Out-of-Hardware Experience

Out-of-Hardware Experience

Software & Consciousness

Tancredi Di Giovanni


Modern theories on the relation between humans and machines are, to a great extent, a consequence of the scientific positivism developed in the Western world during the 19th century, which assumes that the subjective nature of human consciousness can be denied or reduced to its objective physical substance: the brain. This speculation models the relation between humans and machines on two axes: [x] It emphasizes the relation between a loss of humanity and the rise of autonomous machines (this view implicitly portrays software as the consciousness of the machine originated by a hardware). [y] It emphasizes the relation between the social system and the technical system (this view explains software as a cultural and social object that can be studied independently from the hardware).

In order to contain the exponential growth of the technical system and the disappearance of the individual, institutional power becomes the only legitimate means of containment, imposing regimes of engineered subjectification while enmeshing society with its technologies. Fostering visions of dystopic futures and lacking early systematic critiques, these arguments silently underpin the technological and social developments of the 20th century, to reach daily life in our contemporary society.

In Part 1, unified under the umbrella term “machinic life” coined by John Johnston, this thesis looks back at the general history and theoretical results of attempting to build autonomous machines1 “[…] mirroring in purposeful action the behavior associated with organic life while also suggesting an altogether different form of ‘life’ […]” (Johnston, 2008). In opposition to the assumptions of machinic life, this thesis proposes in Part 2 a different approach informed by new developments in the understanding of consciousness: [z] It emphasizes the relation between subjective experience and the technical system, unfolding a clearer understanding of both biological and artificial systems as part of an extended cognitive system.

In this direction, I have found a particular resonance of my thoughts with the work of David Chalmers on consciousness, claiming for a paradigm shift in science to finally allow the study of subjective experience: “when simple methods of explanation are ruled out, we need to investigate the alternatives. Given that reductive explanation fails, nonreductive explanation is the natural choice” (Chalmers, 1995); also, Thomas Metzinger, who, through the study of altered states of consciousness and psychiatric syndromes, is one of the few to propose an appealing alternative (reductionist) model of consciousness capable of explaining the nature of the self: “If we pay more attention to the wealth and the depth of conscious experience, if we are not afraid to take consciousness seriously in all of its subtle variations and borderline cases, then we may discover exactly those conceptual insights we need for the big picture” (Metzinger, 2009); also Katherine N. Hayles, who, starting from a social perspective and criticizing consciousness, links machines and biological systems extending cognition into the body and the environment: “Although technical cognition is often compared with the operations of consciousness […], the processes performed by human nonconscious cognition form a much closer analogue” (Hayles, 2017); and finally, Matteo Pasquinelli, for his studies on machinic intelligence in general, but always a source of great inspiration.

Instead of addressing its technical and cultural aspects directly, the result of these discourses reveals a new primary condition of software pointing toward the subjective experience of new phenomenal worlds that can be built and sustained in collaboration with an external artificial form of cognition. Knowledge, from this point of view, is not inaccessible to the individual level, given by science and institutionalized through society, but is a necessary process made through the construction of worlds, simulating scientific truths or creating useful fictions, but still validating the subject as the designer (or hacker) of its own experience.

“But why should I repeat the whole story? At last we came to the kingly art, and enquired whether that gave and caused happiness, and then we got into a labyrinth, and when we thought we were at the end, came out again at the beginning, having still to seek as much as ever.”
— Plato, Euthydemus

Part 1

“Then, just as the frightened technicians felt they could hold their breath no longer, there was a sudden springing to life of the teletype attached to that portion of Multivac. Five words were printed: INSUFFICIENT DATA FOR MEANINGFUL ANSWER.”
— Isaac Asimov, The Last Question

The Hard Problem of Consciousness

Through the standard scientific method, the challenge of explaining the mind has been mostly addressed by disassembling it into its “functional, dynamical and structural properties” (Weisberg, 2012). Consciousness has been described as cognition, thought, knowledge, intelligence, self-awareness, agency and so on, with the assumption that explaining the physical brain would resolve the mystery of the mind. From this perspective, our brain works as a complex mechanism that eventually triggers some sort of behavior. Consciousness is identified with a series of physical processes happening in the cerebral matter (reductionism) and determining our experience of having a body, thinking, and feeling. This view has been able to explain many unknown elements of what happens in our minds.

In 1995, the philosopher of mind David Chalmers published an article titled Facing Up to the Problem of Consciousness, in which he pointed out that the objective scientific explanation of the brain can solve only an easy problem. If we want to fully explain the mystery of the mind, instead, we indeed have to face up to the hard problem of consciousness: How do “physical processes in the brain give rise to the subjective experiences of the mind and of the world”? Why is there a subjective, first-person, experience of having a particular kind of brain? (Nagel, 1974)

Explaining the brain as an objective mechanism is a relatively easy problem that eventually, in time, could be solved. But a complete understanding of consciousness and its subjective experience is a hard problem that scientific objectivity cannot access directly. Instead, scientists have to develop new methodologies and eventually non-reductive models, considering that a hard problem exists – How is it possible that such a thing as the subjective experience of being “me, here, now” takes place in the brain?

Echoing the mind-body problem initiated by Descartes in the 17th century, subjective experience, also called phenomenal consciousness (Block, 2002), underlies any attempt to investigate the nature of our mind. It challenges the physicalist ontology of the scientific method showing the unbridgeable explanatory gap (Levine, 2009) between the latter’s dogmatic view and a full understanding of consciousness. This produces the necessity of a paradigm shift allowing new alternative scientific methods to embrace the challenge of investigating phenomenal consciousness, for example the neurophenomenology proposed by Francisco Varela (1996).

Reactions to Chalmers’ paper range from a total denial of the issue to panpsychist positions, with some isolated cases of mysterianism advocating the impossibility of solving such a mystery (Weisberg, 2012). In any case, the last thirty years have seen an exponential growth in multidisciplinary research addressing the hard problem with a constant struggle to build the blocks of a science of consciousness finally accepted as a valid field of study (Metzinger, 2009). Hidden for ages behind ambiguous religious beliefs in the soul and the immediacy of empirical evidence on which science is based, phenomenal consciousness is now at the very first stages of a proper scientific unfolding of its contents (Metzinger, 2009).

Thanks to a renewed view of science and its methods, subjective experience is starting to be resized to its effective dimensions, filling the gaps in the understanding of ourselves and the world. But before we explore in depth the contents of phenomenal consciousness and their implications in understanding software, it is essential to shift our attention toward the evolution of new kinds of machines in technical systems. Fostering the division between hardware and software and leading part of the scientific community to acknowledge the limits of its own practices, the understanding of the autonomous machine is the fundamental step in actualizing and testing the scientific modeling of the mind.

Machinic Life and Its Discontents – I

In The Allure of the Machinic Life, John Johnston (2008) attempts to organize the contemporary discourse on machines under a single framework that he calls machinic life:2

“By machinic life I mean the forms of nascent life that have been made to emerge in and through technical interactions in human-constructed environments. Thus the webs of connection that sustain machinic life are material (or virtual) but not directly of the natural world.” (Johnston, 2008)

Machinic life, unlike earlier mechanical forms, has the capacity to alter itself and to respond dynamically to changing situations. Implying the full attempt to produce life and its processes out of artificial hardware and software, the definition of machinic life allows us to reconsider the different experiences of the last century under the common goal of building autonomous machines, and to understand their theoretical backgrounds and assumptions as a continuum.

Subsumed in the concept of techné,3 the mythological intuition of technology already shows the main paths of the contemporary discourse of machinic life. In fact, in the myth of Talos and in Daedalus’s labyrinth, we can find the first life-like automaton as well as the first architectural design reflecting the complexity of existence and outsourcing thought from human dominion. However, only in the 19th century, with new technological discoveries and scientific positivism,4 did scientists start building the bearing structures of what would become the two main fields of machinic life of the 20th century: Cybernetics and Artificial Intelligence (AI).

On one side this process begins with the improvement of the steam engine, with Sadi Carnot’s thermodynamics (1824) joined with the debate on the origin of life opposing the theory of evolution to the religious belief in creationism. Unleashed from the religious teleology (purpose) imposed by God’s intelligent design and consigned to the random chance of natural selection introduced by Charles Darwin and Alfred Wallace’s evolutionary biology (1859), human existence was losing any perspective of independent agency (Rushton, 2019). In 1858, Wallace wrote a letter to Darwin comparing the evolutionary process with the vapor engine’s autoregulatory system, or feedback loop, later studied by J. C. Maxwell’s control theory (1868):

“The principle of this process [natural selection] is exactly like that of the centrifugal governor of the steam engine, which checks and corrects any irregularities almost before they become evident” (Wallace, 1858)

With this same conclusion, Samuel Butler, speculating on the evolution of machines in writings such as Darwin Amongst the Machines (1872) and Erewhon (1879), reintroduced the idea of teleology in the concept of adaptation, developing a framework where machines are capable of evolving and reproducing exactly as biological organisms do (Rushton, 2019). Wallace and Butler’s speculative theories anticipated a new understanding of the machine that would be actualized only through the advancement of control theory in communication, guns automation, and biology during World War II (Johnston, 2008). In 1946, a number of scientists, prior to working for military projects, collectively modeled the autoregulatory system of the body and simulated it in autonomous robots, giving rise to Cybernetics defined as Control and Communication in the Animal and the Machine (Wiener, 1948).

In parallel to these developments, the study of mathematics and logic, along with the revolution of the Jacquard loom (1804), led to the invention of the first general-purpose computer and the translation of elementary logic into binary algebra. Charles Babbage and Ada Lovelace’s effort to design and program the analytical engine (1837), together with Boolean logic (1854), started a new era of computation in which mental labor was no longer an exclusive prerogative of humans, but could be performed by an economy of machinery. Demonstrated for computable numbers by Alan Turing and Alonzo Church’s model of computation theory in 1936, the idea of formalizing thought in an instrumental set of rules (algorithm) can be traced back to Plato5 and Leibniz6 (Dreyfus, 1972). The Church-Turing thesis, together with Von Neumann’s computer architecture (1945) and Shannon’s information theory (1948) influenced by cybernetics, mark the birth of the digital computer making possible the beginning of Artificial Intelligence (AI) in 1956 (Russell & Norvig 2003).

If the classical world had the intuition of the sentient machine, and the modern world brought the realization of its possibility, it is only with the practical experience of cybernetics and AI that the contemporary discourse of machinic life can be formulated. Nonetheless, this dual nature of contemporary discourse embodies the convergence of different theories in biological, mechanical and computational systems within a multidisciplinary approach, driven by complexity and information. Furthermore, as we will see in the next chapter, the limits of machinic life in understanding and building working models of the mind can already be seen in how cybernetics and AI equate human nature with the nature of the machine, leading to the distinction between hardware and software.

Machinic Life and Its Discontents – II

Consolidated during the Macy Conference which took place in 1946 in New York City, and guided by the works of Norbert Wiener, Arturo Rosenbleuth (1943) and Warren McCulloch (1943),7 cybernetics was the first framework capable of generating a working theory of machines (Johnston, 2008). Its influence has spread throughout different disciplines such as sociology, psychology, ecology, and economics, as well as in popular culture (cyberculture). The prefix cyber-, in fact, would become emblematic of a new understanding of the human condition as profoundly intertwined with machines. Supported by statistical information theory, experimental psychology, behaviorism and control theory, Norbert Wiener (1948) saw in the process of adaptation of the body first described as homeostasis by Walter Cannon (1936), the possibility to simulate the same mechanism in autonomous artificial organisms. Transforming life into a complex adaptive system pairing an organism with its environment through feedback loops, this position conceptually leads to the dissolution of boundaries between natural and artificial, humans and machines, bodies and minds. Human beings and machines become cybernetic subjects, in a world where nature and life are no longer a matter of organic and inorganic substance but of structural complexity (Johnston, 2008). The implications of this view broke the boundaries of human identity, leading theorists to talk of post-humanism and to explore new realms of control and new speculations on the nature of machine simulation (Hayles, 1999).

Despite the variety of subfields developed by Cybernetics,8 the parallel advent of the digital computer obscured most of its paths for decades (Cariani, 2010). The focus of researchers and national funding shifted toward the framework of Artificial Intelligence (AI). This new focus on intelligence, of which consciousness is allegedly a feature, was made possible by establishing a strict relation between the mind – reduced to the brain – and the digital computer. In fact, another revolution was taking place in the field of psychology. The inability of behaviorism, which considers psychological processes as a matter of inputs and outputs, to include mental processes in understanding humans and animals, was opening the doors to the cognitive revolution. The mind, intended as the cradle of cognitive processes, was compared with the digital computer’s information processing, making it possible to test psychological theories and simulate the behavior of mental processes in the artificial brain9 (Miller, 2003). Furthermore, this approach allows to extend the mind-body dualism in the machine as software and hardware. In contrast to cybernetics, which promotes autoregulation in biological and artificial organisms as embodied knowledge acquired through experience (Johnston, 2008),10 AI and its subtending computer philosophy foster the division between hardware and software, abstracting information processing from its physical ground and leading to the consequent obscuration of hardware through software (Kittler, 1992).

Before AI was officially born, in 1950 Alan Turing published an article titled Computing Machinery and Intelligence, in which he designed the imitation game, more widely known as the Turing test. The computational power of the computer was identified with the act of thinking, which is understood as intelligence:

“The reader must accept it as a fact that digital computers can be constructed, and indeed have been constructed, according to the principles we have described, and that they can in fact, mimic the actions of a human computer very closely.” (Turing, 1950)

Because the phrasing of the problem as “can machines think?” can lead to ambiguous results, allowing computer scientists to explore the possibility of creating intelligent machines, Turing reversed the question into a behavioral test – Can we say a machine is thinking when imitating a human so well that s/he thinks s/he is talking to another human? If you can’t recognize that your interlocutor is a machine, then it doesn’t matter whether it is actually thinking, because in any case, the result would be the same: a human-level communication. Thinking and mimicking thinking become equivalent, allowing machines to be called intelligent. In his text, Turing dismisses the argument of phenomenal consciousness and the actual presence of subjective experience by sustaining that such a problem does not necessarily need to be solved before being able to answer his question. Indeed, the Turing test suggests more than a simple game. It signals the beginning of a new inquiry into the theoretical and practical possibility of building “real” intelligent machines while indicating some possible directions11 to build a machine capable of passing this test (Dreyfus, 1972, Rescorla, 2020).

Riding the new wave of the cognitive revolution and embracing the cybernetic comparison between humans and machines, a group of polymaths began to meet in 1956 at Dartmouth College, the birthplace of AI. Developed by Allen Newell and Herbert A. Simon, the Logic Theorist was the first working program exploring the automation of reasoning through its formalization and manipulation within a symbolic system. Called Symbolic AI, this approach would become the workhorse leading the expected escalation from programs limited to performing only a narrow task, narrow artificial intelligence (NAI), to programs capable of doing any task, artificial general intelligence (AGI), and finally to achieve the level of human intelligence, human-level artificial intelligence (HLAI), as prospected by Turing (Russell & Norvig, 2003). Exactly because of this overstated goal and expectations, the fathers of AI12 will be remembered as enthusiastic researchers drawn in a spiral of premature predictions and hyperbolic claims (Dreyfus, 1972) which have mostly failed or are yet to be achieved.

Infected by this early enthusiasm, psychologists and philosophers of science already struggling with the possible equation between the brain and the thinking machine, started to attempt a serious interpretation of the human mind based on the information processing of new computational systems. This approach, called computationalism13 led to several theories (Rescorla, 2020) such as: The Computational Theory of Mind (CTM) introduced in philosophy by Hilary Putnam (1967), which basically understands the mind as a linear input-processing-output machine in the style of the computational model provided by Turing; Jerry Fodor’s Language of Thought Hypothesis (LOTH) and its Representational Theory of Mind (RTM) (1975), which claim that thinking is only possible in a language-like structure to build thoughts at the top level; and the Physical Symbol System Hypothesis (PSSH) of A. Newell and H. Simon (1976), which sees in the physical symbolic system everything needed to build a true intelligence. In popular culture as well, the same enthusiasm led to a new ideology of the machine, climaxing with the fictional character HAL 9000 in the 1968 novel and movie 2001: A Space Odyssey by Arthur C. Clarke and Stanley Kubrick14 (Dreyfus, 1972).

Despite great enthusiasm and high expectations, the idea that computers can do all the things a human can do has been heavily criticized. Philosophers such as Hubert Dreyfus (1965) and Noam Chomsky (1968) started to highlight the problematic aspects of the computationalist theories of mind, beginning a critical analysis of AI that revealed the simplistic assumptions perpetuated by the unjustified hype and incapacity of self-criticism of major AI researchers, and showed the technical limitations of physical symbolic systems. The inability of these systems to grasp the value of context, essential in gaining knowledge and achieving common sense (Russell & Norvig, 2003), and the impossibility to formalize all aspects of intelligence, such as creativity and intuition (Dreyfus, 1972), were recognized as some of the principal boundaries in “decoding” the mind.

In the same direction, philosopher John Searle, criticizing the comparison of the human mind with computers in understanding things, developed a thought experiment called the Chinese room15 (1980), arguing for an underlining distinction between a strong AI capable of true understanding, and a weak AI which merely simulates understanding. Searle’s argument raises the same issues of the aforementioned hard problem of consciousness, defining a threshold between the actual AI and the human mind. Other thought experiments, such as Jackson’s Mary’s room16 (1986) touch the subjectivity of experience directly, which seems to resist all the efforts of the scientific community to reduce it to a machine and its weak computational intelligence.

Machinic Life and Its Discontents – III

Computational symbolic AI postulates that, using a top-down approach, one can engineer all aspects of the mind in digital computers, including consciousness – which is reduced to a mechanism of the brain. However, despite early successes (still limited when compared to the actual goals of HLAI), a succession of failed predictions, conceptual limitations and difficulties in finding commercial applications resulted in two periods of recession between 1974-1980 and 1987-1993, best known as AI winters (Russell & Norvig, 2003). After these periods, criticism moved toward the symbolic approach, and the development of new research inspired by cybernetics led AI researchers to understand intelligence and the design of life through different approaches called sub-symbolics.

Instead of an upstream representation of knowledge typical of the manipulation of symbols, in 1943 cyberneticist Walter McCulloch (1946) was already looking closely at the architecture of the brain, exploring the possibility of reproducing its networks of neurons in artificial neural networks (ANN). However, this system would become effective only in 1986 with Rumelhart, Hinton and McClelland’s parallel distributed processing (PDP), pairing multiple layers of ANNs and drastically increasing their capacity (Russell & Norvig, 2003). The use of ANN in AI explains intelligence from a bottom-up approach, introducing the paradigm called connectionism. These new AI systems are capable of learning and finding useful patterns by inspecting sets of data and reinforcing the connections between their “neurons” (Alpaydin, 2016). Thanks to the internet and developments over the last decade with the deep learning method, ANNs can now be fed with large amounts of data, dramatically increasing their capacity to learn and producing a renewed hype in connectionism and AI.

Another relevant approach in AI resulting from the late influence of cybernetics is the intelligent agent paradigm, described by Stuart J. Russell and Peter Norvig in 2003. Reintroducing the discourse on complex systems, the concept of the rational agent (borrowed from economics) becomes the way to refer to anything capable of interacting with an environment through sensors and actuators. AI systems developed from this perspective are capable of achieving a goal by keeping track of their environment, learning and improving their performance autonomously. In parallel with the developments in AI, cybernetics also highlighted the possibility of simulating biological evolution in software environments. Starting from the arrangement of simple operators called cellular automata (CA) on a grid, and simple laws describing possible interactions, neighbor cells start to reproduce, die, and evolve, forming complex chaotic systems, stable loops and astonishing patterns that are impossible to predict a priori (Johnston, 2008).

These new ways of defining life and intelligence to correct the symbolic approach, are moving toward a deeper understanding of cognition, which instead of being represented only as a symbolic system, also exists on a sub-symbolic level, and instead of being a designed product, is seen as a part of evolutionary processes. However, despite these new developments, AI is encountering its boundaries. Life, like intelligence, is generated from the interaction with an extremely complex and variegated environment, the noisy physical world which is made of radiations and electromagnetic phenomena, particles and wavelengths in continuous interaction. A chaotic world, that neither the capabilities of contemporary computers, nor the amount of data of the internet, can simulate (Johnston, 2008). Furthermore, the companies relying on deep learning are looking into the problem of understanding why these learning systems make the choices they make. Their autonomous way of learning through layers of networked neurons creates nested black boxes extremely difficult to unpack, raising a thorny debate on discrimination and biases embedded in software. To escape from these limitations, scientists are now working on a more holistic understanding of intelligence which combines the sub-symbolic approach with the knowledge representation of symbolic AI (Marcus & Friedman, 2019). In robotics, situated AI is rediscovering the necessity of having a body, and taking robots outside of the labs to interact with the noisy physical world, hoping to find new ways to generate knowledge from direct experience instead of merely simulating it in virtual environments (Russell & Norvig, 2003).

Over the last 20 years, machinic life has started to take seriously its critiques and to reassess the simulation of the adaptive unconscious and embodied knowledge typical of biological organisms (Kahneman & Friedman, 2011) as the possible link to produce the high-level intelligence typical of intuition, creativity and the spontaneous complexity of life. However, almost 70 years after the first AI program, we are still surrounded by only weak-and-narrow AIs. On the one hand, some researchers have reformulated the goal of building human-level systems, as well as “strong AI”, toward less pretentious and more practical aims. On the other hand, despite its turbulent early history of unfortunate claims and the slow growth of connectionism, the perspectives of engineering AGI and strong AI systems, and of populating the world with new forms of artificial life, are growing faster – and these perspectives, again, are leading to more premature claims.

Riding the regenerated hype made possible by the boom of deep learning, private institutions such as MIT, tech entrepreneurs such as Elon Musk,17 and many other researchers in AI-related fields such as the futurist Ray Kurzweil,18 are repeating the same errors of the early fathers. They daydream of a future-oriented, techno-utopianist world that is a direct reminder of the morally dubious neo-liberal Californian Ideology (Barbrook & Cameron, 1995). This new goal, expressed by MIT spokesman Lex Friedman (2018), represents the big picture of AI which passes through the development of AGI and HLAI, to reach the technological singularity as described by the science fiction writer Vernor Vinge:

“a change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater than human intelligence.” (Vinge, 1993)

Eventually, this black-boxed super artificial intelligence (SAI) is expected to develop artificial consciousness on its own, emancipating itself and starting to “think for itself”, becoming the dominant form of life of the future (eventually helping, killing or snubbing human beings).

AI’s big picture infects the nervous system of popular culture. It creates misunderstandings about the actual state of affairs and clamorous expectations of the near future, while discouraging doubts of positive future perspectives. Furthermore, it provides a framework in which the understanding of software and computers in general relies on a matrix of abstractions obscuring their actual nature – still built of man-made mechanisms and weak AI systems that rely on the labor of their producers and the interests of a capitalist market ready to exploit the gullible end user. AI researchers, instead of disregarding the theoretical issues and technical limitations of their approaches, and instead of following the mainstream and commercially appealing big picture of AI, should convert their goals to developing a framework able to confront the problem raised by an in-depth study of consciousness. Machinic life in general should be reframed to allow the study of the mind (and the body) with the primary goal to increase the scientific exploration of consciousness, thus allowing a more complete understanding of nature. At this point in time, however, subjective experience still appears as what differentiates humans from machines, allowing us to imagine a present-oriented future where the big picture of AI is resized to its “weak” actuality and the focus is shifted to fix the natural and social problems that mankind procrastinates with ignorance and presumption. This direction plots out a more fruitful path that, through an understanding of consciousness, automatically leads to a better understanding of life, the world, and the technical systems allowing to design useful AIs with the awareness of their consequences on both the biological and artificial level. In the best case, if machinic life succeeds in engineering phenomenal consciousness, and as professor Matteo Pasquinelli (2014) hopefully interprets the words of Turing, its result will be a new kind of alliance between the two forms of cognition.

Before proceeding with a detailed account of the characteristics of subjective experience, its similarities and differences with the computer, and its relation with software, in the next chapter I will briefly introduce other approaches generated under the influence of machinic life. Instead of a focus on the autonomous machine, these frameworks reframe the human-machine dichotomy, developing the spaces in between these two extremes.

Beyond Humans and Machines

Confining human beings to a totally subaltern level, yet destined to become redundant, the intelligent and autonomous artificial organism conceived by cybernetics and AI implies an unsurpassable threshold between human and machine performance. However, in this power play of configurations between natural and artificial agents, other possible worlds can be articulated. Worlds where humans and machines not only coexist but melt together, achieving that level of close interaction between organisms known as symbiosis and leading to the paradigms of intelligence augmentation (IA) and cyborg theory.

On the one hand, and in parallel to AI, IA claims the possibility of augmenting human intelligence through technological means (Pasquinelli, 2014). Anticipated in 1945 with the prophetic words of Vannevar Bush, speculating on computer interfaces, and theoretically forged in cybernetics by W. Ross Ashby:

“[…] it seems to follow that intellectual power, like physical power, can be amplified. Let no one say that it cannot be done […].” (Ashby, 1956)

Fostered by the visions of J. C. R. Licklider’s man-machine symbiosis (1960) and Simon Ramo’s intellectronics (1961), and through efforts in close collaboration with the United States Department of Defense, the 60s saw the consolidation of this promising paradigm in the development of interactive computing and the user interface. The work of Douglas Engelbart at the Augmentation Research Lab (ARL) and his political plan bootstrapping human intelligence (1962) expected to automatically affect society, will be remembered as the highest peak of IA before disappearing into the less politicized human-computer interaction (HCI) in the late 70s. Nowadays, a new frontier of amplification, interaction and control directly linking the brain with the computer is becoming possible (even though it is still at the earliest stages). The brain-computer interface (BCI) brings us closer to those “disturbing phenomena” collectively known as extrasensory perception “[which] seem to deny all our usual scientific ideas” (Turing, 1950) – the same BCI which Elon Musk’s company Neuralink wants to develop, among other things, as a universal panacea to communicate with the artificial superintelligence of the dystopic near future (Musk & Friedman, 2019).

On the other hand, the disclosure of the cybernetic concept of life dissolves the human-machine dichotomy into an ecosystem of patchworked organisms mixing together artificial and biological parts. This continuum, called machinic phylum by Deleuze and Guattari (1980) (Johnston, 2008), is the home of the cyborg (cybernetic organism) (Haraway, 1985), transforming its body in the playground where internal and external assemblages of parts, like implants different in their substance but communicating through feedback loops, coexist. Cyborg theory represents all the shades articulating the space between what is human and what is a machine. In this direction, Thomas Metzinger (2009) explains how hybrid biorobotics are another framework that is backing away from the purely artificial goal of AI and standard robotics, exploring the possibility of mixed species. The idea is that we can build artificial hardware running biological software, as well as use artificial software to control biological hardware. If the first way is an attempt to deploy patterns emerging in biological neural networks to run on artificial computers, the second finds its example in RoboRoach where the movements of a cockroach are controlled through an artificial implant sending electrical impulses to its nerves. This last approach – reconnecting to the BCI mentioned above – leads, when it is used to directly stimulate the brain, to what Metzinger (2009) calls neuro-enhancement, the artificial control of mental states (as the neuro- version of psycho-pharmacology). Due to the uncertainty of the assumption of machinic life sustaining consciousness that can be instantiated in a substance different than the biological (biological assumption) (Dreyfus, 1972), it seems that the control of the brain through artificial means could be an alternative way to achieve the synthesis of consciousness. Further technological developments in the field of BCI and in the design of non-neural hardware will make it possible to consider to what extent the biological assumption is indeed effectively an assumption, or an actual limit in building artificial consciousness.

All these different configurations, and the consequent understanding of the relation between the human and the machinic, have a common denominator. The first step seems to be the much-acclaimed technological singularity, intended (in less dystopic terms than Vinge’s abovementioned version) as a particular moment in time in which there will be a drastic change in how we deal with technologies. It could be the advent of AGI, HLAI or SAI; the construction of an affordable BCI, or the rise of a cyborg society and the synthesis of artificial consciousness. But the final point, the farthest moment where theories conflate, is the bio-digital fusion that will follow the exponential growth of humans and machines, and actualize the correspondence between the two systems:

“The stars and Galaxies died and snuffed out, and space grew black after ten trillion years of running down. One by one Man fused with AC [Automatic Computer], each physical body losing its mental identity in a manner that was somehow not a loss but a gain. Man’s last mind paused before fusion, looking over a space that included nothing but the dregs of one last dark star and nothing besides but incredibly thin matter, agitated randomly by the tag ends of heat wearing out, asymptotically, to the absolute zero.” (Asimov, 1956)

Part 2

“I am not advocating that we go back to an animistic way of thinking, but nevertheless, I would propose that we attempt to consider that in the machine, and at the machinic interface, there exists something that would not quite be of the order of the soul, human or animal, anima, but of the order of a proto-subjectivity. This means that there is a function of consistency in the machine, both a relationship to itself and a relationship to alterity. It is along these two axes that I shall endeavour to proceed.”
— Felix Guattari, On Machines

Here, Me, Now

Subjective experience is phenomenal consciousness (Block, 2002) and since the standard scientific method relies on an objective account of the mind based on empirical evidence, it cannot directly explain it (Chalmers, 1995). Philosophy, instead, has developed different methods to look at the phenomena (the things that appear to us) in themselves.19 In the late 19th century, Edmund Husserl’s phenomenology inquired about the nature of mental content, acknowledging the possibility to infer objective knowledge about this content and the external world. During the first half of the 20th century, analytic philosophers theorized the sense-data, later the qualia: minimal mind-dependent unities which, when combined together, constitute the whole phenomenal consciousness (Metzinger, 2009). These approaches and the description of the mind portrayed by the aforementioned cognitive revolution involve the mental representation20 of the external world (representational realism) instead of direct contact with it (naive realism) (Metzinger, 2009). Our perception is deconstructed, processed in different areas of the brain, and recomposed in the world as we experience it.21

The contents of our phenomenal consciousness accessible through introspection are resumed in the experience of having a first-person perspective (me) upon the world (here) in a specific moment in time (now). Generally, our point of view takes place from within our body, which is itself represented as part of the world, giving us a sense of embodiment, ownership and selfhood, as well as location, presence, and agency (Metzinger, 2009). On the one hand, the “me” or self, as experienced by humans and a few mammals, is built upon a higher level of consciousness, allowing us to access memories and project into the future, using language and logico-mathematical thinking. Turning the first-person perspective inward, this extended or secondary consciousness makes us particularly self-aware beings, able to explore our own mental states and to account for and experience “experience” itself. The lower level, called core or primary consciousness is common in humans, a large number of mammals, and marine organisms such as octopus, and consists of a more basic form of self-awareness. On the other hand, the representation of space and time persist in most species as a basic level, which neuroscientist Antonio Damasio calls nonconscious protoself (Hayles, 2013, 2014, 2017).22 I will return later to this argument, but it is important to highlight that the absence of a consistent subject capable of inwardness, makes us doubt to which extent certain animals are able to experience emotions and feelings as originating from within themselves. However, the impossibility to know what it is like to be another living being leaves this argument open to debate (Nagel, 1974, Chalmers & Friedman, 2020).

Given the hypothesis that the brain is the sufficient cause for consciousness to exist,23 whatever it is that constitutes this consciousness must have a sort of correlation with the physical brain. This is what scientists call the neural correlate of consciousness (NCC) (Tononi & Koch, 2015), an extremely complex but “coherent island emerging from a less coherent flow of neural activity” that than becomes a more abstract “information cloud hovering above a neurological substrate” (Metzinger, 2009). Clinical cases and limit experiences that are directly accountable (such as neuropsychiatric syndromes, dreams, meditation, use of drugs, and so on) help to map which part of the brain is activated when the experience of the “here, me, now” happens in different circumstances (Metzinger, 2005, 2009, Tononi & Koch, 2015). In fact, in spite of an all-or-nothing process, consciousness is graded and non-unitary, taking place in different phenomenal worlds. If we manage to link a particular subjective experience with a pattern of chattering neurons, we could get closer to solving the hard problem of consciousness. In particular, the first step to explain subjective experience would be to solve the one-world problem: how different phenomenal facts are merged together (world binding) in a coherent whole, and defining particular NCCs should lead to finding the global NCC and the minimal NCC necessary for phenomenal consciousness to take place (Metzinger, 2009).

In his book Ego Tunnel, Thomas Metzinger (2009) defines consciousness as “the appearance of a world”; the brain is understood as a “world engine” capable of creating a wide variety of explorable phenomenal worlds. In particular, Metzinger focuses on the phenomenal worlds of dreams and out-of-body experiences (OBE) in order to develop a functionalist, reductionist theory of consciousness. These states of mind, where a complete experience of disembodiment can be achieved, have led him to develop a particular definition of self, that instead of being a stable instance, is a process running in our brain when we are conscious, and turning off when we fall into a dreamless sleep. Exactly as the experience of the here and now is possible because these exist as internal mental representations, Metzinger’s self is identified with the phenomenal self-model (PSM) created for better control over the whole organism, and the phenomenal model of intentionality relation (PMIR) as the model of its relations to others. Although the internal modeling of the “here, me, now” allows a deeper understanding of phenomenal consciousness than simulated virtual reality, Metzinger claims that “no such things as selves exist in the world”. This provocative claim, however, might be misleading in understanding the nature of the ego, which, notwithstanding the perspective of an internal self-model, seems more ontologically rooted when we consider the tangibility of experience itself (Hayles, 2013, Chalmers & Friedman, 2020).

Metzinger, like other researchers, tries to explain why it really looks like we are living in a simulation created by our own brains. While conscious experience seems to take place far away from the physical world, as an indirect representation, it seems to dwell in a place other than the physical brain, which instead is the object of study of most of the scientific community. Drawing a liminal space existing between our brain and the physical world, and claiming a reality of the phenomenal world closer to dreams, Antti Revonsuo calls the experience of being “here, me, now” reasonably an “out-of-brain experience” (as cited in Metzinger, 2009).

Engines and Experiences

If the actualization of the computer metaphor24 in a computationalist perspective has its limitations in practice, kept as a metaphor it helps us think about many aspects of our beings. In particular, the difference between hardware and software reflects our struggle to interpret the relation between our body and our mind, our brain and our consciousness. The first part of this text highlighted how computers can produce symbolic and sub-symbolic operations, evolutionary dynamics and embodied knowledge, resulting in external behaviors identical to those of living beings. However, the available thinking machines cannot be said to be conscious. Most evidently, computers lack that active individual instance called “self” which causes a world to appear. But what about the “here” and the “now” of computers?

In her book Hamlet on the Holodeck, literary critic Janet H. Murray (1997) develops a theory of new media based on their literary nature. She quotes an excerpt from Italo Calvino’s If on a winter’s night a traveler, describing the experience of a writer in front of his typewriter:

“Every time I sit down here I read, ‘It was a dark and stormy night…’ and the impersonality of that incipit seems to open the passage from one world to the other, from the time and space of here and now to the time and space of the written word; I feel the thrill of a beginning that can be followed by multiple developments, inexhaustibly.” (as cited in Murray, 1997)

Murray explains how the overwhelming capacity of the analog text to project the reader into its world is reconfigured and augmented in new media. Not only can the text be translated into a digital file, displayed and multiplied, but the whole nature of computer software, where the digital text takes shape, is itself textual. Both the stack of layers of programming language and the binary code dwelling at its foundation are texts expressing meaning. This backstage of computers has been used critically in literature (Hayles, 2004, Goldsmith, 2011), and software art (Cramer & Gabriel, 2001, Cramer, 2002), unveiling the textual nature and the conceptual realm of the processes undergoing the graphical user interface (GUI), and comparing these to human nature. Referred to as the Rorschach metaphor (Nelson, 1974, Turkle, 1984), the projective character of digital media is increased by the unique spatial aspects of the software’s environment. Often called cyberspace, this represents a geographical space through which we can move, in an interactive process of navigation and exploration. Furthermore, the user/interactor, the active part of this process, triggers certain events to happen in a temporal immediacy:

“You are not just reading about an event that occurred in the past; the event is happening now, and, unlike the action on the stage of a theater, it is happening to you.” (Murray, 1997)

Integrating space and time, software enables a world to be experienced. As the brain described by Metzinger, the hardware of computers works as a world engine. However, because of the absence of an internal experiencing consciousness making the world appear, their “here” and “now” is actualized only through the subjective experience of an external “me”. Similarly to this view of software as potential worlds, the computer scientist and pioneer in educational software Seymour Papert developed the concept of the microworld:

“[The microworld is] a little world, a little slice of reality. It’s strictly limited, completely defined […]. But it is rich. […] The microworld is created and designed as a safe place for exploring. You can try all sorts of things. You will never get into trouble. You will never feel ‘stupid’.” (Papert, 1987)

The microworld works as an educational tool, helping children to learn how to operate and design multiple contained digital environments. In the long term, this knowledge of different small worlds can be used to create something larger: a macroworld (Papert, 1987).

What we call “software” is a stack of abstractions relying on each other but, in the end, it is nothing more than electrical impulses happening on the physical level of the hardware; in fact, “there is no software” (Kittler, 1992). The same happens with our consciousness, which scientists have continuously been trying to reduce to the brain itself; and, to paraphrase Metzinger, “there is no self”. However, the influence of software in our society is widespread. The worlds created by software shape the physical world, and, in many regards, it is increasingly considered a cultural object worthy of being studied in depth. Something similar is happening to the self, which is actually experienced as more than an abstract model switching on and off. It seems to contain the instruments enabling us to transform a meaningless physical world into a meaningful phenomenal universe (Chalmers & Friedman, 2020), worth being explored and giving us the means to create our complex society. When the self interacts with the self-less computer, the projective mechanisms of the textual software are activated, transporting the individual to experience a new phenomenal world. From this view, if the hardware represents the physical level, the software is not a property of the hardware, but represents the possibility of a phenomenal world which is actualized only when experienced by a self. This phenomenal dimension that the software acquires can be described as an out-of-hardware experience, exactly because it is experienced by a conscious subject located outside of the hardware.

However, if the software is experienced out-of-hardware, and consciousness is experienced out-of-brain, where is subjective experience exactly located? The identification of subjectivity within the hardware is typical of that researcher whose scientific approach negates and reduces subjective experience to the mechanism of a brain. These researchers easily tend to alienate their own selves, idealizing computers as living organisms and predicting the ability of these computers to generate consciousness autonomously. Instead, when the problem is posed in these terms, the individual can claim back their power over the machine in shaping the center of phenomenal consciousness. In fact, the one-world problem of subjective experience mentioned earlier assumes that one world is first needed for consciousness to take place. A first mental simulation is necessary, and then, from this one world, other simulations similar to the microworlds described by Papert can be performed, predicting the results of an action or recalling a past event.25 However, the hardware and the brain are two different kinds of world engine. They are two different systems and, even when producing the same results, they differ precisely in substance, structure, and processes (Dreyfus, 1972). When we experience software, a phenomenal world, other than the simulation of our main world, opens up in front of us. From the inside of the first world out-of-brain, a second world out-of-hardware can appear. In computer science, when a system runs a simulation of another system, this is called emulation. Given this notion, the brain and the hardware can be understood respectively as a world simulator and a world emulator, when seen from the perspective of subjective experience.

Extending Cognition

To better understand the relationship connecting humans and machines, it is necessary to understand how the phenomenal “here, me, now”, the compound of consciousness, differs from the “here” and “now” of the selfless world of software. This debate has progressed in many directions – however, the foundational elements for understanding this relationship have been there all along.

A first connection is already contained in Erewhon (1872), the main novel of the aforementioned forerunner of cybernetics, Samuel Butler – and also in its influence upon the work of Deleuze and Guattari. With a title meant to be read backward (an anagram of “nowhere”), Erewhon contains a section titled the book of the machine in which consciousness was for the first time seen as binding humans and machines. Deleuze’s critique of representation (1968), articulated by his concepts of difference and repetition, would reframe this term not just as a no-where but as a now-here.26 Later, in their collaborative work Anti-Oedipus, Deleuze and Guattari (1972) would relate the same term to their concept of desiring-machine, and Butler’s understanding of machines to the body without organs. Finally, Guattari (1995) would describe the machine as a proto-singularity differing from biological organisms but closely related to their nature.

The term proto-singularity suggests a direct link to the aforementioned proto-self, defined ten years later by Damasio (2000) as the collection of brain devices that continuously and nonconciously maintain the state of the body within the narrow range and relative stability required for survival, and representing the deep roots of the elusive sense of “self” of conscious experience. Still referring to two different domains, technical and biological, the theoretical correspondence of these terms can be traced back to the offspring of cybernetics of the late 60s,27 and in particular to the research of biologists Humberto Maturana and Francisco Varela (Guattari, 1995, Hayles, 1999, 2017). Maturana and Varela first developed the idea that cognition emerges in living systems from their ability to self-organize as self-contained systems (autopoiesis), later broadening this position to include the sensorimotor capacity of the organism to match and interact with its specific environment (enaction). Proposing an alternative to computationalism and connectionism, the enactivist paradigm extends cognition beyond the brain and consciousness, into the nonconscious inner processes happening in the organism as a body (embodied cognition) that interact with an external environment (situated cognition) (Hayles, 2014, 2017, Pasquinelli, 2014, Rescorla, 2020). This radical view of cognition can be extended furthermore outside of the body to create frameworks including not only animals and plants but also technological systems, and eventually, natural processes (distributed cognition) (Hutchins, 2000, Hayles, 2004, 2014, Pasquinelli, 2014), getting closer to the panpsychist view in which the mind becomes a fundamental element of the whole of reality.

In her recent works, Katherine N. Hayles (2014, 2017) reframes Damasio’s protoself as nonconscious cognition, emphasizing the extension of cognition outside of consciousness into embodied and situated processes, and the relevance of the nonconscious as a new cognitive sphere including both biological and technical systems. Furthermore, because cognition presupposes interpretation and production of meaning,28 the nonconscious provides a framework, which she calls cognitive assemblages, to extend social theory beyond anthropocentrism and consciousness, into a cognitive ecology of human and nonhuman cognizers. Differing from the unconscious in its inaccessibility through conscious states, the nonconscious posits itself in between material processes and consciousness, providing the first layer of meaningful representations needed by consciousness to take place. Furthermore, according to new empirical proofs, the nonconscious works faster and can process a larger amount of information than consciousness, preventing the latter from being overwhelmed. However, with its ability to choose, at its simplest level between a zero and a one, and to perform faster than consciousness, the technical nonconscious can condition our decisions and behaviors, making new techniques of surveillance and control possible.29 From these perspectives, the study of computational media becomes a necessity to complete a coherent map of social interactions and to openly accept their active role in the production of culture.

The framework of the nonconscious cognition developed by Hayles provides a working model to understand the actual relationship between consciousness and software. In fact, given an extended cognition beside consciousness, on the “biological” side, we find conscious processes relying on internal, dynamical representations provided by the biological nonconscious. These representations, that are maps of the environment and the body continuously updated in a window of time, provide consciousness with the building blocks of an embodied sense of self and a point of view through which it experiences a coherent phenomenal world. On the “artificial” side, there is no consciousness and self to reinterpret the representations provided by the technical nonconscious. Furthermore, far from being embodied and situated within biological organisms, the technical nonconscious is an embedded system burnt in silicon, compiling and interpreting lines of text internally stored and manifesting its represented content through an interface. This technical cognitive process happens in real time, like the biological one, and represents the abstract spatial dimension described by its code providing the “here” and “now” necessary for a world to appear. The software stands for the possibility of the representational processes of the technical nonconscious to be extrapolated, internalized and re-represented by a consciousness that integrates its “self” to experience a new phenomenal world as an out-of-hardware experience.

Conclusion: A Walk Through the Language Maze

The attempts of the proponents of machinic life to build autonomous machines, and the articulations of human-machine symbiosis, are essential steps in exploring the processes of cognition. However, these frameworks rely on premature assumptions perpetuated as pretended actualities, while they fail to consider the consequences of their claims and products for the public at large. The focus of these disciplines should change, because the symbiosis is clearly already happening and it necessarily changes our lives and our societies through a “control without control”. Indeed, the mimesis of consciousness in technical systems, and its underlying faith in a true artificial consciousness, must rely on an understanding of biological consciousness and, eventually, it must be reframed in accordance. Instead of the rush to increase the capabilities of technical systems, developing a science of consciousness is a necessary step that we must take first, and that will allow us to disclose the nature of subjective experience and reorganize our understanding of the physical world, the biological and technical systems, and the mind.

In the same trajectory, understanding consciousness provides new means to look beyond consciousness itself. It allows us to find the natural position of an elusive object of inquiry which, because it is observable only inside ourselves as subjects, has been used for ages to perpetrate an unnatural anthropocentrism now seen to be threatened by our own technologies. The extension of cognition outside of consciousness, which is already envisioned in Intelligence Augmentation and the cyborg theory, allows us to think of a natural social ecology where different forms of cognition, conscious or not, shape each other in a communal influence. Instead of being a threat, it opens new physical and intellectual relationships with new forms of cognition in and beyond the biological realm.

The interaction between human beings and the technical system through software, as discussed in this thesis, is a comment on the validity of such developments and an insistence of the necessity to continue in this direction. It envisions new ways to articulate the study of software, on the one hand, by standing firmly in the materiality of the physical processes that constitute it and reduce it completely to the hardware, and on the other hand, by highlighting how the interaction with a conscious subject makes it possible to rethink software in terms of an experiential world abstracted by underlying material processes. Drastically different from other phenomena, which fail to provide the complexity of an experiential world, software can be arguably said to augment consciousness, instead of only augmenting cognition and intelligence. The technical questions as to the validity and consequences of this thesis rely on the next developments in our understanding of consciousness, of the physical world, and of their relationship which still is uncertain. These developments, as many people would like to fantasize, might help us to understand that consciousness can be actually instantiated in artificial machines able to “feel” feelings and perceive themselves as embodied in a physical world. They might help us to consider inhabitable, artificially simulated worlds, which currently are still games that can’t be mistaken for real worlds. But right now, we must be able to understand why this is not actually happening, and why we are still deeply different from machines.

Perhaps the link between the human and the machine consists of a maze created by their intertwining layers of languages30 – a “language maze” made of verbal and non-verbal languages, natural languages and formal languages, computer code and machine languages. A Daedalus’s labyrinth of material – informational, algorithmic and literarily explorable spaces developing in the horizontal and the vertical dimensions, from microscopic to macroscopic territories. A Penelope’s web made of rooms hiding recursive simulations and emulations of other rooms, of other mazes, and of itself. Perhaps what distinguishes the human from the machine is the capacity, illusory or real, of being whole with the “language maze” as an infinite space in which to build new worlds from scratch.

“The Labyrinth is presented, then, as a human creation, a creation of the artist and of the inventor, of the man of knowledge, of the Apollonian individual, yet in the service of Dionysus the animal-god.”
— Giorgio Colli, La nascita della filosofia (The Birth of Philosophy)


  1. Not meant to be exhaustive, this historical account of machinic life (which comprise Cybernetics, symbolic and sub-symbolic AI and a brief mention of ALife) provides a sporadic overview of a much more interesting and articulated story that is still in progress, and of which a more detailed account can be found elsewhere. 

  2. Developed within Deleuze and Guattari’s machinic philosophy (1972, 1980) (Johnston, 2008), the term “machinic” postulates “the existence of processes that act on an initial set of merely coexisting, heterogeneous elements, and cause them to come together and consolidate into a novel entity.” (DeLanda, 1997). 

  3. “An art, skill, or craft; a technique, principle, or method by which something is achieved or created.” (Oxford Dictionary) 

  4. Formulated by Auguste Comte in the early 19th century, positivism rejects subjective experience because it is not verifiable by empirical evidence. 

  5. “I want to know what is characteristic of piety which makes all actions pious […] that I may have it to turn to, and to use as a standard whereby to judge your actions and those of other men.” (as cited in Dreyfus, 1972) 

  6. “Once the characteristic numbers are established for most concepts, mankind will then possess a new instrument which will enhance the capabilities of the mind to far greater extent than optical instruments strengthen the eyes, and will supersede the microscope and telescope to the same extent that reason is superior to eyesight.” (as cited in Dreyfus, 1972) 

  7. McCulloch’s seminal work is particularly relevant as a first comparison between the brain and digital information processing, anticipating both computationalism and connectionism (Dreyfus, 1972, Rescorla, 2020). 

  8. “[…] self-organizing systems, neural networks and adaptive machines, evolutionary programming, biological computation, and bionics.” (Cariani, 2010) 

  9. Hubert Dreyfus (1972) refers respectively to cognitive simulation (CS) and artificial intelligence (AI) “in a narrower sense”

  10. Johnston (2008) explicitly positioned cybernetics in opposition to the previous argument by Hayles (1999) “that cybernetics […] effected a ‘disembodiment’ of information by defining it independently from its material substrate”

  11. Natural language processing, problem-solving, chess-playing, the child program idea, and genetic algorithms. 

  12. In her account of the early days of AI, Pamela McCorduck (1979) recognizes Allen Newell, Herbert A. Simon, Marvin Minsky and John McCarthy as the early fathers of this discipline (McCorduck & Friedman, 2019). 

  13. This approach is often called functionalism in philosophy (Block, 2002), even though this is a generalization (Rescrola, 2020). 

  14. HAL 9000 is depicted as a malevolent human-like artificial intelligence capable of feeling emotions; the character was designed with the technical consultancy of Marvin Minsky (Dreyfus, 1972). 

  15. “Suppose that I’m locked in a room and given a large batch of Chinese writing […] [but] to me, Chinese writing is just so many meaningless squiggles. Now suppose further that after this first batch of Chinese writing I am given a second batch of Chinese script together with a set of rules for correlating the second batch with the first batch. The rules are in English, and I understand these rules as well as any other native speaker of English. They enable me to correlate one set of formal symbols with another set of formal symbols, and all that ‘formal’ means here is that I can identify the symbols entirely by their shapes. Now suppose also that I am given a third batch of Chinese symbols together with some instructions, again in English, that enable me to correlate elements of this third batch with the first two batches, and these rules instruct me how to give back certain Chinese symbols […] from the point of view of somebody outside the room in which I am locked – my ‘answers’ to the questions are absolutely indistinguishable from those of native Chinese speakers. Nobody just looking at my answers can tell that I don’t speak a word of Chinese.” (Searle, 1980) In conclusion, a machine following a code, exactly as the person locked in the Chinese room, doesn’t “really” understand its inputs and outputs. 

  16. “MARY is confined to a black-and-white room, is educated through black-and-white books and through lectures relayed on black-and-white television. In this way she learns everything there is to know about the physical nature of the world. She knows all the physical facts about the environment […] If physicalism is true, she knows all there is to know. […] It seems, however, that Mary does not know all there is to know. For when she is let out of the black-and-white room or given a color television, she will learn what it is like to see something red, say. This is rightly described as learning – she will not say ‘ho, hum.’ Hence physicalism is false.” (Jackson, 1986) In conclusion, subjective experience can’t be reduced to a code and therefore “strong AI” is not possible with symbolic AI. 

  17. In particular, for some of the goals of their companies and their various claims about the advent of AGI (Musk & Friedman 2019). 

  18. In particular, for their previsions and their way of popularizing the advent of AGI in their books. 

  19. Eastern philosophy already developed a philosophy of mind, centuries before Christ, that is still particularly relevant for recent developments in the scientific understanding of consciousness (Hayles, 2017). 

  20. These mental representations can be understood as functional models produced by the evolutionary process and naturally selected for their survival and adaptive value (Metzinger, 2008). 

  21. In general, this scientific view is based on empirical data, implying that the physical reality described by nuclear and quantum physics exists, and that our phenomenal experience is projected on top of it. 

  22. Hayles (2017) refers to the works on neuroscience of Antonio Damasio and the Nobel laureate Gerald Edelmann, which respectively define the two levels of consciousness as core and extended consciousness and primary and secondary consciousness

  23. This is the essential condition for a reductionist theory of mind (Metzinger, 2008). 

  24. The computer metaphor, which compares the brain to a computer, is emphasized here in terms of “metaphor” rather than “computer” – which instead, when compared to computationalist approaches, should be understood as a “computational system” (Rescorla, 2020). 

  25. This nested hierarchical structure is common in conscious mental simulations as well as in software, where the operating system runs at the top level. 

  26. “Butler’s Erewhon seems to us not only a disguised no-where but a rearranged now-here” (Deleuze, 1968). 

  27. This late and last offspring of cybernetics is called second-order cybernetics (Johnston, 2008, Hayles, 1999). 

  28. The field studying the production of meaning in the biological realm is called biosemiotics (Hayles & Sampson, 2018). 

  29. This problem is known as the missing half-second (Hayles, 2014, 2017). 

  30. “Language” here is understood in the broader sense, including non-verbal language and sensorial perception, to suggest all possible signifiers.7 

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