvements. The journey ahead necessitates technological innovations and an intense focus on the ethical, transparent, and responsible use of these systems. The future that combines these will unfold a new era of AI language processing capabilities for a breadth of practical cases.

## B. IMAGE PERCEPTION AND GENERATION

Artificial vision is the field where deep artificial neural networks have unexpectedly revolutionized AI, approaching human performance for the first time in history [109]. This revolution began just over a decade ago [110]. In the wake of this revolution, a new and unique perspective has emerged: the combination of visual perception and image generation. This concept was not present in the 50 years of image processing tradition that preceded deep learning, which primarily meant recognizing its content when processing an image. In that era, image synthesis was a separate domain entirely [111].

The idea that perception and generation are unified in vision is supported by fascinating cognitive science and neuroscience theories and evidence. However, these theories and evidence have rarely been explicitly used to inform the development of artificial vision systems. One immediate example of this unity is the phenomenon of visual imagery. When a person tries to picture an object or a situation, they are essentially re-enacting an internally simulated perception of that object or situation. It was debated whether visual imagery shares features and mechanisms with visual perception for a long time. While Stephen Kosslyn was a supporter of this hypothesis [112], Zenon Pylyshyn flatly rejected it [113]. Recent evidence suggests that Pylyshyn was wrong. Studies have shown that, in the absence of the visual stimulus, the primary visual cortex is activated during visual imagery, albeit with a simplified representation of the object of interest [114]. This infers that visual imagery is not simply a form of imagination but rather an internal simulation based on the exact neural mechanisms of visual perception. More recent evidence suggests a significant overlap in neural processing during perception and imagery, encompassing the visual, parietal, and frontal cortices. This indicates that the two processes are more closely related than previously thought [115]–[117]. Reusing the same neural circuit is the foundation of mirroring, mental simulation (thinking), imitation, deliberation, and mind reading [118].
## 1) The idea of autoencoder

There are several ways to implement the unified approach of perception and generation in deep artificial neural networks. The oldest, yet still a fundamental component in the most advanced models, is the autoencoder, a neural network whose task is to reproduce its input as output by learning a low-dimensional representation of the input. This may seem like a straightforward objective, but as the autoencoder learns to replicate its input, it demonstrates the ability to construct highly insightful and concise internal representations of the data. This concept has been around for quite some time [119] but has more recently served as the cornerstone for the transition from shallow to deep neural architectures [120], [121]. The critical challenge of training neural architectures with multiple internal layers was initially addressed by associating each layer with a Restricted Boltzmann Machine [120]. This allowed the layers to be individually pre-trained in an unsupervised manner. Adopting autoencoders overcame the training costs associated with Boltzmann Machines. Autoencoders permit the training of the whole network like an ordinary fully connected layer. The key idea here is to utilize the same input as the target output, which trains each layer to optimize input reconstruction. The overall result is a regularization of the entire model, akin to the one achieved with Boltzmann Machines [122].

Regardless of specific implementation details, an autoencoder's fundamental structure consists of two neural models. The first is the encoder, responsible for computing a compact representation of a high-dimensional input. The second is the decoder, often called the generative model, which generates high-dimensional data using the low-dimensional compact representation as input. In the initial implementation of the autoencoder for handwritten digit recognition [120], the encoder was constructed by stacking feed-forward layers with a decreasing number of units. At the same time, the decoder consisted of feed-forward layers with a number of units that mirrored those of the encoder in reverse order. In subsequent developments, the encoder typically comprises a hierarchy of convolutional filters interspersed with nonlinear downsampling. This architecture essentially follows the design introduced by Hinton [110], which is derived from the earlier Neocognitron model proposed by Fukushima [123]. The corresponding decoder component adopts the deconvolution approach [124]–[126], which alternates convolution filtering with unpooling, ultimately restoring the high dimensionality of the input image.

## 2) From autoencoders to diffusion models

The autoencoder stands as a fundamental concept bridging the realms of perception and generation, and it is one of the few outcomes of AI with a robust neurophysiological counterpart [127]. The autoencoder has served as the genesis of numerous divergent approaches, departing from their initial biological inspiration and evolving into the cornerstone of today's most influential AI generative models for image generation.

While the primary focus of deep learning developments at the start of the past decade was on enhancing the successful Convolutional Neural Networks (CNNs) architectures, the autoencoder found its path, mainly due to its compelling self-supervision potential. One notable achievement in recognition systems inspired by the autoencoder paradigm is the U-Net, initially designed for medical applications [128]. Kulka et al. [129] harnessed the latent autoencoder representations effectively for discriminating individual graphical attributes within images, including location, pose, lighting, texture, and shape. During this same period, a probabilistic variant of the autoencoder gained rapid popularity, wherein each element of the latent vector incorporates both the mean and variance parameters of a Gaussian distribution. This variant is commonly referred to as the variational autoencoder [130], [131], and, akin to the deterministic autoencoder, it offers the valuable advantage of self-supervision [132]. A notable image processing model that combines variational autoencoders with convolution layers is PixelVAE [133].

A pivotal advancement that greatly influenced the exclusive generative utilization of autoencoders was the introduction of stacked denoising autoencoders [121]. In this innovative approach, the original image undergoes a gradual degradation by adding Gaussian noise. Each autoencoder stage takes a specific level of degradation as input, aiming to encode the immediate level with reduced noise in its output. Through the sequential chaining of these autoencoders, there is a progression from an almost uniformly noisy state to the best achievable reconstruction of the original image.

The immediate benefit of this framework in typical recognition or detection tasks is its effectiveness in handling noisy images. However, the true novelty lies in the framework's ability to generate high-quality images by randomly sampling a matrix filled with Gaussian noise. Denoising autoencoders share similarities with formulations inspired by principles from physics [134], where degradation occurs through probabilistic diffusion, resembling processes in non-equilibrium statistical physics [135]. The degradation operation may be produced by down-sampling, blurring, or Gaussian noise [136]. In all instances, the reverse transformation is learned through conventional neural approaches, and these methodologies are collectively referred to as diffusion models [137].

One of the early and still widely used proprietary tools for generating images with diffusion models is Midjourney<fnref n="6" />. In a parallel vein of development, a team at Ludwig Maximilian University of Munich [138] shifted the perturbation process from the input/output of the autoencoders into the latent space. This not only reduced complexity but also notably enhanced image quality. They called this solution stable diffusion, with support from the company Stability-AI.

Thus far, the intersection of perception and generation has yielded formidable methods for synthesizing images without any initial visual cues; they can be generated entirely randomly (see Fig. 2 for a couple of examples). However,

<footnote n="6">⁶https://updates.midjourney.com/.</footnote>
<figure><img src="image_4.png" /><figcaption>**FIGURE 2.** Images generated by DALL-E 3 when giving as prompt the title of this paper: "Artificial Intelligence in Science and Society: the Vision of the Universal Scientific Education and Research Network."</figcaption></figure>

imagination is inherently non-random, and for most practical purposes, a trade-off is necessary between creativity and alignment with the intended image's purpose. This raises the question of how to "condition" or "guide" the model toward generating the desired type of image.

With the wide availability of deep learning classification models, it has become easy to integrate generative models with classifiers. This allows for selecting images with a high probability of being categorized into a specific class. Consequently, users can designate one or more standard ImageNet class labels to steer the image generation process [139].

### 3) The Transformer milestone

The Transformer architecture has also affected the field of computer vision, providing an excellent solution to the previously mentioned challenge: guiding image generation according to user preferences. Whether you are a fashion designer, a magazine cover illustrator, or an altermodern painter, there are no more effective means of articulating your creative vision than through natural language. The emergence of neural language models equipped to comprehend natural language descriptions reveals an exciting new avenue for exerting precise and captivating control over image generation.

The OpenAI team achieved a groundbreaking milestone by introducing the first Transformer-based image generation model, known as DALL-E [140]. This remarkable achievement was made possible through a series of research endeavors that, driven by the success of Transformers in natural language processing, explored the direct application of this technology to image processing [141]. A pivotal aspect of this development is the concept of "image tokenization," which involves efficiently segmenting images into a one-dimensional sequence of encoded vectors. While preserving all visual features captured by CNNs in a linear token sequence can be challenging, it proves sufficient when the objective is to align images with natural language descriptions to control image generation. In the case of DALL-E, this challenge is addressed by preprocessing images with variational autoencoders, compressing a 256x256 RGB image into a 32x32 grid of tokens, each having 8192 potential values. The text component is processed during generation to generate the most likely sequence of "image tokens" as

the output. In the second version of DALL-E, the image generation aspect is further enhanced by including a diffusion model decoder, which derives its latent information from the Transformer-tokenized representation of the predicted image. This combination of a Transformer-based approach for simultaneous learning of text and related images, coupled with a diffusion model to enhance output quality and resolution, has become a pivotal factor in the ongoing advancement of generative models. Notably, Google's ImaGen Google [142] is a testament to the continued evolution of such models. Meta has made another notable achievement with the DINO model [143], a self-supervised learning algorithm for training Vision Transformers (ViTs) without needing large amounts of labeled data. DINO works by training two ViTs, a student and a teacher. The student is trained on augmented or distorted versions of images, while the teacher is trained on the original images. The student model is then asked to predict the original image from the augmented image. By doing this, the student model learns to identify and preserve the images' essential features, even when distorted.

Generative AI rapidly integrated itself into the creative toolkit of artists and designers. A pioneering moment in the art world occurred when Jason Allen's piece, "Théatre D'opéra Spatial," generated using the Midjourney tool, obtained the first artistic award ever won by an AI-created work at the Colorado State Fair Fine Arts Competition. As of September 2022, OpenAI reported that over 3,000 artists use their DALL-E AI system from 118 countries.

Unsurprisingly, the art world did not uniformly embrace this innovation with enthusiasm. Many scholars and critics raised concerns regarding potential risks to the integrity of an artist's work and the evolving concept of art and creativity. Consequently, a profound and ongoing debate has emerged, addressing the implications of generative AI from a multitude of perspectives: psychological [144], philosophical [145], and sociological [146]. This debate is intricate and multifaceted, a topic that is beyond the scope and purpose of this discussion. Instead, we believe that the sentiments of media researcher and artist Joanna Zylinska [147] eloquently capture the essence of this discourse: "It is worth remembering that the anxiety evoked by ML technology in relation to art has historical precedence. In the early 1820s, for example, it was feared that the invention of photography would lead to the death of painting. Instead, photography generated an explosion of new ways to see and create images—including painted ones".

Equally unsurprising is the regulatory world's struggle to deal with the disruptive effects of these technologies in various legal domains, as they impact multiple areas, such as privacy, copyright, civil liability, cybercrime, and disinformation. While it will take some time for these various sectors to accommodate Generative AI's specificities, the AI Act proposes harmonized rules for the design, development, and post-market monitoring of AI systems throughout the European Union's market. It uses a risk-based approach, categorizing artificial intelligence systems into unacceptable, high,
limited, and minimal risk levels. On this basis, the regulation
offers a three-pronged approach concerning Generative AI.

First, generative models will be categorized according to the risk levels identified for the AI systems into which they are implemented. Thus, they could, for instance, be prohibited (unacceptable risk) or subject to requirements before entering the market (high risk).

Second, the limited risk category is particularly relevant for systems implementing Generative AI models, and notably, its requirements can be combined with those of high-risk systems. Article 50 establishes that artificially generated or manipulated content (text, audio, image, or video) must be marked as such by the provider in a machine-readable format (e.g., via watermarks). The deployer must disclose the artificial nature of the output. At the same time, humans must be informed that they are interacting with systems such as chatbots (typically based on language models). These so-called transparency measures for limited risk systems relate to the nature of the system and its output. They are intended to promote trust, prevent manipulation and disinformation, and enable informed choices by individuals who interact directly with an AI system or are exposed to AI-generated content as a kind of informed consent [148].

Third, general-purpose AI models, including Generative AI models, will also be subject to specific documentation and copyright compliance requirements (article 53). In addition, general-purpose AI models with systemic risk will be required to assess and mitigate potential systemic risks, ensure adequate cybersecurity, and document and report serious incidents and relative mitigation actions (article 55).

C. AUTONOMOUS VEHICLES

Autonomous vehicles (AVs) are one of the simplest forms of embodied AI. They have a narrow AI goal, which is safe and efficient locomotion on roads (safe and efficient driving). The vision of self-driving vehicles has notable precursors in the European "Prometheus" project from the early 1990s and the US DARPA Challenges (2005-2007). As a form of embodied intelligence, AVs have a body that must respond to environmental stimuli while pursuing long-term navigation goals. Given the definitions we have provided in Sec. II, they are examples of agents that require cognitive abilities [149] (and are potentially suitable for consciousness, according to what we posited in Sec. II).

The dominant sensorimotor architecture (Fig. 3, top) of today's AVs follows a traditional control engineering approach consisting of three sequential functional blocks [150]: 1) a perception block that creates a symbolic "representation" of the road environment, 2) a decision block that plans a behavior by reasoning over the symbol set, and 3) a control block that actuates the behavior through lower-level control loops.

The definition of the atomic symbols for the output of
the perception layer is made by human designers. Thus, al-
though the perception layer may contain large trainable neural
networks that classify sensor data into different entities, the

<figure><img src="image_5.png" /><figcaption>**FIGURE 3.** Top: the sense-think-act architecture used for self-driving systems. Bottom: the end-to-end architecture. Both are functions that map sensory data to control actions. The systems will always respond in the same way to the same input (except for noise) and cannot be considered as having conscious internal initiatives.</figcaption></figure>

output classes are predefined and fixed. If an entity that does
not belong to the predefined classes occurs, it is forced into
the closest class or ignored. That was the source of a fatal
accident involving a UBER car that hit a pedestrian who
was walking with a bicycle, as such a pattern could not be
recognized into a predefined class by the vehicle's perception
system [151], [152]. A review of AI techniques for crash
prediction may be found in [153].

Then, following the choice of the symbols for the world "representation," engineers design the algorithms for the decision block. From an engineering perspective, this is expected to guarantee that the systems are verifiable and that their safety and performance can be guaranteed. Unfortunately, the complexity of the situations that a self-driving agent may face generates complex rules and algorithms –see, e.g., [154], [155].

The two shortcomings above may explain why the recent
development of autonomous driving technology is impeded
by the continued emergence of many edge and corner cases
[156]–[159], with a long tail of unexpected situations in
which cars are unable to act [160] or act dangerously [151],
[152].

From the perspective of AI, self-driving vehicles engi-
neered with the sense-think-act architecture look closer to
complex control systems rather than genuine artificial intel-
ligent agents. They are programmed by human beings and
evolve only with human re-coding. As such, these systems
are deterministic controllers. Their output depends on the
input and may vary only because of noise in the perception-
action chain. One can consider these systems to possess some
cognitive abilities. Still, they are not conscious, have no “free
will,” and will not have any self-initiated goal other than
responding in a programmed way to the environment and
vehicle states.

As an alternative to the sense-think-act architecture (Fig. 3, bottom), NVIDIA demonstrated a lane-keeping function
trained end-to-end, given raw sensory input and steering examples from an expert human driver [161]. However, in the following studies, Waymo stated that “simple imitation of a large number of expert demonstrations is not enough to create a capable and reliable self-driving technology” and studied a perturbative approach to creating synthetic data that help to generalize self-driving abilities [162]. These efforts witness the attempt to explore different cognitive architectures, which are discussed next. Although the implementation is different, the following systems are still simple functions that map input to output and are unconscious.

## D. ROBOTICS AND HUMAN-ROBOT COOPERATION

Compared to autonomous vehicles, robots are more complex forms of embodied AI. They may have a more comprehensive set of AI goals involving abilities such as manipulating objects (for example, the dexterous manipulation of tissues is exceptionally challenging), locomotion in completely unstructured environments, and interaction and cooperation with humans.

A significant difference between embodied intelligence and LLM is that embodied robots can act on the environment and observe the consequences of their actions. In this way, they can explore the world in autonomy via generalized motor babbling and construct predictive models of increasing abstraction levels. This gives embodied AI the ability to refine behaviors, acquire new behaviors, and, in principle, new goals. If a robot develops selfish goals and behavior, the gap between a cognitive system and consciousness shrinks, albeit it is hard to say whether this mechanism alone may be sufficient to reach consciousness. Below is an example of a robot capable of extending its sensorimotor system. However, let us first recall precursor cognitive architectures.

The subsumption architecture [163] generates behaviors using perception-action loops organized hierarchically. It starts from simple behaviors at the bottom and creates more complex behaviors at higher levels by subsuming lower levels. The topic of action selection, i.e., which of the many behaviors created in parallel by subsumption architectures takes control of the agent, was studied by Prescott [164], indicating that a centralized switching mechanism is preferred. With centralized action selection, the system is scalable: new behaviors can be added to the stack (or learned). As long as they provide the behavior reward on a standard currency scale, they become immediately available for selection to the agent. Adaptive behaviors and learning of new behaviors are obtained seamlessly with layered control architectures (the name given to subsumption architectures with centralized action selection).

Notably, layered control architecture in robotics parallels the affordance competition hypothesis in natural cognition [165], where the brain’s “dorsal stream” responds to affordances by instantiating potential actions in parallel. Then, the basal ganglia selects one using a competition process based on estimating their rewards.

<figure><img src="image_6.png" /><figcaption>**FIGURE 4.** A cognitive architecture that can learn models of the world and use them to self-extend its sensorimotor repertoire.</figcaption></figure>

Layered control architectures have two primary learning loci: one is learning biases in the action selection process, which can be used to steer the behaviors according to long-term goals [166]. This process may be realized through reinforcement learning or other methods. The second learning mechanism consists of expanding the subsumption stack, which can happen in several ways. A promising approach is generative AI: the perception-action loops are made of generalized autoencoders with various branches, which resembles the organization in Convergence Divergence Zones proposed by Damasio [167]. With generative AI, different objects are encoded separately in the latent space, avoiding using predefined symbols. The symbols are instead learned. These symbols are often qualified as “modal” because they are linked to the states of the neural convergent and divergent zones that produce the particular patterns in the latent space. Re-activation of those states can reconstruct the corresponding input or predict corresponding motor behaviors (similar to image perception and generation but declined to sensorimotor control). Learning the evolution of modal symbols across time corresponds to learning predictive models. In turn, predictive models can be used to imagine hypothetical events and derive novel action strategies [127], [168].

A final benefit of the layered control architecture is emergent human-robot interaction [169]. This relies on a modified version of action selection where actions are selected according to the similarity with the activity of a human with which the agent intends to cooperate.

An example of a robot with the above capabilities is the self-driving agent developed in the EU-funded project Dreams4Cars<fnref n="7" />. The robot sensorimotor architecture is shown in Fig. 4, superimposed on the outline of a brain, inspired by Cisek's affordance competition hypothesis. The red arrow is the primary perception-to-action loop. It creates many potential actions by responding to affordable actions offered by the environment. The action selection loop selects one of them via a competition process and gates it to the motor system.

<footnote n="7">7 www.dreams4cars.eu</footnote>
In this way, the agent achieves basic adaptive behavior [170]. However, by noting the consequences of actions, the agent can learn models of the world (the violet feedback loop and the red arrow in the inverse direction). These models have various uses [118], [171]. Among them, learned models of the world can be used to simulate the real world mentally (without engaging in dangerous actions) to develop action strategies safely. Once created, these new sensorimotor behaviors can be incorporated into the primary action priming loop and extend the agent's capabilities [172], [173]. Alternatively, biasing strategies can be developed to steer action choices towards those more beneficial for the agent [174] (this is where hypothetical consciousness might control the agent).

Concerning the purpose of the present paper, we note that, compared to the self-driving agents of Fig. 3, several feedback loops now allow the agent to self-extend its capabilities. Hence, while at any given time, an agent always responds in the same way to external stimuli (hence, it is not conscious), with time, it can evolve its abilities and improve its ways of dealing with problems. The agent evolution was supervised in Dreams4Cars by human operators who decided which scenarios should have been analyzed. However, in principle, an agent could explore hypothetical scenarios randomly and –without supervision– we cannot exclude that the agent develops selfish (but dangerous for humans) behaviors. Hence, some mechanism for approval of learned behaviors should be set. Compared to humans, artificial agents can also share learned behavior in a much easier way. This may be useful (for example, a population of cars can learn a safer behavior from one rare, dangerous event that happened to one agent). Still, it can also create the risk of quickly spreading undesirable behavior.

Thus, from the perspective of artificial intelligence, the robotics cognitive architectures described above are capable of different forms of learning. Hence, they can evolve autonomously. The agents have cognitive states. However, given the definition we provided in Sec. II, the simple possession of cognition does not imply such agents are conscious. Nonetheless, guaranteeing ethical and safe self-evolution is an open point.

## E. MULTIMODAL SYSTEMS AND AGI

In recent years, multimodal systems have emerged as a critical area of focus within AI research. These systems concurrently utilize multiple input or data types (e.g., text, image, video, audio) to make more nuanced interpretations, decisions, and predictions. Key advancements include image captioning, visual question answering, and audio-visual speech recognition.

Various architectures have been developed to tackle multimodal tasks as part of the continual innovations in AI. We mention a few of them below.

* Multimodal Transformers. Recent technologies have seen an extension of transformer-based models from the realm of Natural Language Processing (NLP) into the multimodal domain. These models are proficient at dealing with multimodal data using self-attention mechanisms that enhance their learning capabilities from multiple data sources.

* The Late Fusion CNN-RNN model is a hybrid architecture for video classification tasks. The model employs a CNN for extracting frame features and a Recurrent Neural Network (RNN) for sequentially encoding those features. The model's architecture allows it to process videos in a manner that accounts for both the visual content (CNN) and the temporal dynamics (RNN).

* ViLBERT (Vision-and-Language BERT) is a model architecture designed for tasks that require understanding both visual and textual content. It features two parallel BERT (Bidirectional Encoder-Representations from Transformers) models, one each for vision and language, connected through co-attentional transformer layers. This architecture allows effective communication between the textual and visual streams.

* Developed by Facebook AI, MultiModal Framework (MMF) is a multimodal research framework to facilitate the development of vision and language models. It is used in models like VisualBERT and MMBT (Modular Multimodal BERT), which effectively combine text and image data for various tasks such as image-text matching and visual question answering.

* LXMERT (Learning Cross-Modality Encoder Representations from Transformers) is another prominent example of multimodal transformer models. It accommodates visual and textual data, possessing separate encoders for each and a cross-modality encoder for integrated processing.

The architectures mentioned above underline the potential held by multimodal systems in advancing AI's capabilities. However, despite these advances, finding an effective way to synchronize data from different modalities continues to be challenging, driving ongoing innovations in this area.

### 1) Limitations and improvements

Despite the progress shown by the above systems, challenges persist. For instance, integrating heterogeneous data and synchronizing different modalities remain significant hurdles. While the field has seen impressive growth, there is still much room for improvement to advance models' accuracy, context understanding, and real-time performance in complex, real-world environments.

### 2) Artificial General Intelligence

The ultimate goal in AI research is to achieve artificial general intelligence, an AI system with generalized human cognitive abilities to understand, learn, and apply knowledge across a broad range of tasks. Despite substantial progress in the past few years, AGI remains hypothetical, with tangible examples yet to surface in the real world. While multimodal systems are a steppingstone towards developing AGI, it is critical to note that AGI's realization involves addressing challenges beyond technical feasibility, including ethical implications, societal
readiness, and regulatory landscape adaptation. Wreturnck to
these issues in Sec. V.

3) The non-axiomatic reasoning system (NARS)

NARS, or the Non-Axiomatic Reasoning System, is a model
designed to embody a theoretical understanding of intelli-
gence that is abstract enough to apply to human cognitive
processes and artificial intelligence systems. This model is
distinctive because it does not focus on specific application
problems but aims to provide a general-purpose system that
can handle various tasks, including those not anticipated by
the system or its designers [175], [176].

NARS is intended to be an Artificial General Intelligence (AGI) system. It is designed to abstract intelligence from human intelligence, focusing on the ability to adapt and function in a wide range of situations, not limited by the specific biological characteristics of human brains. The system is structured to handle tasks and problems that are not predefined or anticipated, making it a general purpose. This aligns with the goals of AGI, which aims to create systems that can perform any intellectual task that a human being can, rather than being confined to narrow, specialized tasks typical of many contemporary AI systems.

The architecture of NARS emphasizes adaptability, re-
silience to insufficient knowledge and resources, and the
capability to learn from experience, all of which are key
attributes desired in AGI systems. It does not attempt to
model human thought processes or learning methods directly;
instead, it develops its problem-solving strategies based on
its interactions and experiences, further underlining its align-
ment with the broader objectives of AGI.

The core principle behind NARS is the “Assumption of In-
sufficient Knowledge and Resources” (AIKR), which posits
that any intelligent system must operate under limited knowl-
edge and constrained resources. This leads to a system that,
rather than striving for perfect or optimal solutions, prioritizes
flexibility, adaptability, and originality. NARS uses a unique
approach to reasoning based on experience and adapts over
time, reflecting the system’s ongoing interaction with its en-
vironment. This model contrasts with traditional AI models
that often rely on fixed, pre-programmed knowledge or purely
statistical learning mechanisms [177].

In summary, NARS represents a significant departure from
conventional artificial intelligence paradigms by emphasizing
an experience-based and adaptive reasoning process. Its flexi-
bility may be the key to avoiding many potentially dangerous
behaviors that an autonomous agent endowed with superin-
telligence might otherwise develop.

F. BRAIN-COMPUTER INTERFACES

Brain-Computer Interfaces (BCI)—also known as Brain-
Machine Interfaces) are systems that can translate observed
brain activity into commands or messages for interactive
applications [178], [179]. A typical example of a BCI is a
system that enables users to move a cursor on a computer
screen towards the left or right, simply by imagining move-

ments of the left and right hand, respectively. Such imagined
movements can be recognized from the user's brain activity,
typically measured by ElectroEncephaloGraphic (EEG) elec-
trodes placed on the user's scalp. BCIs might find numerous
applications, including enabling (severely) motor-impaired
users to control assistive technologies, e.g., spellers, power
wheelchairs, or prostheses; for real-time mental state mon-
itoring and adaptive interaction (e.g., to adapt the level of
automation of an autonomous system to the users' mental
workload); or for motor and cognitive rehabilitation, among
others [180].

1) Artificial Intelligence in BCI designs and applications

Many BCI systems are based on AI [181], [182] and have
been so since some of the first pioneer BCI designs [183].
Indeed, BCIs notably use Machine Learning classification
algorithms to recognize the users' EEG patterns associated
with a given mental state or intention, e.g., an imagined left-
hand movement or a high cognitive workload (a.k.a. men-
tal efforts). Most BCI designs are based on so-called shal-
low classifiers (e.g., Linear Discriminant Analysis or Sup-
port Vector Machines) [181]. Still, like many other fields, a
rapidly increasing number of studies and works now explore
Deep Learning for EEG classification and BCI designs [182],
[184]. However, it should be noted that, contrary to other
domains such as image or speech recognition, there has not
been, at least not so far, a Deep Learning revolution in BCI.
Most international brain signal classification competitions are
not won by Deep Learning methods, probably due to a lack of
large brain signal BCI databases, but by so-called Riemannian
Geometry algorithms [185]–[187], which manipulate EEG
signals represented as covariance matrices. This may change
in the future, as several efforts are made to gather large EEG
data sets by combining the efforts of multiple laboratories
[188] or by finding ways to reuse various existing data sets
together [189]. Finally, more recently, AI algorithms have
been used not only for EEG and brain signals classification
but also in the design of the BCI-based application itself,
typically to adapt dynamically, in an automatic and intelli-
gent way, the application or interface to the users’ mental
states. This can, for instance, enable to automatically adapt
the level of automation assistance using Partially Observable
Markov Decision Process (POMDP) [190], [191]; to propose
a personalized sequence of training exercises using Intelligent
Tutoring Systems (ITS) [192], [193]; or to learn the users’
long-term goals and a model of the users using Reinforcement
Learning (RL) [194], [195].

2) Applications of AI-based BCIs

This section presents a brief overview of the various appli-
cations of BCI to illustrate its potential and capabilities. It
does so by distinguishing invasive BCIs, which measure brain
signals from within the skull or even within the brain, using
implanted electrodes, from non-invasive BCIs that only use
sensors placed on or around the skull [178]. Indeed, these two
BCI categories have different capabilities – sensors implanted
in the brain naturally have much better signal quality and information – but they also target different user populations and have more significant risks.

During the last few years, there have been several impressive developments in invasive BCIs, notably those that are based on chronically implanted brain sensors, such as Micro Electrode Arrays (MEA) implanted in the brain (the more invasive sensors) or ElectroCorticoGram (ECOG) implanted below the skull but on top of the brain (thus less invasive). These chronic implants enabled long-term data recording from a given patient, leading to large amounts of data available for AI algorithms and long-term user training, which can also significantly improve BCI performances and capabilities. Indeed, BCI control is a skill that needs to be learned and trained [196]. In the domain of neuroprosthetics, invasive BCIs have notably enabled tetraplegic users to control robotic arms with up to 10 degrees of freedom with MEA [197], [198] or to control a full exoskeleton with up to 8 degrees of freedom with ECOG [199]. Combining invasive BCI with invasive muscle stimulation or brain stimulation to provide artificial sensations also improved the control over neuroprosthetics [200], [201]. Invasive BCIs have also recently made impressive progress in BCI-based communication, with some works enabling an implanted BCI user to spell up to ninety characters per minute, using brain activity only, by imagining the gestures done to write these characters [202]. Even more recently, direct speech decoding was demonstrated, with a spelling speed of 62 words per minute with MEA [203] and 78 words per minute with ECOG [204].

Despite less impressive results due to lower quality signals that are also less informative, non-invasive BCIs, notably those based on EEG, are much more popular and more studied. They are also more likely to be usable by many users since they do not require surgery. The most promising applications of non-invasive BCIs can be gathered in three main categories: assistive technologies [205], Motor and Cognitive Rehabilitation [206], and NeuroAdaptive Technologies (NAT) based on mental state monitoring [207]. Note that there are also works in which non-invasive BCIs can be used for direct control of applications for healthy users, e.g., to control video games [208]. However, the current (un)reliability of non-invasive BCIs (e.g., current EEG-based BCIs that recognize left or right imagined movements make about 20-25% of errors on average, while between 10% to 30% of BCI users cannot use current EEG-based BCIs [209]) make them unlikely to be used in practice outside the lab, at least in the short term.

In terms of assistive technologies, EEG-based BCIs have been used to control wheelchairs (to go forward, turn left or right by imagining, e.g., foot, left-hand or right-hand movements respectively), simple prosthetics (e.g., opening or closing a prosthetics hand), and spellers [205]. The latter is generally based on so-called reactive BCIs [210]. With such reactive BCIs, different letters are displayed on the screen, and a specific visual stimulus is usually overlaid on each letter (a different one per letter). Suppose the user pays attention to

<figure><img src="image_7.png" /><figcaption>**FIGURE 5.** Illustration of a BCI being used to monitor User eXperience (UX) from EEG signals to assess and adapt 3D interaction tasks accordingly (©Inria / Photo H. Raguet).</figcaption></figure>

one such stimulus. In that case, this evokes a stimulus-specific brain response that can be detected in the user's EEG signals to select the corresponding letter [211]. Latest developments in EEG-based BCI spellers enabled spelling speed at a rate of up to thirty-five error-free characters per minute [212], [213], and have become commercial products, e.g., Intendix from g.tec<fnref n="8" /> or MindAffect<fnref n="9" />.

Regarding motor and cognitive rehabilitation, non-invasive BCIs are notably promising for post-stroke motor rehabilitation [214]. Indeed, patients who suffer from a stroke may be paralyzed in a limb due to a stroke lesion in the corresponding brain area. Such paralysis may prevent the user from benefiting from physical therapies. However, since BCIs can detect motor intention and imagination from EEG signals, they can be used to guide the patient to use the damaged brain area, even if they are unable to move the corresponding limb: dedicated feedback, e.g., visual feedback of a 3D hand moving on screen or tactile input on the paralyzed hand, can be provided when the BCI detects a movement intention. This can stimulate brain plasticity in the damaged brain area and thus help towards recovery. Recent research and meta-analyses have shown that BCI-based post-stroke motor rehabilitation is clinically effective (in complement to traditional therapy), and it leads to better rehabilitation than motor imagery or functional electrical stimulation therapy alone [214]–[216]. Currently, further applications of BCI-based post-stroke rehabilitation are being investigated, including post-stroke speech rehabilitation [217] or cognitive rehabilitation [206].

Finally, non-invasive BCIs can be used for real-time mental state monitoring (e.g., see Fig. 5). Indeed, these instruments can monitor cognitive, affective, or conative (i.e., related to motivation) states, such as mental workload (related to mental efforts), attention levels, valence or arousal (emotional states), error perception or curiosity [210], [218]–[221]. Naturally, recognizing such mental states is far from perfect, as always with BCIs, with classification accuracies in the range of 60%

<footnote n="8">⁸https://www.gtec.at/</footnote>

<footnote n="9">⁹https://www.mindaffect.nl/</footnote>
to 90% to distinguish two mental states (e.g., curious vs non-
curious or perceived error vs no error). Monitoring such states
opens the door to many promising applications, so-called
NeuroAdaptive Technologies (NATs), that dynamically adapt
to the user's mental state. For instance, NATs can provide
optimal sequences of training exercises adapted to the mental
efforts of the user for education. They can also be used for
neuroadaptive gaming, to adapt the difficulty of the game to
the attention or stress of the user, or for intelligent cockpits,
to adapt the information provided in the cockpit to the pilot's
attention or mental workload, to ensure they do not miss alarm
or relevant information [207].

3) Potential benefits and risks of AI-based BCIs

BCIs are very promising for many applications, as they
could enable severely motor-impaired users to regain some
autonomy and independence, thanks to BCI-based assistive
technologies, notably for prosthetics and speller control. They
are also promising for the rehabilitation of stroke patients to
improve their motor, speech, or cognitive recovery. Finally,
mental state monitoring can enhance education efficacy and
efficiency, entertainment quality, or safety in critical environ-
ments.

Naturally, AI-based BCIs are not without risk either. Since
they can monitor brain activity and adapt the interaction with
users accordingly, they might be used for ethically debat-
able applications when not for applications that should be
forbidden altogether. For instance, BCIs can be (and are)
used for neuromarketing, possibly for tailoring the adver-
tisement shown to users according to their preferences de-
coded by a BCI, thus influencing –possibly without the users’
knowledge- what they will buy. Similar approaches could
influence political preferences or, in general, unknowingly
manipulate users. To the best of our knowledge, there are
no reports in scientific publications that this is feasible in
practice, but this seems theoretically a possibility. BCIs could
also monitor workers or students (e.g., are they working hard
enough based on their estimated mental workload?). Again,
these are only possibilities but not realities so far, but these are
risks one needs to consider. In general, data privacy is a con-
cern for BCI. Responsibility is another concern: when using
an AI-based BCI to control an application if this application
(e.g., a wheelchair) creates an accident, who is responsible?
These questions and others are ethical and legal questions that
would need to be debated and answered [222].

Interestingly enough, the OECD has developed guidelines
for responsible innovation in neurotechnologies<fnref n="10" />, which in-
clude BCIs. Such policies are currently being implemented in
the various OECD states. This is a first step towards limiting
the risks of AI-based BCI use. However, these are only guide-
lines which are non-constraining. Actual laws are probably
needed, such as the neuro-rights that Chili implemented in its

constitution<fnref n="11" />. The recent EU AI act also aims at regulating
affective computing, to which some passive BCIs are related
[6].

IV. AI IN SCIENTIFIC RESEARCH

In this section, we examine the state-of-the-art application
of AI technology to a selection of scientific research areas,
focusing on the crucial elements that are reshaping the field
and will drive innovation in the future. In so doing, we also try
to identify the most promising and the most dangerous aspects
of this impending revolution. Consideration of the latter, in
particular, will inform a discussion aimed at imagining pre-
emptive measures and possible areas of intervention where
we may successfully mitigate or avert the threats posed by a
fully unsupervised and unconstrained diffusion of AI.

A. PHYSICS

Physics is traditionally a field of scientific investigation where
new ideas and methods emerge and develop only after a
difficult gestation period. During that transition, the commu-
nity goes through a multi-stage process that sometimes may
last for a long time. This inertia exists because, in general,
physicists are trained to automatically doubt and ruthlessly
question new claims and revolutionary ideas. They acquire
that attitude by working in a field of science that moves slowly
and where genuine revolutions are rare. At the same time, ill-
supported or made-up claims of disruptive innovations come

<figure><img src="image_8.png" /><figcaption>FIGURE 6. The Higgs boson signal (red histogram) overlaid to known processes involving the production of a Z boson and other particles that could mimic the signal characteristics in the “golden” four-lepton final state, extracted by the CMS experiment from 10.31 inverse femtobarns of 7- and 8-TeV proton-proton collisions produced in 2011-2012 by the Large Hadron Collider. Experimental data are shown by black dots with vertical uncertainty bars; blue and green histograms show expected backgrounds.</figcaption></figure>

<footnote n="11">¹¹https://en.unesco.org/courier/2022-1/chile-pioneering-protection-neurorights.</footnote>

<footnote n="10">¹⁰https://www.oecd.org/science/recommendation-on-responsible-innovation-in-neurotechnology.htm.</footnote>
in at a steady pace. In such an ecosystem, it pays off to err on the side of caution and align with the establishment.

At what we might call “stage zero,” a new idea (either a new physics theory, a new instrumental technique, or a new method to extract inference from data) does not receive enough attention to even be worthy of a reaction by the most influential members of the community: it may then remain for a significant amount of time a topic that is only considered and investigated by the few researchers who brought it up in the first place. At this stage, it may be very difficult for the proponents to get the person-power and the resources required to develop their ideas, acquire funding to increase the effort, or publish interim results. Stage one in the path of acceptance takes place when the new idea is finally brought to the attention of a significant number of academics, e.g., by being presented at an international conference or published in visible publications, at a level at which it cannot be ignored any longer. The academic establishment is then likely to express an evaluation or a reaction. The reaction is almost universally one of general skepticism; it may include direct attacks that leverage weaknesses in the less well-developed aspects of the proposed idea, ignoring its strong suits. Proponents at this stage may struggle in the uphill fight to acquire a sufficient consensus from their peers and often get marginalized or even discriminated against<fnref n="12" /> [223]. Only when a sufficient part of the community is convinced of the soundness of the new proposed innovation does a transition occur to “stage two,” when the idea may be explored with significant resources and start to attract funding and the interest of young researchers. Yet even then, it is quite common for a good part of the community —typically represented by the old-schoolers— to remain critical or even openly opposed to the innovation. Eventually, old schoolers graciously tone down their criticism or simply retire or die. That is stage three, when the new idea and its consequences revolutionize the field.

## 1) The past

The general multi-stage path outlined above is not dissimilar to the slow-motion rise and adoption that artificial intelligence methods saw in physics research. We may take particle physics as an example to examine such developments. Particle physics, a field marked by a century of intertwined scientific progress and technological advancements, seems ripe for innovation. Despite this, efforts to leverage the rapidly growing computing power that became gradually available at the end of the 20th century faced significant challenges. During this “stage 0”, indicatively starting in the 1980s [224]—

[226], only a small number of researchers experimented with the new complex algorithms and methods for data analysis that had been made available by recent advancements in computer science and statistics—including statistical learning methods and neural networks; for a review of early studies see [227]. Those pioneering attempts were typically not backed up by the collaborations running large collider experiments. Entering the 21st century, the decade which we may associate with a “stage 1” according to the schematization outlined *supra*, there was a gradual increase in studies of what we now recognize as machine learning techniques, such as decision trees, random forests, and gradient boosting; yet despite this growing interest, the path to publication of analysis results that made use of those techniques remained arduous and uncertain, mainly due to the scepticism of part of the scientific collaborations producing the results, every member of which had the power to request further studies and checks *ad infinitum* before submission to a scientific journal could be made.

Finally, in 2012, a true paradigm shift happened, and “stage 2” started. The ignition point was the discovery of the Higgs boson (see Fig. 6) [228], [229], for which the ATLAS and CMS collaborations at the CERN LHC collider employed machine learning techniques [230]. This coincided with the rise of machine learning in other disciplines, mainly driven by the success of algorithms for image classification in achieving and surpassing human performance. Still, it took a few more years to reach what we might recognize as “stage 3”: old-fashioned academics kept questioning for a little more time the indeterminate nature of the mechanisms inside the “black box,” the unknown way by which neural networks take their decisions when applied to the classification or regression tasks which are common in particle physics data analysis.

Today, the question, in particle physics as well as in other adjacent fields of fundamental science investigation (including astronomy and astrophysics, nuclear physics, and related physics phenomenology areas), is not any longer the way neural networks—which have grown “deep” even in the most straightforward applications and settings—operate their decisions. Rather, the question now is how to fully exploit the latent potential of the large arsenal of tools developed in computer science and how to customize, extend, and improve the functionality of those new tools for the specific tasks demanded by the investigation of frontier physics. The revolution has fully taken place, and what twenty years ago had moved from being labeled as “statistical learning” (with an emphasis on the properties of the estimators those algorithms could produce) to more properly “machine learning” (with an emphasis on the adjustment of algorithms to the specific learning task, and a focus on their performance), is starting to be called without restraint with the broader umbrella term of artificial intelligence.

## 2) The present

Artificial intelligence is used today in fundamental physics to improve the performance of experiments, to boost the pre-

<footnote n="12">¹²A fitting example of this situation is the “dynamical likelihood” technique developed by Kunitaka Kondo and collaborators in the late 1980s. The method was meant to apply to data analysis in the CDF experiment at the Fermilab Tevatron collider, which (among other things) was searching for a signal of the sixth quark, the top. Despite its soundness, the dynamical likelihood method was never accepted for application in CDF analyses, and over a decade had to pass before the technique was accepted as a highly performant, correct method and was finally employed to measure the mass of the top quark. It subsequently became a standard weapon in the toolkit of particle physicists.</footnote>
cision of measuring instruments, and to achieve sufficiency
<fnref n="13" /> of summary statistics employed in the analysis of the
complex, multi-dimensional data usually acquired by those
instruments. In addition, AI is used by phenomenologists
to simplify the investigation of large-dimensional parameter
space or to improve the potential of theoretical calculations.
The gauge of a completed paradigm shift is observing the
path of a particle physics analysis to publication, operated by
a large collaboration. As we mentioned *supra*, only fifteen
years ago a study using a neural network technique would
be questioned harshly and would not be allowed to proceed
to submission for publication until extensive, excruciatingly
detailed, and deep cross-checks were performed. In contrast,
today, an analysis result that does not employ a neural net-
work or a similarly powerful supervised learning technique
for data reduction and inference extraction will be automat-
ically considered suboptimal, questioned for the reason of a
lack of optimization, and often regarded as unworthy of being
published.

The above paradigm shift is fueling a sociological effect within the scientific community. Young researchers who start their path as Ph.D. students in physics and dedicate themselves to the extraction of measurements from experimental data are quickly catching the message that they either become experts in those computer science tools or had better move on to some other career path. The result is that physics knowledge is losing value to computer science skills, even within the walls of academia. The long-term outcome of this trend remains to be seen. Still, in the meantime, we can observe how major universities worldwide have promptly reacted to the shift in demand and have started to tap the throughput of bachelor courses in Physics to hire students. The new term “Physics of Data” is being used to advertise or directly name new master courses that focus on computer science concepts and training at least as much as they focus on advanced physics. The message is that to be a good physicist, you must know computer science well today.

Meanwhile, the exploitation of deep neural networks and
supervised learning has gradually become only one of the
activities on which experiments base the production of their
results, as new powerful methods (normalizing flows, trans-
formers, autoencoders) and, in general, unsupervised learning
methods have flanked it and gradually grown in importance.
Following what is happening elsewhere, deep neural net-
works are being replaced with the more general concept of
differentiable programming, which is the true engine under
the hood of neural networks: the capability to backpropagate
the gradients of a loss function to optimize the network
parameters. Differentiable programming is offering itself for
the solution of tasks that, until yesterday, were not even
considered as approachable. Here, we mention two of them.

The first novel application concerns generative models. In fundamental physics, the mechanisms responsible for data generation in any relevant instrument are intrinsically stochastic –two protons hitting the same block of matter with the same energy, e.g., will never produce the same chain of reactions. The differences are so stark that the problem is not amenable to solution by writing down a likelihood function of the parameters of interest given the observed data: it is intractable by those means. In such situations, inference can be operated by comparing observation with simulated data that samples the space of latent parameters governing the physics of the system under study; the procedure is captured by the term “simulation-based inference” or “likelihood-free inference.” In recent years, new unsupervised learning methods (in particular, normalizing flows, autoencoders, and adversarial networks) have become available to produce generative models that greatly simplify the task of scanning the latent space and extracting precise inference from the stochastic input data. These techniques are thus revolutionizing the analysis of large bodies of stochastic data in fundamental science applications.

The second task AI technology enables is the end-to-end
optimization of the instruments conceived by researchers
to further their knowledge of fundamental science [231].
Large particle physics or astroparticle experiments involve
the design of instruments of such complexity that the only
viable option for the study of their potential performance has
until now been the discrete sampling of a few “reasonable”
configurations informed by experience and the comparison
of their performance on a proxy of the actual set of wide-
ranging goals of the experiment. This procedure is virtually
certain to miss consideration of innovative design solutions
that leverage the interplay of any number of the many inter-
related construction parameters at play.

The above discrete sampling can be revolutionized by artificial intelligence, exploiting differentiable programming in the construction of a complete model of all the functional blocks of the pipeline connecting the data collection and interaction with the detector with the pattern recognition performed on the resulting detector response and with the successive information extraction procedures, all the way to the calculation of an experiment-wide “objective function” that encapsulates the true goals of the experiment, optionally including an appraisal of constraints, costs, and any other factor worth including in the recipe (see Fig. 7). The differentiable model allows for the complete exploration of high-dimensional parameter space of construction choices in a continuous way, and the identification of the global maximum of the objective function.

There is one additional important point concerning present-
day AI's effect on the collateral effects of collaborative re-
search in physics. As in other disciplines, the computing
power required for the heaviest applications is quite signif-
icant not only in itself but also as a source of carbon dioxide
in the atmosphere; it is a source that cannot be neglected any
longer when designing and operating large experiments, as

<footnote n="13">¹³Sufficiency is a property of a statistic (a function of the data) that retains all the information about the parameters of interest. A sufficient statistic provides an efficient summary of the data, often by reducing its dimensionality, without losing information relevant to the estimation or inference of the parameters.</footnote>
<figure><img src="image_9.png" /><figcaption>**FIGURE 7.** Sketch of an end-to-end modeling pipeline encoding all the functional elements that connect the design elements of a detector to the physical processes taking place in it during data taking, the pattern recognition and inference extraction procedures operated on the resulting data flow, and the final computation of a global utility function. Suppose the model is differentiable (e.g., thanks to creating a valid surrogate model of the stochastic processes generating the data, left). In that case, the update of detector parameters (top left) following the gradient of the utility function (top right) allows the instrument to be completely optimized. Boxes labeled "H" indicate where hybrid digital-neuromorphic computing can be integrated into the procedure.</figcaption></figure>

it may exceed by orders of magnitude the emissions due to operation of the experimental complex. For example, analyzing the data produced by high-energy collider experiments requires not only the collection, reconstruction, and storing of collision data but also the concurrent production and use of massive datasets produced by high-fidelity simulations of the physical processes and the apparatus. The ATLAS collaboration recently surpassed the mark of one million 100% active CPU cores employed for their computing effort –a large part of which is used to generate simulated data. As we move to higher-intensity machines (such as HL-LHC, the already approved high-luminosity version of the Large Hadron Collider, which will deliver ten times higher rates of collisions), the impact on our environment of these experimental endeavors is a concern. AI may help significantly reduce this load in several ways. One of them, which is not new, is improving the use of distributed resources by optimizing access to data and computing. Another is the demonstration of the validity of the above-cited generative models, which may eventually restrict to validation tasks the large “full simulation” datasets that today still absorb most of the computing resources of the LHC experiments.

### 3) The future

Following the two avenues mentioned above to reduce the environmental impact of large physics experiments, a third one that must be placed in the category of prospects is the development of the new paradigm offered by neuromorphic computing. Neuromorphic processors, which encode information with the time of arrival of electrical pulses in close analogy to the behavior of biological neurons (see Fig. 8), have the potential to reduce the energy consumption of computing tasks by many orders of magnitude compared to standard von Neumann computers. For example, it has been calculated that to produce the same amount of computing expressed by a

<figure><img src="image_10.png" /><figcaption>**FIGURE 8.** Illustration of the encoding and processing of signals by a neuromorphic computing system. In (a), a time-dependent signal (black curve) is processed by Lebesgue sampling to detect the time of integrated changes of its value; this results (b) in two streams of spikes (blue and red dashes) which represent positive and negative variations of the transduced signal. (c) represents the spike-based encoding of the resulting information stream for n channels representing axons; (d) offers a schematic of a spiking neural network unit with input and output channels, mimicking the functional elements of a neural element.</figcaption></figure>

human brain, which consumes about 20 watts of power, a digital computer must employ 20 megawatts [232]. In addition to those vast energy benefits, neuromorphic devices offer co-location of processing (the neuron membrane, which adds potential from the incoming signals and fires once a threshold is reached) and data (the strength of the connections of synapses to the neuron membrane). This constitutes a second disruptive improvement over present-day technology. Neuromorphic computing devices offer a perfect substrate for new artificial intelligence developments due to their natural functioning closely mimicking the biological brain.

Although the technological challenges are significant, no evident showstoppers have been identified in the development of this new paradigm, and the issues appear instead to concern details of its technical implementation. The question, therefore, seems to be not whether neuromorphic processors will be developed and start to compete with conventional digital computers in a significant number of tasks but rather when such a transition will eventually happen. Besides the huge potential of neuromorphic computing in reducing the carbon footprint of large experimental endeavors, its development is also bound to play a quite significant role in many applications for fundamental physics experiments. For example, the
<figure><img src="image_11.png" /><figcaption>**FIGURE 9.** Aerial view of the region around Geneva (Switzerland) with a sketch of the proposed location and dimensions of a future circular collider. The Large Hadron Collider (LHC) and the other smaller facilities at CERN (SPS, PS) are overlaid to the view in the lower left; the future circular collider footprint is green.</figcaption></figure>

possibility to endow particle detectors with many small in-
dependent processors integrated into the detection elements,
which perform computing operations with the output of those
elements and produce high-level summaries, thus massively
reducing the data flow to the backend, constitutes a promising
avenue of exploitation of these systems, once their commer-
cial viability and technological readiness reaches the required
threshold. Another example concerns applications where the
energy needed to operate sensors is a scarce resource (such
as in experiments at the north or south pole or experiments
in space where a large, distributed set of small, independent
units operate detection, pre-processing, and transmission of
signals powered by small-scale solar panels): neuromorphic
computing is thus poised to provide a new groundbreaking
avenue for 21st-century research.

As we point out throughout this work, artificial intelligence
is already transitioning to become a more impactful reality
in our societies and technology at an increasing pace. This
situation, dubbed one of “jolting technologies” (where jolt, a
colloquial term for the jerk, is the third derivative of position;
a positive, non-zero jerk implies an increase of the force
acting on the system, with a divergent effect that we are not
accustomed to conceive as it is alien to our physical experi-
ence), has been argued to be the signature of an impending
singularity [233]. A true singularity would be generated if
AI systems were to become capable of autonomously self-
improving themselves. This effect is nowhere near being
proven at the time of writing. Yet, the impact of this evolving
situation is very significant in fundamental physics research
because of the large scale and the large time of conception-
to-commissioning of the big science experiments required to
further our understanding of fundamental physics and of the
universe.

For example, a new future circular collider (FCC) has been
proposed as the machine that should receive the baton from
the Large Hadron Collider after the 2040s, when the HL-LHC
phase will have reached its natural termination point. If an

FCC or a similar gargantuan-scale machine (see Fig. 9) will
be built, it will obviously be entirely based on artificial intel-
ligent systems for the functioning of all its parts (accelerating
chain, beam control systems, detector operation, data collec-
tion and reduction, event reconstruction, digital twinning and
simulation, inference extraction). What is more to the point,
though, is that in accordance with the wish to have such a
machine ready not much later than the end of operation of
the LHC, the community has already started design studies a
few years ago. Sitting on a steep slope of technological im-
provement, however, is not a very comfortable position to be
in if we are to make crucial decisions on design, allocation of
resources, and other thousands of important choices that play
a role in the success of a multi-decennial machine. Therefore,
the present situation embodies a misalignment risk. This is not
the kind of existential misalignment risk between the goals
of humans designing a general artificial intelligence and the
goals that the system may decide to set for itself once it is
operative (we discuss that situation *infra*, see Sec. V), but
it is still a very serious concern. Suppose today we design
a machine that will be commissioned and start to take data
only twenty or thirty years from now. How can we ensure that
we are not making the wrong choices and end up investing
efforts and resources in hardware that will be unfit to be
best exploited by the artificial intelligence tools that will be
available then?

A solution to the above difficulty is to take the “best case scenario” as a baseline –in stark opposition to the attitude one would be advised to take in less long-term projects of this kind. Suppose we cannot model the performance an artificial intelligence may offer thirty years from now in extracting information from generated data by the device we are designing today. In that case, our best bet is to assume that such performance will be arbitrarily large. We are helped by the fact that a ceiling exists for this task: it is the perfect, lossless extraction of sufficient summary statistics from any relevant part of the generated physical processes. In so doing, we are led to concern ourselves with operating design choices that maximize the amount of information physically generated by the system; instead, any choice we made that reduced the information produced by the raw physical processes would be liable to be regretted upon, when using a future higher-intelligence system for inference extraction.

Let us make an example to clarify the point mentioned
above. Calorimetric measurements of fluxes of subnuclear
particles exploit different mechanisms by which energetic
particles lose energy in interacting with dense media. Until
recently, calorimeters were only tasked to measure the total
energy of incident particles, and their designers did not even
attempt to extract from the interactions any usable infor-
mation on the substructure of the flow of particles crossing
the detector, let alone the exact timing of the interactions or
useful information on the particles’ identity. This construction
paradigm was shaken to its roots about twenty years ago, once
it was realized how important features could be mined in the
substructure of the generated energy flow [230]. Calorimeters
<figure><img src="image_12.png" /><figcaption>**FIGURE 10.** GEANT-4 [234] simulation of the energy release in a block constituted by a lattice of 100x100x100 calorimeter cells (of dimensions 3x3x12 mm) made of lead tungstate hit by a 100 GeV proton; displayed units are cell indices. The proton hits the center of the block in the (x,y) projected image shown above by traveling in the z-direction (orthogonal to the xy plane). The lateral diffusion of secondary particles and their energy depositions is visible in the plane orthogonal to the incident proton direction. The released energy (shown by a color temperature map) has been summed over all the cells at the same (x,y) coordinate. It is evident that together with total energy information, the fine-grained cells record information on the number and direction of secondary hadrons produced in the shower, opening the way to the extraction of more information.</figcaption></figure>

suddenly started to be constructed with a finer granularity to gain access to that substructure; experiments that had not considered that aspect at the design stage could not exploit it now. It is already dawning on us that future calorimeters should not only allow access to highly granular information on the longitudinal development of the energy release, as well as its timing (which also produces useful information for discriminating processes of different origins) but they should be built to allow for particles of different identity to manifest their different interaction properties in a detectable manner (see Fig. 10). Such has never been the purpose of a calorimeter in the past, but times are changing fast. While we have not yet been able to demonstrate that it be possible to, e.g., exploit the different localized patterns exhibited by hadrons of different species in interacting with nuclei (e.g., the different interactions undergone by charged pions and kaons hitting nuclei of different materials) for precise particle identification, we must err on the safe side and assume that such a feat will be pulled off in the future, as it is in principle possible.

Whether physicists will adopt the above “forward-thinking” attitude in designing their apparatus remains an interesting question for the coming years. Hopefully, the fast increase in the capabilities of artificially intelligent systems that we are experiencing daily in other areas of human occupation will inspire the present generation of decision-makers in fundamental science and allow them to consider the impli-

cation of that accelerated acceleration.

## B. FORMAL SCIENCES

The fields of Mathematics and Computer Science are integral to the development and evolution of Artificial Intelligence. This symbiotic relationship enhances our understanding of complex AI models and greatly influences problem-solving approaches in both AI and formal sciences. This section delves into the key intersections between these disciplines, highlighting current research directions, the role of advanced mathematical concepts, and prospects, as they collectively guide the path for AI's theoretical foundations and practical applications.

The theoretical foundations of AI are progressing at an unprecedented rate, as indicated in the survey article [235], which is based on an invited lecture at the International Congress of Mathematicians 2022. The author identifies relevant research directions at the intersection of mathematics and artificial intelligence: mathematical foundations for AI and, conversely, AI for mathematical problems. In this direction, the concept of artificial neurons is crucial for developing deep neural networks. As indicated in Sec. III, Riemann Geometry algorithms are useful in Brain-Computer Interfaces [185].

As an example of the envisioned breakthroughs in mathematics, the use of AI will be crucial to gaining insight into classical partial differential equations and inverse problems. Multi-layer neural networks for deep learning based on fractional differential equations can be used to search an optimal structure [236], and artificial neural networks can efficiently approximate high-dimensional functions in numerical approximations of the Black-Scholes partial differential equations [237].

Let us delve into other innate examples of the relationship between AI and Mathematics and the profound influence that the former is having on the progression of this field of research. The rhythm of this advancement is a well-orchestrated dance of formal sciences and AI, with each complementing and advancing the other. The intersection of AI and formal sciences paves the path for significant advancements in computational complexity, thereby improving the efficiency of learning algorithms and the development of energy-efficient AI. It also buzzes with continual refinement of optimization algorithms, forging the path for enhanced AI learning and problem-solving capabilities. Here are additional realms where Mathematics and AI converge and complement each other.

An understanding of Statistical Learning Theory is crucial for developing machine learning algorithms. Fundamental mathematical concepts like the Vapnik-Chervonenkis (VC) dimension for model complexity, Rademacher complexity to control variance, and empirical risk minimization to reduce training error fortify the application of machine learning algorithms. A recent addition to this blend is Category Theory, focusing on abstract structure and the relationships between mathematical structures. It has found exciting applications in the theoretical foundations of machine learning and AI.
For instance, composing morphisms mirrors the assembly of complex systems from simpler ones, as seen in neural network architectures.

The development and operation of AI algorithms is firmly rooted in the Optimization Method. For instance, techniques like gradient descent, linear programming, and convex and non-convex optimization methods are core to AI algorithms' training process. They play an essential role in managing model accuracy and effectiveness, ensuring an optimal solution is achieved.

Graph Theory forms a pillar for discrete mathematics in AI. Essential for path planning in robotics and strategy in games, it has applications in AI branches like social network analysis and semantic web, specifically ontology. Methods from probability theory and stochastic processes are also vital in AI. Important concepts like Bayesian networks, Markov Decision Processes, and Monte Carlo methods are indispensable for dealing with uncertainty and probabilistic decision-making in AI.

As AI systems increasingly form part of critical operations, their security becomes ever more significant. Advanced cryptographic techniques guarantee secure communication channels and protect sensitive AI system data.

While we still grapple with the fundamentally black-box nature of artificial neurons and deep learning models, Explainable AI (XAI) represents an exciting frontier [238]. It strives to make AI decisions more transparent and understandable. The necessity for an AI agency to oversee and regulate these aspects is becoming starkly evident to ensure the successful coexistence of humans and AI. Developing verifiable and accountable algorithms is paramount to confirming behaviors aligned with desired outputs. This nexus of AI and Mathematics, embracing old and new concepts alike, is key to harnessing AI's potential effectively and responsibly. The growth of this relationship is accelerating faster than ever, indicating an exciting future at their confluence.

Finally, we all are concerned with the black-box-like opacity of AI to detect biases and prevent potential harms [239]. A possibility is to generate an explainable model, a kind of “white box” proxy replicating the inputs and outputs of the original system. An AI Agency to regulate different aspects, as indicated before, is necessary. In all cases, verifiable algorithms should be implemented to monitor and complement the opaque ones.

## C. GEOGRAPHY

The application of artificial intelligence techniques to research in Geography dates back to the first geospatial and temporal studies developed by geographers to map terrain more precisely. Already in 1986, Couclelis [240] described how the application of new computing techniques would significantly impact geography. However, it took a few more decades before the power of machine learning brought to the development of entirely new applications [241]. A relevant turning point happened in 2000, when geopositioning through the signal of GPS systems became 10 times more

precise overnight, as finally applying a 1996 deliberation of the Clinton administration, the US Air Force removed the scrambling of signals from its satellites. Besides the obvious benefits it brought to navigation systems, the order-of-magnitude improvement in the precision of the localization of devices receiving GPS signals was a powerful enabler of a wealth of new applications. Today, a ground positioning precision of one meter is the standard, and new systems have been announced that will bring the precision down by two orders of magnitude.

A significant further advancement was achieved soon after the geopositioning revolution of 2000, when free availability of high-resolution imagery of the whole Earth's surface was provided to internet users, thanks to a free-distribution software (Google Earth) which was first released by Google in 2001 [242], and has since withstood regular improvements and updates.

Nowadays, the use of geospatial data for mapping, surveys, and other tasks benefits from a number of AI-powered new methods that have been enhancing research. Remote sensing and image analysis are performed with convolutional neural networks, extracting features and patterns from satellite imagery and other sources. This enables improved results in areas ranging from large-scale land cover classification, object detection, and change detection, tasks that may be routinely performed with high accuracy. Geographic Information Systems (GIS) software integrates AI tools to improve data processing, analysis, and visualization capabilities. Besides their use for geography *per se*, GIS applications are also used in various other fields, such as urban planning and transportation management. Environmental monitoring is performed automatically by algorithms that can detect deforestation, monitor wildlife populations, and assess the impact of climate change on ecosystems. In addition, large language models are used to improve data mining from textual and social media data, to understand human behavior and socioeconomic trends at different spatial scales, and to enable studies of the interplay of human activities and the environment.

While the enhancements mentioned above in our capability to harvest and process data from the surface of our planet constitute clear progress and are enablers of better planning and intervention in the environment, there are ethical and social implications arising from the diffuse use of geospatial analysis. Data privacy and algorithmic bias are two issues that apply here, and although they pre-date the application of AI technologies, the use of AI tools is enhancing their relevance.

In the context of artificial intelligence for developing geographical disciplines, a new concept was recently developed: GeoAI [243]. Theoretical advances are now boosted by data management, computer hardware and software, and the fast processing of those data with the new computers. GeoAI is today a discipline within geographical sciences devoted to developing computer programs that can imitate the human view of space and time. Then, the geographical changes, human perception, and spatial and temporal changes are researched using the GeoAI to achieve an advance of knowledge about
the status of the environment and to find solutions to the impact of humans on our planet [244]–[246].

## D. AGRICULTURAL SCIENCES

Agricultural sciences encompass the production and processing of food and fiber, involving technologies related to tillage, plant cultivation, and harvesting, as well as animal production and the processing of plant and animal products for human consumption and use. These sciences face significant challenges, particularly in finding sustainable solutions to the problem of feeding a rapidly growing global population, amidst declining arable land, water, and soil resources due to ongoing environmental degradation and climate change [247]. Agricultural farms and firms must address four main objectives: ensuring an adequate food supply, alleviating poverty, achieving better health and nutrition for a growing population, and conserving natural resources [248]. Historically, agriculture has been a driving force in economic development, playing a central role in agricultural, rural, and structural transformation. Notably, poverty remains most prevalent in rural areas where agriculture provides substantial income, employing 1.23 billion people and supporting over 3.83 billion livelihoods across all stages of the agricultural value chain [249].

Digital crop and livestock farming holds significant potential to meet future food demands [250]. The rapid advancement and diffusion of artificial intelligence (AI) technologies are poised to transform global agriculture. AI, machine learning (ML), and Internet of Things (IoT) sensors that provide real-time data for algorithms are increasing agricultural efficiency, improving crop and animal productivity, and reducing food production costs. Business intelligence research projects that global spending on smart, connected agricultural technologies and systems, including AI and ML, will reach $15.3 billion in revenue by 2025. Farming, traditionally involving numerous manual processes and stages, places immense pressure on farmers. To survive today, farmers must be experts in fertilizers and soils, crop-specific insecticides, planting and irrigation cycles, and weather effects, among other things. AI can complement these applied technologies by facilitating the most complex and routine tasks. It can collect and process large amounts of data on a digital platform, determine the best course of action, and even initiate it when combined with other technologies.

AI in agriculture promises to improve crop management and productivity by phenotyping plants, diagnosing plant diseases, efficiently applying agrochemicals, and providing site-specific agronomic advice. While AI can revolutionize agriculture, farmers need support to implement it correctly. Applying AI in agriculture on a global scale represents a promising opportunity to help farmers minimize or manage their risks to produce economically viable agricultural products.

Bannerjee et al. [251] conducted a literature review covering 100 significant contributions from 1983 to 2017, where AI techniques addressed agricultural challenges. In the 1980s

and 1990s, rule-based expert systems were widely used in agriculture. As in other fields, machine-learning algorithms have quickly taken the dominant role after the turn of the century. More recently, more advanced hybrid systems such as neuro-fuzzy systems and image processors coupled with artificial neural networks have been utilized.

### 1) Positive impacts of AI in agriculture

The integration of AI with agriculture offers numerous benefits, including the possibility to quickly and effectively analyze market demand to simplify crop selection and identify profitable agricultural products, the management of risk through forecasting and predictive analytics that may reduce errors in business processes and minimize crop failure, the breeding of crop seeds that are more likely to withstand biotic and abiotic stresses. In addition, autonomous systems may be developed to monitor soil health by diagnosing nutrient deficiencies or toxicity, protecting crops by detecting and managing pests and diseases, feeding crops by optimizing the use of irrigation, nutrients, and agrochemicals, and harvesting crops by automating and predicting the optimal harvest time.

A recent systematic review by Sachithra and Subhashini [252] explored how AI contributes to agricultural sustainability. They found that the most common application of AI in agriculture is predictive modeling for total agricultural output value, followed by harvesting applications. They also noted the increasing use of AI and image-processing techniques to enhance overall efficiency and sustainability in agriculture. In another relevant study, Linaza et al. [253] summarized recent research activities through projects that were developed and implemented in a few European countries. They defined terms and concepts related to precision agriculture, described global trends and policies fostering AI-based solutions in the agricultural sector, and reviewed the current state-of-the-art AI applications within precision agriculture. They concluded that AI technologies are instrumental in providing support for decisions for farms, as well as monitoring conditions and optimizing production. For example, they help farmers fine-tune the inputs for each crop. These improvements have positive outcomes in water use reduction, and they may also mitigate the emission of greenhouse gases. The cited source also suggests that substantial improvements may come from developing dedicated, intelligent robots that may autonomously retrieve plant and soil samples or assist in livestock management.

Recent research has focused on improving crop production, preventing crop diseases, managing irrigation, and managing livestock using various AI tools [254]. The integration of high-throughput phenomics and genomics with analytic applications based on large volumes of data has sparked a revolution in agricultural science [255], [256]. AI will enable crop management software to incorporate biological information, developing holistic agronomic programs that integrate both chemical and biological insights. The ability to navigate complexity, distinguish between correlation and causation, and use ML to uncover hidden patterns is crucial for unlocking
<figure><img src="image_13.png" /><figcaption>**FIGURE 11.** Digital agriculture processes within the South America region's farm and agriculture value chain. Processes are grouped into four main categories: field and livestock segmentation, crop and livestock monitoring, activity tracking and control, and other applications within the value chain (Source: [258]).</figcaption></figure>

the big data potential. The simultaneous processing of large datasets provides tremendous opportunities for researchers in academia and industry to advance agricultural science. In this field, the potential of ML models, expert systems, and autonomous machines on farms, farmers, and food security is still largely overlooked or underappreciated.

The large potential of these new methods also entails some risks. Systemic risk factors of AI in agriculture may depend on one side on the interoperability, reliability, and relevance of agricultural data relied upon and on the other on the possible unintended socio-environmental consequences of the narrowness of objectives (such as employing models only optimized for yield), and safety and security concerns with the deployment of ML platforms at scale [257]. Suggested risk mitigation measures include involving rural anthropologists and applied ecologists in the technology design process, applying responsible and human-centered innovation frameworks, establishing data cooperatives to improve data transparency and ownership, and initially deploying agricultural AI in digital sandboxes. Agricultural technology companies (AgTech) are the primary agents of this ongoing revolution toward digital agriculture and the potential drivers of adoption of further innovative AI-based technologies [258]; these companies can provide knowledge-based digital agriculture services at all stages of the agricultural value chain (see Fig. 11). With their expertise and products, AgTech may mitigate the problems and limitations of digital agriculture connected to connectivity, data collection, transmission, storage, accessibility, and interoperability.

Disseminating technical advice and best practices through agricultural extension services is crucial for supporting 570 million small-scale farmers worldwide, contributing to food security and rural development. In a recent study, Tzador *et al.* [259] assessed the potential benefit to farmers from the use of large language models to transform agricultural extension. This study focused on LLMs' ability to simplify scientific knowledge and provide personalized, location-specific recommendations that may boost productivity. LLMs can be transformative by simplifying science and making advisories personal. Results from the cited study also highlighted the shortcomings of this technology, informed by real-life testing

of GPT to generate technical advice for Nigerian cassava farmers. An idealized LLM design process incorporating human experts was proposed to ensure the safe and responsible dissemination of LLM functionality across farming worldwide.

## 2) The future of agriculture in the AI age

As discussed above, the benefits that AI methods have brought to agriculture are undeniable; AI may integrate diverse types of data across crop and livestock science, unraveling the complex interplay between genetics, nutrition, agronomy, and the environment, as well as influencing on-farm performance and informing ongoing breeding programs for more efficient and sustainable food production [260].

In the future, we anticipate further quantum leaps in agricultural productivity. These may come through the application of cutting-edge technologies such as AI-assisted gene editing, thus meeting today's needs of farmers and the society they feed and anticipating the challenges posed by long-term sustainability in a deteriorating environment and a growing demand. Smart farming tools can perform small, repeatable, and time-consuming tasks, freeing up farm workers for more strategic operations requiring human intelligence. Additionally, the employment of vertical farming systems, which allow the growing of crops in vertically stacked layers in controlled indoor environments, enhances the efficiency and sustainability of crop production. These technological advancements represent the next step in the evolution of smart agriculture, necessitating other technologies to function effectively. Technology providers must address challenges related to improving their tools, helping farmers solve them, and communicating how ML helps solve real problems, such as reducing manual labor. As farmers, cooperatives, and agricultural development companies embrace data-centric approaches and expand AI use to improve yields and quality, the future of AI in agriculture looks promising. Wider adoption of AI-based agricultural practices will only be achieved through collaboration among researchers, technology developers, suppliers, farmers, and their advisors across the digital innovation system [261]. AI will unlikely replace the farmer, but it will significantly enhance decision-making, transforming agriculture into a more efficient, sustainable, and climate-resilient future.

# E. MEDICAL SCIENCES

## 1) The past

The history of AI in medicine dates back to the mid-20th century and has gone through various phases of development since. AI's popularity rose in 1950 when Alan Turing introduced the idea of computers mimicking human intelligence in his book, "Computer and Intelligence" [262]. In the 1960s, Stanford University developed DENDRAL—an expert system that could analyze organic chemical compounds by examining their mass spectra and applying general chemistry knowledge. This was one of the first AI expert systems to be integrated into medical applications as it replicated a chemist's problem-solving and decision-making processes
[263]. Also, during this time, the SRI Artificial Intelligence Center created Shakey the Robot in 1966, which could plan its own routes and rearrange standard objects using humanistic thought processes such as perception and reasoning. Referred to as the “first electronic person” by Life magazine, Shakey highlighted the soaring potential for AI to understand and execute instructions [264]. A decade later, Stanford’s School of Medicine developed MYCIN, a tool assisting physicians in determining the bacterial pathogens behind infections such as bacteremia or meningitis, as well as recommending the appropriate antibiotic therapy dosage options according to the patient’s mass [265].

Indeed, the 1970s were a pivotal decade for AI in medicine. Already in 1971, the University of Pittsburgh’s School of Medicine developed INTERNIST-1 [266], a large-scale set of disease profiles containing patient symptoms, laboratory abnormalities, signs, and demographic data (Institute of Medicine [US] Council on Health Care Technology 1988). This database was designed to assist medical professionals in diagnosing complex diseases. In 1976, the LDS Hospital in Salt Lake City introduced the Health Evaluation through Logical Processing (HELP) system [267]. HELP was the pioneering hospital information system that combined the collection of clinical data with computerized aid in real-time decision-making<fnref n="14" />. This provided medical professionals with alerts for harmful drugs and other clinical recommendations. Despite being innovative and showcasing the potential for AI in medicine, these systems struggled to gain widespread acceptance due to certain technological constraints and skepticism from many groups within the healthcare industry and the public.

The 1980s and 1990s saw continued progress with significant advancements. DXplain, a system developed by the Laboratory of Computer Science at the Massachusetts General Hospital in 1986, could provide justifications for over 2600 diseases based on clinical symptoms and user laboratory data. Specifically, DXplain can output a ranked list of potential diagnoses to be considered based on symptoms, recommends which additional information should be collected, as well as manifestations that would be atypical for each specific diagnosis¹⁵. AI applications extended into cardiology with consultation systems and clinical tools like CorSage, developed by Cedars-Sinai in 1989 [268]. CorSage utilizes a blend of AI and statistical methods to assist physicians in pinpointing heart patients who are at the highest risk of experiencing recurrent coronary events. Further, The Human Genome Project, launched in 1990 by the National Human Genome Research Institute, provided crucial genetic data that allowed AI systems to understand further genetic factors in diseases¹⁶. Numerous organizations, projects, and hospitals played a crucial role in supplying data to train and operate these AI systems.

Machine learning caused a significant evolution at the dawn of the 21st century when neural networks revolutionized medical diagnostics, particularly the use of CNNs in imaging. For example, regarding breast cancer detection, Google’s DeepMind developed a CNN model that understands mammograms to identify cancerous lesions with an accuracy that often surpasses humans<fnref n="17" />. Another relevant application is in diagnosing diabetic retinopathy. In 2020, Temple University developed IDx-DR, the first FDA-approved autonomous AI system for this purpose, and it uses CNNs to analyze retinal images and accurately detect diabetic retinopathy [269]. Overall, this evolution of AI highlights its transformative influence on medical diagnostics and its growing acceptance within the healthcare industry.

## 2) The present

Today’s applications of AI in medical science can be categorized into two main branches: virtual and physical. We examine them separately below.

The virtual part includes a wide spectrum from electronic health record systems to neural network-based advice providers in clinical decision-making. AI-driven electronic health record systems incorporate features such as Natural Language Processing (NLP), intelligent image input suggestions, and data entry recommendations to improve operations efficiency and simplify patient record management for healthcare professionals [270]. Another virtual aspect is rooted in Machine Learning, which harnesses algorithms and data to enable AI systems to replicate human learning processes, enhancing their precision over time. Recent developments in AI have improved the prediction, speed, efficiency, and accuracy of the diagnostic process. AI algorithms can analyze large amounts of medical data such as vital signs, biosignals, medical history, laboratory test results, demographic information, and imaging data such as MRIs, ultrasounds, X-rays, DXAs, and CT scans. This analysis helps medical professionals make informed decisions and accurate predictions in patient care [271]. An example is IBM’s “Watson for Oncology” software, which oncologists use to make treatment decisions for cancer patients [272]. By combining attributes from data from Memorial Sloan Kettering and a patient’s file, Watson for Oncology recognizes and orders possible treatment plans. Other medical disciplines, such as drug development, digital consultation, and pathology, use AI to assist medical professionals.

Machine Learning has facilitated advances in medical sciences. For example, unsupervised algorithms for protein-protein interactions contributed to the discovery of promising therapeutic targets [273], computational methodologies for recognizing DNA variants (e.g., single nucleotide polymorphism) to predict specific diseases or physiological traits [274], and particular algorithms for electronic medical records to capture and process data in real-time to facilitate the finding of patients with a positive family history for spe-

<footnote n="14">¹⁴https://www.clinfowiki.org/wiki/index.php/Health_Evaluationeti through_Logical_Programming_(HELP).

¹⁵https://www.mghlcs.org/projects/dxplain.

¹⁶https://www.genome.gov/human-genome-project.</footnote>

<footnote n="17">¹⁷https://health.google/caregivers/mammography/.</footnote>
cific genetic disorder or individuals with increased risk of spe-
cific chronic diseases <fnref n="18" />. Recently, ML models have proven
beneficial in assisting healthcare professionals in diagnosing
various diseases and illnesses earlier based on a series of
parameters. Another interesting virtual application of AI in
medical care is the utilization of softbots. Softbots have been
introduced as teachable psychotherapeutic avatars and have
shown promise for pain control measures in pediatric patients
with cancer and for detecting emotional disturbances such as
suicidal ideas [275].

The physical part includes advanced medical devices, in-
telligent prostheses, and complex robots for care delivery
(carebots). AI-assisted robots have been used as companions
for the geriatric population with cognitive or mobility impair-
ments, as assistants in surgeries or solo performers, and as
teachers for autistic children [276], [277]. Nevertheless, rou-
tine application of AI-assisted robots is associated with major
ethical issues and requires standardization, precise evaluation
of the efficacy, and close follow-up on the related side effects
and outcomes.

The evaluation of patients’ performance in rehabilitation
is improved by using AI systems such as wearable health
sensors. Interestingly, AI is suggested as a powerful tool for
monitoring guided drug delivery to different tissues.

Some AI-based tools received FDA approval; for example, Kardia for ECG monitoring on smartphones and detecting atrial fibrillation [278], Guardian and Sugar.IQ systems for glucose level monitoring and prediction of hypoglycemic episodes [279], [280], Empatica's wearable Embrace for detecting epileptic seizures [281], [282], wearable sensors for evaluating gait, posture, and tremor in patients with neurodegenerative diseases [283], and other algorithms for interpreting pulmonary function tests [284], [285], predicting the decrease in glomerular filtration rate [286], processing the endoscopic and ultrasound imaging in gastroenterology [287], and assisting cancer diagnosing via computational histopathology [288].

3) The future

Using past data to improve clinical decision-making is the
core concept of evidence-based medicine. AI facilitated un-
covering complex associations among a vast set of different
data, which cannot be humans. This allows machine learning
systems to approach complex clinical problems in a way
similar to how a physician would operate. The superiority
of these systems to clinicians is the openness to numerous
simultaneous inputs and the ability to rapidly process data.
Indeed, such systems can obtain data from as many cases as a
clinician may visit in a lifetime, just in a minute. Accordingly,
AI-assisted tools were successfully used in the classifica-
tion of suspicious skin lesions, radiologically determining
pulmonary tuberculosis, and triaging systems [289]. Taken
together, AI performance in well-defined tasks, such as the
classification of suspicious skin lesions, had higher sensitiv-

ity and specificity than expert dermatologists. Moreover, AI
could be a promising tool for poorly resourced services; for
example, the lack of expert radiologists in remote regions
where tuberculosis is prevalent could be compensated by deep
learning models [290].

The use of AI tools may reduce the financial burden on
the healthcare system while also offering more personalized
clinical advice to patients. Application of AI-assisted tools in
medical settings may reduce the workload of healthcare staff,
hence they can spend more time on critical cases.

AI systems do not have empathy and compassion; hence
most patients prefer to be visited by a human physician.
Lack of direct human-human contact may result in imperfect
communication and lead to the system missing essential in-
formation that human physicians may be able to observe in
addition to the main patient's complaints and details of their
health problems; that ancillary information may sometimes
prove necessary for clinical decision-making. In general, AI
systems are still less trusted by patients [291]. On the other
hand, uncontrolled access to AI-assisted medical help may
result in misuse, information overload for the patient, misdi-
agnosis, and mismanagement.

Some serious criticism focuses on the validation process of
AI tools. For instance, most of the studies on the efficiency of
AI versus human physicians are suggested to have unreliable
designs and lack primary replication [292]. In addition, the
majority of the studies on the use of AI in clinical practice are
criticized due to retrospective design, sample size, spectrum
bias, and selection bias [287]. Finally, confronting AI and
physicians is probably not the proper approach; some studies
considered the combination of AI-assisted tools and human
physicians, which outperforms either alone [293].

Different ethical issues are raised with AI development.
Considering that medical sciences are engaged with people's
health, addressing ethical issues concerning the application of
AI in medicine is of utmost importance. Again, we insist that
an AI Agency to supervise these ethical aspects is necessary.

AI and data mining unlock vast possibilities for uncov-
ering new links between health and various aspects of life,
drawing from extensive repositories of health records (i.e.,
US Medicare, US Veterans Affairs, British Tissue Repository,
ADNI, and others) and genomic data available on scientific
and genealogy platforms such as 23andMe, Ancestry, and
similar sites. Despite official governmental restrictions on ac-
cess and use, there are data privacy concerns associated with
many of these repositories. In December of 2022, BlackRock
purchased Ancestry.com<fnref n="19" /> and later a couple of plaintiffs filed
a class action complaint, alleging BlackRock violated the Ge-
netic Information Privacy Act<fnref n="20" />—a California legislation that
imposes privacy regulations on direct-to-consumer genetic
testing companies while also affording consumers access and

<footnote n="19">¹⁹https://cookcountyrecord.com/stories/641978335-appeals-panel-blackrock-s-purchase-of-ancestry-com-doesn-t-mean-they-can-be-sued-for-obtaining-illinoisans-genetic-info.</footnote>

<footnote n="20">²⁰https://privacyrights.org/resources/genetic-information-privacy-act-california.</footnote>

<footnote n="18">¹⁸https://doi.org/10.1186/s13336-015-0019-3.</footnote>
deletion rights. The plaintiffs alleged that before the Blackstone acquisition, Ancestry linked their saliva sample genetic sequencing kits with personal details such as email and home addresses. They claimed that Blackstone's acquisition compelled Ancestry to disclose this protected information. Despite official restrictions on access and use, concerns arise when investors like BlackRock heavily invest in platforms such as Ancestry, prompting questions about the potential unauthorized transfer of data to insurance databases.

Independent from investing in repositories, further data privacy concerns exist, such as the logistics behind training AI models. High-quality, large, and diverse data sets are required to effectively improve and train AI models; however, not all organizations collecting medical data are shielded by the HIPAA privacy law. Furthermore, erroneous or biased data used by AI can lead to inaccurate predictions, causing under or over-diagnosis and treatment<fnref n="21" />. The question remains: when will the precision of machine learning exceed that of the top specialist expert? Diagnosis is the gateway of treatment plans, and here, AI possesses the capability to process exponentially more inputs than the human brain, facilitating world-class diagnosis and treatment to be delivered anywhere while highlighting the lack of infrastructure required for delivery.

In Alzheimer's disease (AD), for 30 years, researchers have evaluated the most highly predictive genetic factor in AD, the apolipoprotein genotype (APOE). APOE is a reliable predictor of Alzheimer's disease risk, with individuals carrying the E4 allele facing three times the risk and homozygotes facing a risk 10 to 20 times greater. Add this to the differential penetration among African Americans and Hispanics, and there are clear risk classes [294]. The use of AI to mine large demographic and patient-record databases can reveal additional risk predictions. These analyses extend beyond diagnostics, accurately predicting individuals who are not yet affected by the condition. This raises ethical concerns surrounding issues related to information accessibility, counseling restructuring, and the stigma associated with pre-symptomatic labeling. These matters necessitate vigorous dialogue in this rapidly emerging area, which not only provides labeling from databases and genetic data without individual knowledge but also from speech or gait patterns. Developing policies for most conditions requires independence and should not solely depend on patient advocacy groups, which agents of industry frequently back.

As AI tools are trained based on specific datasets, the algorithms may be biased toward certain patient groups, leading to discriminatory traits. In addition, AI tools need access to a vast amount of patient data to be trained and to make decisions. Hence, data security and privacy, as well as data ownership using AI tools, is controversial. Along the same lines, patients probably do not understand how their data is being processed by AI tools, which endangers informed consent. There are also concerns regarding the accountability

(the responsible party for AI decisions and actions in the healthcare setting) and transparency (the complexity of AI systems makes it challenging to rationalize how the system arrived at a decision) of AI tools. From the global health perspective, the application of AI-assisted tools may exacerbate the current health disparities. Another challenge would be the overreliance on AI systems, which may lead to debilitating critical thinking and decision-making skills of healthcare professionals [295], [296].

AI can transform the grounds of medicine as it is time-efficient, inexpensive, and does not have physical limitations as humans do. It offers great promise to improve healthcare and reveal the root causes of genetic, lifestyle, and chronic diseases. However, parameters must be established to address patient data privacy and ethical concerns. Medical professionals must provide AI control, oversight, and security to utilize its transformative potential in medicine fully.

## F. BEHAVIOURAL SCIENCE

Behavioral science offers an example of how AI has the potential to help scientific progress as well as to hurt and hinder it. We discuss this contrasting situation below.

### 1) Empowerment of behavioral science by AI

Artificial Intelligence has the potential to significantly augment scholarly work in behavioral science, offering tools and methodologies that can enhance the efficiency and effectiveness of research and practice.

Among the more obvious benefits is the potential efficiency of uncovering past knowledge if the models are properly curated. Advanced search algorithms can sift through vast databases of scholarly articles, rapidly extracting findings that are relevant to specific questions. With enough curation, the quality of research can be weighed as part of this process.

Barriers to fair consideration of research findings can potentially be reduced in this fashion. For example, standard indexing engines have a known bias against non-English sources or studies from lower-and-middle-income countries—even if the science is of high quality [297]. Biases of this kind can considerably distort systematic reviews of the literature [298], but AI tools can help overcome these language barriers.

Some research topics that would simply be impossible without AI can be explored. For example, communications researchers might study the impact of discussing modern issues in the style of historical figures (e.g., “What would Walter Cronkite say about abortion and how would it land?”). Other research topics might be explored with greater experimental control. For example, social persuasion research could examine the impact of AI-generated transcripts in which scientists tightly specify the parameters. The ease of conducting experiments could also be increased by using AI tools to manage and analyze large datasets, automate recruitment, or manage the engagement of participants.

Access to sophisticated modeling tools is another advantage afforded by AI that is already widely in use. More

<footnote n="21">²¹https://veterans.house.gov/calendar/eventsingle.aspx?EventID=6371.</footnote>
advanced statistical and measurement tools can better detect complex behavioral patterns, allowing for the development of more precise theoretical models. For example, extensive high-density longitudinal data on scores of individuals with specific mental or behavioral problems can readily overwhelm conventional models, but AI methods can discover signals that reside amidst a great deal of noise. AI tools applied to longitudinal data on the process of change and their relation to outcomes in a large number of individuals, each considered ideographically, may be able over time to produce a more functional diagnostic system than the current normative categorical systems such as the Diagnostic and Statistical Manual of the American Psychiatric Association [299]. As this occurs, kernels of specific evidence-based interventions might be suggested or delivered in a just-in-time fashion that better meets the needs of the individual.

## 2) Empowerment of contextual behavioral science research

We will now provide a concrete example of how behavioral science research can be empowered by AI by using the Contextual Behavioral Science (CBS) research program as an example. CBS embodies a distinct approach within the broader behavioral sciences that emphasizes understanding behavior in relation to its historical and situational contexts [300]. This approach is multilevel, process-based, multidimensional, prosocial, and pragmatic. The core characteristics of CBS can be described as follows:

*   **Multilevel:** CBS research examines behavior across different levels of analysis (e.g., biological, psychological, social) to understand how these levels interact and influence one another. It aims to address the coherence across these levels within a broad evolutionary science framework.

*   **Process-based:** CBS emphasizes identifying and understanding processes of change that are functionally important. This involves focusing on dynamic sequences of events that can lead to clinically relevant outcomes, linking these to basic behavioral and evolutionary principles.

*   **Multidimensional:** CBS research approaches human behavior from various dimensions, including cognitive, affective, behavioral, and social aspects. It considers the complex and interrelated nature of these dimensions in understanding and influencing behavior.

*   **Prosocial:** CBS research is guided by a prosocial purpose, seeking to foster social justice, cooperation, and the betterment of society. It involves being explicitly aware of the societal impacts of research and aiming to address issues like equity, fairness, and inclusiveness.

*   **Pragmatic:** The approach is inherently practical, focusing on developing strategies and interventions that directly and meaningfully impact real-world problems. CBS values research that can be applied to improve human well-being and adapt to changing societal needs.

In essence, CBS research is about understanding behavior

in context, emphasizing practical application and social relevance, and striving to create an adequate behavioral science for addressing the complexities of the human condition.

As mentioned in Sec. III, the Non-Axiomatic Reasoning System and similar AGI systems offer a unique opportunity to enhance our understanding of human cognition and behavior from a CBS perspective. Given that NARS learns in interaction with its environment (rather than being pre-trained on data sets), it could, in principle, be used as an experimental "subject" in settings and procedures akin to those used with human participants. This research strategy has been used to study the development of various forms of concept formation and relational reasoning abilities [301]–[303]. This presents a novel approach to studying multilevel interactions and processes. NARS, designed to mimic certain aspects of human reasoning, provides a controlled environment where researchers can manipulate variables and observe outcomes in a way that is not possible with human subjects. Hence, NARS can serve as a dynamic model for understanding how different levels of analysis –such as cognitive processes, whole-organism behaviors, and social interactions– interact with each other to produce complex behavior patterns. By embedding NARS in simulations that mimic real-world social settings or psychological conditions, researchers can manipulate and observe the impact of changes at one level (e.g., altering cognitive rules or input stimuli) on other levels (e.g., behavioral responses and social dynamics). This approach allows for detailed experimentation on complex, multilevel phenomena in an ethically permissible and highly controllable way. The insights gained from such studies could then inform the development of hypotheses about human behavior, which can be tested in naturalistic settings.

Furthermore, in principle, NARS could be used to carry out process-based research regarding clinically relevant change processes. NARS is unique in that it has a concept of "self" [304]. The SELF in NARS embodies a critical component of its artificial general intelligence, enabling adaptive behavior and decision-making in complex environments. This concept facilitates self-awareness and self-control within NARS, allowing it to perceive and interact with its internal environment similarly to its external surroundings. The SELF-concept evolves through the system's experiences and interactions, starting with built-in operations and expanding through learned behaviors and modifications based on feedback. As a dynamic and evolving feature, it enriches NARS's functionality, enhancing its autonomy and its ability to refine operations and behaviors to better meet its objectives and respond to environmental challenges.

The application of NARS in process-based research offers a novel approach for investigating the cognitive and emotional dynamics involved in, for example, anxiety disorders. This methodology provides experience to NARS that leads to it developing a self-concept that encapsulates various anxiety-related cognitive processes and emotional states, such as worry, avoidance behaviors, and safety-seeking actions. By arranging a series of anxiety-inducing procedures, re-
searchers could manipulate and measure changes in NARS' internal state and behaviors in response to interventions like cognitive-behavioral therapy (CBT) techniques, exposure therapy, and mindfulness-based interventions. The use of NARS would allow for detailed tracking and analysis of how interventions impact anxiety levels and behavioral responses over time, with a particular focus on how changes in self-awareness and the effectiveness of previous coping strategies influence future behavior and self-concept adjustments. This process-based approach would facilitate a deeper understanding of the mechanisms underlying anxiety and its treatment and provide insights into the generalizability of coping mechanisms across different scenarios. Such findings could significantly enhance the development of therapeutic techniques, emphasizing the role of self-awareness and adaptive changes in self-concept in anxiety management. Refer to [304] for an example regarding fear learning.

In addition, AI, in general, can advance the prosocial aims of CBS research. By leveraging AI-driven analysis, researchers can better understand the societal impacts of their work and identify strategies that promote equity, fairness, and inclusiveness. AI can help in modeling the societal implications of various interventions, thereby guiding the development of more socially responsible and effective approaches.

Lastly, the pragmatic nature of CBS research is well-served by AI's ability to provide practical, data-driven insights. AI can aid in rapidly prototyping and testing interventions, streamlining the process of translating research findings into real-world applications. This can significantly enhance the ability of CBS researchers to develop strategies that directly address human well-being and adapt to societal changes.

In conclusion, AI offers a suite of tools and methodologies that can significantly enhance the multilevel, process-based, multidimensional, prosocial, and pragmatic aspects of CBS research. As such, NARS, as it applies to CBS research, is an example of an AI framework that could potentially enhance how behavioral science research is more generally conducted. By integrating AI into their toolkit, researchers can achieve a deeper, more comprehensive understanding of behavior and develop more effective interventions to improve human well-being within a broad evolutionary and life science framework.

### 3) Risks to behavioral science from AI

Despite all its benefits, AI also introduces risks that could undermine the integrity and progress of behavioral science. One primary risk is the propagation of false information that can be mixed with factual data in ways that are difficult to detect. The ease with which AI can generate content further poses a significant threat to the originality and credibility of scholarly work. Plagiarism is a constant threat.

Depending on how models are developed, the risk of cultural hegemony and bias is embedded within AI algorithms. Commercial AI tools could easily be curated in ways that favor specific commercial products or areas, such as research tools that ignore design problems in research on pharmaceuti-

cals for behavioral and emotional problems while criticizing them in psychosocial research on these topics.

There is a risk that the outcome success of large language models could be mistaken for an understanding of the natural processes of language acquisition, especially in vulnerable populations such as children with disabilities. The “Chinese room” problem in this article shows the issue. Still, confusion of this sort could detract from the search for the human processes underlying language competence, thus undermining effective application and slowing knowledge development.

In the area of psychotherapy, as LLMs come to do a better and better job of mimicking the actions of therapists, the human therapeutic alliance may come to be replaced by machine → human substitutes rather than to use AI tools to extend and augment therapeutic interactions. Famous psychotherapists may have their voices and mannerisms, in essence, taken away from them as part of this change. The long-term impact of AI therapists is hard to model in the absence of data, just as with the effects of humanoid robot substitutes in other areas, but it raises difficult ethical and moral issues at the very least.

Finally, the allure and ease of use of new AI methodologies may overshadow the foundational purposes of research and the hard work needed to produce new scientific knowledge. For example, a young scientist might find it far easier to conduct AI-driven meta-analyses of other scientists' work than to risk failure in discovery-oriented research in the lab. This could slow scientific progress even if AI tools perform very well because they cannot replace the need to uncover the processes that lead to behavioral competencies through actual experimentation.

### 4) Possible mitigation strategies

Several strategies can be implemented to minimize the risks associated with AI in behavioral science. The creation of sophisticated tools to detect plagiarism and cultural or other biases within AI-generated content is essential. In a similar way, improving the capacity to discern false information through enhanced AI algorithms may help improve scientific quality.

Psychiatry has long pursued better conflict of interest (COI) regulations to manage the influence of commercial interests on research outcomes, but these can become “pro forma”. In AI, merely waiving one’s hand will not suffice. The involvement of scientists and professional associations in setting standards and providing guidelines for AI use is critical to ensure that the integration of AI into behavioral science enhances rather than detracts from the field’s integrity and progress.

## V. IMPACT OF AI DIFFUSION IN TODAY'S AND TOMORROW'S SOCIETY

### A. ETHICAL ASPECTS

The introduction of AI in today's human society raises concerns that are even more radical than those championed in
the XIX century by the Luddite movement, which strongly opposed the introduction of machines in the textile industry.

While most critics of the introduction of machinery in XIX century industries used to focus on the damage to the overall employability of the workers that were being made redundant, we know that the work market somehow adjusted itself to a new configuration. A side effect of utmost importance, however, became apparent when industrial production evolved to Henry Ford's concept of supply chain: the alienation of the workers, whose job changed from a creative activity where "a cycle" was the creation of a whole finished product, to a repetitive fast activity where the job collapses to a single, quick, repetitive action that requires no thinking nor any critical or creative thought.

The changes to society were radical: the frustration of the workers and their exploitation created the conditions for the rise of new ideologies and self-consciousness of the workers [305]. The rise of Communism molded most of modern politics and history: like all human activities, it involved lights and shadows: most of the laws that regulate the job market nowadays (at least in places that did not adopt the ultra-capitalistic system of the USA) came as a result of the activities of the communism-inspired Unions; the implementation of the Communist ideology has however also brought perversion, mass murder, and inequality in many countries.

The AI revolution can have an even more massive impact than the industrial revolution. AI promises to make most of our actions faster or simpler and is often used to improve connectivity between humans. Still, each benefit is unfortunately open to being exploited in perverse ways. The widespread terror about the impact of AI on the job market has inspired several populist movements across Europe to make "citizen income," a guaranteed income that each citizen would receive to be able to live a normal life even without working, a central message of their campaign (cite Italy, Spain, France, etc.). When applied to human interactions, AI can increase connectivity but also be used to manipulate people's minds and political orientation, enabling companies or malicious actors to sway and decide the result of elections, with negative impacts on the population, increasing overall unhappiness. Several tasks that were once exclusively performed by humans can be executed quickly by large language models today, but this entails several risks. Although non-experts use them to produce content, interpreting their output requires expert knowledge to spot errors correctly. This significantly impacts the reliability of AI-produced content, particularly when humans do not honestly declare the AI generation aspect. AI content and the spreading of its use can be devastating from the pedagogical point of view even when the content itself is correct and reliable: students may be tempted to use the outputs as a substitute for putting real effort into studying a topic, and the knowledge of the subject matter will be more and more delegated to the algorithm, to the point that maintaining a certain level of human know-how and control may become unfeasible.

Among the various essays, Yoshua Bengio et al. [306]

stands out as one of the most authoritative. Its contributors include prominent intellectuals such as Yuval Noah Harari and Daniel Kahneman, alongside key figures in the current AI revolution, including Geoffrey Hinton. Despite their significant contributions to the advancement of AI, these authors express both surprise at the rapid progress and an anticipation of new frontiers that AI will soon reach. They argue that the unpredictable extent of AI's potential intelligence shortly necessitates a severe examination of its associated risks. Rather than calling for a slowdown in AI research, these authors advocate for an accelerated focus on integrating safety measures and verification processes into AI development.

### 1) Benefits and risks of AI diffusion to modern society

AI-generated content and advice are widely used in sectors like healthcare, education, literature, report production, language translation, and social media [307]–[312]. One of the major benefits of AI is that it significantly simplifies content production, both written and visual. Advances in machine learning and AI have led to language models (see *supra*, Sec. III) that generate credible continuations to short prompts. For example, these applications are revolutionizing journalism. They automate writing tasks such as local news stories, special interest stories, and earnings reports. This allows human efforts to focus on editing, selecting, organizing, and presenting content [313].

The rise of AI has also sparked concerns over the proliferation of fake news and misinformation. AI can be used to identify those susceptible to misinformation and manipulation and to create content that sounds reasonable and mimics human news stories, narratives, and behavior [313], [314]. For example, social bots significantly contribute to the spread of low-credibility articles. They amplify such content in the early stages of spreading before it goes viral. These bots target users with numerous followers by responding to them and mentioning them [315]. The users, in turn, reshare the content posted by bots, causing fake news to go viral.

Fake news refers to fabricated information that imitates legitimate news media in form but lacks the organizational process or intent [316]. Unlike legitimate news outlets, fake news sources do not adhere to editorial standards that ensure accuracy. Misinformation can also come from conspiracy theories and health or vaccination misinformation [317], [318]. Studies show that misinformation spreads more widely and quickly than accurate information [319].

During the US presidential election, a quarter of tweets were either fake news (10%) or extremely biased (15%) [320]. Automated accounts and bots played a significant role in spreading fake news. A survey found that 36% of respondents believed in conspiracy theories about the planning of the coronavirus outbreak by powerful people [321]. Research suggests that a substantial proportion of the public views misinformation as highly reliable [313].

Right-leaning media was more linked to fake news than left-leaning media, potentially influencing the election and democratic processes [320]. Misinformation can also lead to