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Neuroscience Weighs in on Physics' Biggest Questions - Issue 107: The Edge

Nautilus

For an empirical science, physics can be remarkably dismissive of some of our most basic observations. We see objects existing in definite locations, but the wave nature of matter washes that away. We perceive time to flow, but how could it, really? We feel ourselves to be free agents, and that's just quaint. Physicists like nothing better than to expose our view of the universe as parochial. But when asked why our impressions are so off, they mumble some excuse and slip out the side door of the party. Physicists, in other words, face the same hard problem of consciousness as neuroscientists do: the problem of bridging objective description and subjective experience. To relate fundamental theory to what we actually observe in the world, they must explain what it means "to observe"--to become conscious of. And they tend to be slapdash about it. They divide the world into "system" and "observer," study the former intensely, and take the latter for granted--or, worse, for a fool.


The Hard Problem of Consciousness Has an Easy Part We Can Solve - Facts So Romantic

Nautilus

What might its relationship to matter be? And why are some things conscious while others apparently aren't? These sorts of questions, taken together, make up what's called the "hard problem" of consciousness, coined some years ago by the philosopher David Chalmers. There is no widely accepted solution to this. But, fortunately, we can break the problem down: If we can tackle what you might call the easy part of the hard problem, then we might make some progress in solving the remaining hard part.


What would it be like to be a conscious AI? We might never know.

#artificialintelligence

But not everyone was prepared to disregard the invisible parts of thinking, the irreducible experience of the thing having the thoughts--what we would call consciousness. In 1948, two years before Turing described his "Imitation Game," Geoffrey Jefferson, a pioneering brain surgeon, gave an influential speech to the Royal College of Surgeons of England about the Manchester Mark 1, a room-sized computer that the newspapers were heralding as an "electronic brain." Jefferson set a far higher bar than Turing: "Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain--that is, not only write it but know that it had written it." Jefferson ruled out the possibility of a thinking machine because a machine lacked consciousness, in the sense of subjective experience and self-awareness ("pleasure at its successes, grief when its valves fuse"). Yet fast-forward 70 years and we live with Turing's legacy, not Jefferson's.


A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machine

arXiv.org Artificial Intelligence

The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. We examine consciousness from the perspective of theoretical computer science (TCS), a branch of mathematics concerned with understanding the underlying principles of computation and complexity, including the implications and surprising consequences of resource limitations. In the spirit of Alan Turing's simple yet powerful definition of a computer, the Turing Machine (TM), and perspective of computational complexity theory, we formalize a modified version of the Global Workspace Theory (GWT) of consciousness originated by cognitive neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene, Jean-Pierre Changeaux and others. We are not looking for a complex model of the brain nor of cognition, but for a simple computational model of (the admittedly complex concept of) consciousness. We do this by defining the Conscious Turing Machine (CTM), also called a conscious AI, and then we define consciousness and related notions in the CTM. While these are only mathematical (TCS) definitions, we suggest why the CTM has the feeling of consciousness. The TCS perspective provides a simple formal framework to employ tools from computational complexity theory and machine learning to help us understand consciousness and related concepts. Previously we explored high level explanations for the feelings of pain and pleasure in the CTM. Here we consider three examples related to vision (blindsight, inattentional blindness, and change blindness), followed by discussions of dreams, free will, and altered states of consciousness.


Electrons May Very Well Be Conscious - Facts So Romantic

Nautilus

Last year, the cover of New Scientist ran the headline, "Is the Universe Conscious?" Mathematician and physicist Johannes Kleiner, at the Munich Center for Mathematical Philosophy in Germany, told author Michael Brooks that a mathematically precise definition of consciousness could mean that the cosmos is suffused with subjective experience. "This could be the beginning of a scientific revolution," Kleiner said, referring to research he and others have been conducting. Kleiner and his colleagues are focused on the Integrated Information Theory of consciousness, one of the more prominent theories of consciousness today. As Kleiner notes, IIT (as the theory is known) is thoroughly panpsychist because all integrated information has at least one bit of consciousness.


Why a 'genius' scientist thinks our consciousness originates at the quantum level

#artificialintelligence

Human consciousness is one of the grand mysteries of our time on earth. How do you know that you are "you"? Does your sense of being aware of yourself come from your mind or is it your body that is creating it? What really happens when you enter an "altered" state of consciousness with the help of some chemical or plant? While you would think this basic enigma of our self-awareness would be at the forefront of scientific inquiry, science does not yet have strong answers to these questions.


On Thinking Machines, Machine Learning, And How AI Took Over Statistics

#artificialintelligence

A few months after Samuel's TV appearance, ten computer scientists convened in Dartmouth, NH, for the first-ever workshop on artificial intelligence, …


Making the hard problem of consciousness easier

Science

The history of science includes numerous challenging problems, including the “hard problem” ([ 1 ][1]) of consciousness: Why does an assembly of neurons—no matter how complex, such as the human brain—give rise to perceptions and feelings that are consciously experienced, such as the sweetness of chocolate or the tenderness of a loving caress on one's cheek? Beyond satisfying this millennia-old existential curiosity, understanding consciousness bears substantial medical and ethical implications, from evaluating whether someone is conscious after brain injury to determining whether nonhuman animals, fetuses, cell organoids, or even advanced machines ([ 2 ][2]) are conscious. A comprehensive and agreed-upon theory of consciousness is necessary to answer the question of which systems—biologically evolved or artificially designed—experience anything and to define the ethical boundaries of our actions toward them. The research projects described here will hopefully point the way and indicate whether some of today's major theories hold water or not. After prosperous decades of focused scientific investigation zeroing in on the neural correlates of consciousness ([ 3 ][3]), a number of candidate theories of consciousness have emerged. These have independently gained substantial empirical support ([ 4 ][4]–[ 7 ][5]), led to empirically testable predictions, and resulted in major improvements in the evaluation of consciousness at the bedside ([ 8 ][6], [ 9 ][7]). Notwithstanding this progress, the conjectures being put forward by the different theories make diverging claims and predictions that cannot all be simultaneously true. Moreover, the theories evolve and continue to adapt as further data accumulates, with hardly any cross-talk between them. How can we then narrow down on which theory better explains conscious experience? The road to a possible solution may be paved by means of a new form of cooperation among scientific adversaries. Championed by Daniel Kahneman in the field of behavioral economics ([ 10 ][8]) and predated by Arthur Eddington's observational study to test Einstein's theory of general relativity against Newton's theory of gravitation ([ 11 ][9]), adversarial collaboration rests on identifying the most diagnostic points of divergence between competing theories, reaching agreement on precisely what they predict, and then designing experiments that directly test those diverging predictions. During the past 2 years, several groups have adopted this approach, following an initiative that aims to accelerate research in consciousness. So far, several theories of consciousness are being evaluated in this manner to test competing explanations for where and when neural activity gives rise to subjective experience. The global neuronal workspace theory (GNWT) ([ 4 ][4]) claims that consciousness is instantiated by the global broadcasting and amplification of information across an interconnected network of prefrontal-parietal areas and many high-level sensory cortical areas. The sensory areas carry out different functions that range from feature processing to object or word recognition. Information in those sensory areas is processed in encapsulated modules, remaining unconscious. The frontal-parietal networks support integrative and executive functions, including selective attention and working memory. According to the GNWT, a stimulus must be attended to trigger activity that helps distribute this sensory information to many parts of the brain for further processing and report. It is this global broadcasting across many modules of specialized subsystems that constitutes consciousness. Conversely, the integrated information theory (IIT) (5) holds that consciousness should be understood in terms of cause-effect “power” that reflects the amount of maximally irreducible integrated information generated by certain neuronal architectures. On the basis of mathematical and neuroanatomical considerations, the IIT holds that the posterior cortex is ideally situated for generating a maximum of integrated information. In this theory, consciousness is not input-output information processing but the intrinsic ability or power of a neuronal network to influence itself. That is, the neuronal substrate of consciousness perpetuates itself for as long as the experience exists. The more cause-effect power a system has, the more conscious it is. For the IIT, the content of an experience is a structure of causes and effects (integrated information), whereas for the GNWT, it is a message that is broadcast globally. ![Figure][10] Testing hypotheses by adversarial collaboration The neural correlates of consciousness for the global neuronal workspace theory (GNWT) and for the integrated information theory (IIT) occupy distinct and overlapping regions in the brain. Each theory predicts synchronization of activity between or within these regions. GRAPHIC: N. CARY/ SCIENCE Another controversy occurs between first-order ([ 12 ][11], [ 13 ][12]) and higher-order ([ 6 ][13], [ 14 ][14]) theories of consciousness. The former claims that reverberating activity in sensory areas suffices for consciousness, whereas the latter claims that a second, higher-order brain state must represent or “point at” these first-order sensory activations for them to be consciously experienced. Both controversies are the types of theoretical disagreements that are currently being empirically tested by use of the adversarial collaboration approach. One of these collaborations, the COGITATE consortium (Collaboration On GNW and IIT: Testing Alternative Theories of Experience), is collecting data and has recently released a detailed preregistered report that outlines the methods, predictions, and planned analyses (). These experiments were designed by neuroscientists and philosophers who are not directly associated with the theories but are in close collaboration with advocates from each theory. The experiments are being conducted in six independent laboratories. Briefly, one of the experimental designs involves an engaging video game with seen and unseen stimuli in the background to determine whether neural correlates of the visual experience are present irrespective of the task. In another experiment, stimuli are shown for variable durations to investigate for how long the neural correlate of the visual experience exists. Neuronal activity in human subjects is measured with both invasive and noninvasive methodologies, from functional magnetic resonance imaging and simultaneous magnetoencephalography and electroencephalography to invasive electrocorticography, and is integrated across methodologies to test the theories' predictions. These focus on two key questions: Where are the anatomical footprints of consciousness in the brain: Are they located in a posterior cortical “hot zone” ([ 15 ][15]) advocated by the IIT, or is the prefrontal cortex necessary ([ 4 ][4]) as predicted by the GNWT? And, how are conscious percepts maintained over time: Is the underlying neural state maintained as long as the conscious experience lasts, in line with the IIT, or is the system initially ignited and then decays and remains silent until a new ignition marks the onset of a new percept, as the GNWT holds (see the figure)? Once the brain data are collected and analyzed, they will be made available to anyone. Relying on adversarial dialogue and collaboration, open science practices, standardized protocols, internal replication, and team science, these initiatives aim to promote empirical progress in the field of consciousness and to change the sociology of scientific practice in general. Solving big questions may require “big science” because such questions are more likely to be solved in unison rather than through isolated, parallel, small-scale attempts. The adversarial collaboration approach builds on the success of large-scale collaborative institutes (such as the Allen Institute for Brain Science) and projects such as the Human Connectome Project or the International Brain Laboratory in neuroscience, which were preceded by initiatives in physics such as the Large Hadron Collider at the European Organization for Nuclear Research (CERN) or the Laser Interferometer Gravitational-Wave Observatory (LIGO) experiment. With this series of adversarial collaborations, neuroscientists will get closer to understanding consciousness and how it fits into the physical world while improving scientific practices along the way. As for the initial theories undergoing this approach, it may be that neither the GNWT nor the IIT are quite correct. No matter the outcome, the field can use the results to make progress in framing new thinking about consciousness and testing other potential theories in the same way. The problem of consciousness will surely remain difficult, but understanding the ancient mind-body problem will become a little bit easier. 1. [↵][16]1. D. J. Chalmers , J. Conscious. Stud. 2, 200 (1995). [OpenUrl][17] 2. [↵][18]1. T. Bayne et al ., Trends Neurosci. 43, 6 (2020). [OpenUrl][19][PubMed][20] 3. [↵][21]1. F. Crick, 2. C. Koch , Nat. Neurosci. 6, 119 (2003). [OpenUrl][22][CrossRef][23][PubMed][24][Web of Science][25] 4. [↵][26]1. G. A. Mashour, 2. P. Roelfsema, 3. J. P. Changeux, 4. S. Dehaene , Neuron 105, 776 (2020). [OpenUrl][27] 5. 1. G. Tononi, 2. M. Boly, 3. M. Massimini, 4. C. Koch , Nat. Rev. Neurosci. 17, 450 (2016). [OpenUrl][28][CrossRef][29][PubMed][30] 6. [↵][31]1. R. Brown et al ., Trends Cogn. Sci. 23, 754 (2019). [OpenUrl][32][CrossRef][33][PubMed][34] 7. [↵][35]1. V. A. F. Lamme , Cogn. Neurosci. 1, 204 (2010). [OpenUrl][36][CrossRef][37][PubMed][38][Web of Science][39] 8. [↵][40]1. A. Demertzi et al ., Sci. Adv. 5, eaat7603 (2019). [OpenUrl][41][FREE Full Text][42] 9. [↵][43]1. A. G. Casali et al ., Sci. Transl. Med. 5, 198ra105 (2013). [OpenUrl][44][Abstract/FREE Full Text][45] 10. [↵][46]1. D. Kahneman , Am. Psychol. 58, 723 (2003). [OpenUrl][47][CrossRef][48][PubMed][49] 11. [↵][50]1. F. W. Dyson, 2. A. S. Eddington, 3. C. Davidson , Philos. Trans. R. Soc. A. 220, 291 (1920). [OpenUrl][51][CrossRef][52] 12. [↵][53]1. V. A. F. Lamme, 2. P. R. Roelfsema , Trends Neurosci. 23, 571 (2000). [OpenUrl][54][CrossRef][55][PubMed][56][Web of Science][57] 13. [↵][58]1. N. Block , Trends Cogn. Sci. 9, 46 (2005). [OpenUrl][59][CrossRef][60][PubMed][61][Web of Science][62] 14. [↵][63]1. H. Lau, 2. D. Rosenthal , Trends Cogn. Sci. 15, 365 (2011). [OpenUrl][64][CrossRef][65][PubMed][66][Web of Science][67] 15. [↵][68]1. C. Koch, 2. M. Massimini, 3. M. Boly, 4. G. Tononi , Nat. Rev. Neurosci. 17, 666 (2016). [OpenUrl][69] Acknowledgments: COGITATE is supported by a grant from the Templeton World Charity Foundation (TWCF) ([www.templetonworldcharity.org/accelerating-research-consciousness-our-structured-adversarial-collaboration-projects][70]). The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of TWCF. L.M. is a Canadian Insititute for Advanced Research Tanenbaum Fellow in the Brain, Mind, and Consciousness program. C.K. thanks the Allen Institute founder, Paul G. Allen, for his vision, encouragement, and support. The authors thank D. Potgieter for championing the adversarial collaboration concept and acknowledge the COGITATE consortium: K. Bentz, H. Blumenfeld, D. Chalmers, F. de Lange, S. Dehaene, S. Devore, F. Fallon, O. Ferrante, U. Gorska, R. Hirschhorn, O. Jensen, A. Khalaf, C. Koch, C. Kozma, G. Kreiman, A. Lepauvre, L. Liu, H. Luo, L. Melloni, L. Mudrik, M. Pitts, D. Richter, G. Tononi. 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Conscious AI

arXiv.org Artificial Intelligence

Recent advances in artificial intelligence (AI) have achieved human-scale speed and accuracy for classification tasks. In turn, these capabilities have made AI a viable replacement for many human activities that at their core involve classification, such as basic mechanical and analytical tasks in low-level service jobs. Current systems do not need to be conscious to recognize patterns and classify them. However, for AI to progress to more complicated tasks requiring intuition and empathy, it must develop capabilities such as metathinking, creativity, and empathy akin to human self-awareness or consciousness. We contend that such a paradigm shift is possible only through a fundamental shift in the state of artificial intelligence toward consciousness, a shift similar to what took place for humans through the process of natural selection and evolution. As such, this paper aims to theoretically explore the requirements for the emergence of consciousness in AI. It also provides a principled understanding of how conscious AI can be detected and how it might be manifested in contrast to the dominant paradigm that seeks to ultimately create machines that are linguistically indistinguishable from humans.


AI is reengineering what it means to be 'human'

#artificialintelligence

We have come together to fight Covid-19 and AI was a key enabler to bring to market vaccines, in unprecedented clinical trial R&D timeframes, to eradicate this virus, and help us get back to a more interactive global community where we can freely travel, visit our favourite restaurants and shop with more access in our local retailer stores. This is an excellent example of AI being used for good. However, much of AI in large global data sets are full of inequalities, incumbencies and biases of the innovators designing AI which have a direct impact on how the technology guides human information, perception and action. As AI leads society towards the next phase of human evolution, it is becoming increasingly more evident that we need to acutely increase our knowledge of AI ethics and reflect on the future world we want to create. Can we create an intelligence that is unconstrained by the limitations and prejudices of its creators to have AI serve all of humanity, or will it become the latest and most powerful tool for perpetuating and magnifying racism and inequality?