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Mind as a Layered Network of Computational Processes all the Way Down to Quantum

Mind as a Layered Network of Computational Processes all the Way Down to Quantum

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Mind as a Layered Network of Computational Processes all the Way Down to Quantum

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  1. IACAP 14 Conference of the International Association for Computing and Philosophy Mind as a Layered Network of Computational Processes all the Way Down to Quantum Gordana Dodig Crnkovic Professor of Computer Science School of Innovation, Design and EngineeringMälardalen University, Chalmers & University of GothenburgSweden http://www.idt.mdh.se/~gdc/

  2. Mälardalen University Chalmers & GU - Sweden Mind as a layerednetworkofcomputationalprocesses all the way down to quantum

  3. Response to a classical criticism against computational models of mind Classical criticism against computational models of mind is based on a model of computation as a discrete symbol manipulation of the Turing machine type. In this work I will present alternative computational model that avoids all the problems of old computationalism and meets Sprevak’s desiderata for a computational theory of mind. This is just a very broad first sketch of the naturalized computational approach that will need years to complete incorporating the results of new projects on brain research and new computational models such as DNN and others.

  4. The plan of my talk In this talk I will argue that mind can be seen as manifestation of a layered network of computational processes all the way down to quantum. The common framework through all layers is built on information (as a relational concept that defines structures for an agent) and computation that represents a dynamics of informational structure. Mind as a highest level of information processing dissolves into cognition in a lower level that dissolves into metabolism on the lowest level of organization that dissolves into chemistry that dissolves in physics which ends with quantum level. Levels are essential for understanding of mind as a structured complex process. Mind as a layerednetworkofcomputationalprocesses all the way down to quantum

  5. What is mind? the Oxford dictionary definition The element of a person that enables them to be aware of the world and their experiences, to think, and to feel; the faculty of consciousness and thought: a lot of thoughts ran through my mind A person’s ability to think and reason; the intellect: his keen mind A person’s memory: the company’s name slips my mind A person identified with their intellectual faculties: he was one of the greatest minds of his time As such kind of higher order process, mind relies on lower level processes that we can identify with cognition. Mind is set of processes in which consciousness, perception, affectivity, judgment, thinking, and will are based. Mind as a layered network of computational processes all the way down to quantum

  6. Mind: relationships with other concepts http://www.visuwords.com Mind as a layered network of computational processes all the way down to quantum

  7. What is cognition? After half a century of research in cognitive science, cognition still lacks a commonly accepted definition (Lyon, 2005). Textbook description of cognition: “all the processes by which sensory input is transformed, reduced, elaborated, stored, recovered and used” (Neisser, 1967) is so broad that it includes present day robots. On the other hand, the Oxford dictionary definition: “the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses” applies only to humans. *Mental = relating to the mind. Mind is set of processes in which consciousness, perception, affectivity, judgment, thinking, and will are based. Mind as a layered network of computational processes all the way down to quantum

  8. Cognition - relationships Mind as a layered network of computational processes all the way down to quantum

  9. Mind vs. cognition Mind as a layered network of computational processes all the way down to quantum

  10. Cognition at different levels of organization of living organism – from cells up • Traditional anthropogenic approach to cognition* – only humans are cognitive agents • Biogenic approaches* – cognition is ability of all living organisms, no matter how “primitive” – goes a level below the complexity of human language – to complex systems chemical signaling and regulation processes. (Maturana & Varela, 1980; Maturana, 1970), argued that cognition and life are identical processes. • New sub-biotic approaches to cognition assume that it is possible to construct cognitive agents starting from abiotic systems – a level below biogenic cognition. The question is if abiotic systems can be considered cognitive, in what sense and on which level. * (Lyon, 2005) Mind as a layered network of computational processes all the way down to quantum

  11. Connecting anthropogenic with biogenic and abiotic cognition We focus on cognition and propose the common framework for understanding Anthropogenic, Biogenic and Abiotic Cognition. We argue that (as in the rest of biology) – nothing makes sense except for in the light of evolution (Dobzhansky, 1973) and the cognition as a process can only be understood in the light of evolution. Regarding abiotic systems we will compare their “cognitive behavior” with living organisms, and draw conclusions. Mind as a layered network of computational processes all the way down to quantum

  12. Living as a process is a process of cognition • “A cognitive system is a system whose organization defines a domain of interactions in which it can act with relevance to the maintenance of itself, and the process of cognition is the actual (inductive) acting or behaving in this domain. Living systems are cognitive systems, and living as a process is a process of cognition. This statement is valid for all organisms, with and without a nervous system.” (Maturana, 1970) • In 1991, Kampis proposed a unified model of computation as the mechanism underlying biological processes through “self-generation of information by non-trivial change (self-modification) of systems” (Kampis, 1991. Self-Modifying Systems in Biology and Cognitive Science: A New Framework for Dynamics, Information and Complexity). Mind as a layered network of computational processes all the way down to quantum

  13. Cognition as biological phenomenonStarting from anthropogenic perspective: The brain development “The brain development may be carried out based on the basic body-organization-blueprints that are specific to an animal species depending on their strategy to survive in an environment. To understand how our brains are established in the course of evolution, we have been conducting a comparison of the structure and function of the gene that are essential for establishing body organization and brain development in a wide rage of animals with nervous system.” When we model higher levels of organization as generated by organization of lower levels, we acknowledge the importance of context for a process to develop a structure. http://lcn.brain.riken.jp/tool_kit_evolution.htm Mind as a layered network of computational processes all the way down to quantum

  14. Digression: wonders of evolution – the smallest insect with brain, smaller than an amoeba • Size of the smallest insect and two protozoans in comparison. • Megaphragma mymaripenne. • Paramecium caudatum. • Amoeba proteus. • Scale bar for A–C is 200 μm. B and C are made up of a single cell, A the wasp complete with eyes, brain, wings, muscles, guts – is actually smaller. This wasp is the third smallest insect alive. the smallest nervous systems of any insect, consisting of just 7,400 neurons. Housefly has 340,000 Honeybee has 850,000. 95% of the wasps’s neurons have no nucleus. http://www.sciencedirect.com/science/article/pii/S1467803911000946 The smallest insects evolve anucleate neuronsArthropod Structure & Development, Volume 41, Issue 1, January 2012, Pages 29–34 Mind as a layered network of computational processes all the way down to quantum

  15. Human brain studies Natural information processing Henry Markram (2012) The Human Brain Project, Scientific American 306, 50 – 55 Human connectome http://outlook.wustl.edu/2013/jun/human-connectome-project http://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdf The Human Brain Project

  16. Current brain research initiatives The Human Brain Project (HBP) is a large scientific research project, directed by the École Polytechnique Fédérale de Lausanne and largely funded by the European Union, which aims to simulate the complete human brain on supercomputers to better understand how it functions. The BRAIN Initiative(Brain Research through Advancing Innovative Neurotechnologies, also referred to as the Brain Activity Map Project) is a proposed collaborative research initiative announced by the Obama administration on April 2, 2013, with the goal of mapping the activity of every neuron in the human brain.Based upon the Human Genome Project, the initiative has been projected to cost more than $300 million per year for ten years.(Wikipedia) The Allen Institute large-scale brain mapping. In 2012 three additional major research initiatives: Neural Coding (understanding how information is encoded and decoded in the mammalian brain) Molecular Networks (understanding how information is encoded and decoded within a cell) Cell Types (large-scale descriptive resources of human and mouse brain cell types at molecular, morphological and connectional levels) Atlasing (collection of online public resources integrating extensive genomic and neuroanatomic data) http://www.alleninstitute.org/science/research_programs/index.html

  17. Current computational modeling in cognitive science Symbolic modeling evolved from the computer science paradigms using the technologies of Knowledge-based systems - "Good Old-Fashioned Artificial Intelligence" (GOFAI). Used in expert systems and cognitive decision making, and extended to socio-cognitive approach. Subsymbolic modeling includes Connectionist/neural network models.

  18. Current computational modeling in cognitive science Dynamical systems theory closely related to ideas about the embodiment of mind and the environmental situatedness of human cognition based on physiological and environmental events. The most important here is the dimension of time. Neural-symbolic integration techniques putting symbolic models and connectionist models into correspondence. Bayesian models of brain function whichassume that the nervous system maintains internal probabilistic models that are updated by neural processing of sensory information using methods approximating those of Bayesian probability.

  19. Reality for an agent - informational structure with computational dynamics Information is defined as the difference in one physical system that makes the difference in another physical system. This reflects the relational character of information and thus agent-dependency which calls for agent-based or actor models. As a synthesis of informational structural realism and natural computationalism, I propose info-computational structuralism that builds on two basic concepts: information (as a structure) and computation (as a dynamics of an informational structure) (Dodig-Crnkovic, 2011). (Dodig-Crnkovic & Giovagnoli, 2013) Information and computation are two basic and inseparable elements necessary for naturalizing <cognition>. (Dodig-Crnkovic, 2009)

  20. Information as a fabric of reality for an agent (information is relational) “Information is the difference that makes a difference. “Gregory Bateson It is the difference in the world that makes the difference for an agent. Here the world includes agents themselves too. “Information expresses the fact that a system is in a certain configuration that is correlated to the configuration of another system. Any physical system may contain information about another physical system.” Carl Hewitt Bateson, G. (1972). Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology pp. 448–466). University Of Chicago Press. Hewitt, C. (2007). What Is Commitment? Physical, Organizational, and Social. In P. Noriega, J. Vazquez, Salceda, G. Boella, O. Boissier, & V. Dign (Eds.), Coordination, Organizations, Institutions, and Norms in Agent Systems II (pp. 293 –307). Berlin, Heidelberg: Springer Verlag.

  21. Information structures as a fabric of reality (thus structured/organized data) for an agent Informational structural realism (Floridi, Sayre) argues that information (for an agent) constitutes the fabric of reality: Reality consists of informational structures organized on different levels of abstraction/resolution. See also: Van Benthem and Adriaans (2008) Philosophy of Information, In: Handbook of the philosophy of science series. http://www.illc.uva.nl/HPI Ladyman J. and Ross D., with Spurrett D. and Collier J. (2007) Every Thing Must Go: Metaphysics Naturalized, Oxford UP Floridi, L. (2008) A defence of informational structural realism, Synthese161, 219-253. Sayre, K. M. (1976) Cybernetics and the Philosophy of Mind, Routledge & Kegan Paul, London.

  22. The relational definition of information Combining definitions of Bateson: “Information is a difference that makes a difference.”(Bateson, 1972) and Hewitt: ”Information expresses the fact that a system is in a certain configuration that is correlated to the configuration of another system. Any physical system may contain information about another physical system.”(Hewitt, 2007), we get: Information is defined as the difference in one physical system that makes the difference in another physical system.

  23. Structure vs. processInformation vs. computation For all living agents, information is the fabric of reality. But: the knowledge of structures is only half a story. The other half are changes, processes – information dynamics. (In classical formulation: being and becoming.) Information processing will be taken as the most general definition of computation. This definition of computation has a profound consequence – if computation is the dynamics of informational structures of the universe, the dynamics of the universe is a network of computational processes (natural computationalism). Dodig-Crnkovic, G., Dynamics of Information as Natural Computation, Information 2011, 2(3), 460-477; Selected Papers from FIS 2010 Beijing, 2011.

  24. It is important to notice:Computationalism is not what it used to be… … that is, the thesis that persons are Turing machines. Turing Machine is a model of computation equivalent to algorithm and it may be used for description of different processes in living organisms. We need computational models for the basic characteristics of life as the ability to differentiate and synthesize information, make a choice, to adapt, evolve and learn in an unpredictable world. That requires computational mechanisms and models which are not mechanistic and predefined as Turing machine.* * We need learning such as PAC Probably Approximately Correct – Leslie Valiant

  25. Computationalism is not what it used to be …… that is the thesis that persons are Turing machines Computational approaches that are capable of modelling adaptation, evolution and learning are found in the field of natural computation and computing nature. Cognitive computing and cognitive robotics are the attempts to construct abiotic systems exhibiting cognitive characteristics. It is argued that cognition comes in degrees, thus it is meaningful to talk about cognitive capabilities of artifacts, even though those are not meant to assure continuing existence, which was the evolutionary role of cognition in biotic systems.

  26. Actor model of computation suitable to support the relational concept of information - concurrent distributed computation “In the Actor Model [Hewitt, Bishop and Steiger 1973; Hewitt 2010], computation is conceived as distributed in space, where computational devices communicate asynchronouslyand the entire computation is not in any well-defined state. (An Actor can have information about other Actors that it has received in a message about what it was like when the message was sent.) Turing's Model is a special case of the Actor Model.” (Hewitt, 2012) Hewitt’s “computational devices” are conceived as computational agents – informational structures capable of acting on their own behalf.

  27. Actor model of concurrent distributed computation Actors are the universal primitives of concurrent distributed digital computation. In response to a message that it receives, an Actor can make local <decisions>, create more Actors, send more messages, and designate how to respond to the next message received. For Hewitt, Actors become Agents only when they are able to process expressions for commitments including the following: Contracts, Announcements, Beliefs, Goals, Intentions, Plans, Policies, Procedures, Requests, Queries. In other words, Hewitt’s Agents are human-like or if we broadly interpret the above capacities, life-like Actors.

  28. Computing nature and nature inspired computation This image, by IBM scientists using an atomic force microscope, shows a nanographene molecule with carbon-carbon bond Subatomic particles Atoms Bacterial colony a multi-cellular “organism” Unicellular organism DNA molecule Bacteria collectively “collects latent information from the environment and from other organisms, process the information, develop common knowledge, and thus learn from past experience”(Ben-Jacob, 2009) Peter J. Denning. 2007. Computing is a natural science. Commun. ACM 50, 7 (July 2007), 13-18. DOI=10.1145/1272516.1272529 http://doi.acm.org/10.1145/1272516.1272529 http://www.ted.com/talks/bonnie_bassler_on_how_bacteria_communicate

  29. Hierarchy of human body down to atoms • Levels of organization (interrelatedness) • Physical • Chemical • Cellular • Tissue • Organ • System • Organismal • Social • Ecological http://www.pc.maricopa.edu http://www.cea1.com/anatomy-sistems/body-systems-2/

  30. Computing cells: self-generating systems “a component system is a computer which, when executing its operations (software) builds a new hardware.... [W]e have a computer that re-wires itself in a hardware-software interplay: the hardware defines the software and the software defines new hardware. Then the circle starts again.” Kampis (1991) p. 223 Kampis (1991) Self-Modifying Systems in Biology and Cognitive Science. A New Framework For Dynamics, Information, and Complexity, Pergamon Press Dodig Crnkovic, G. (2011). Significance of Models of Computation from Turing Model to Natural Computation. Minds and Machines, (R. Turner and A. Eden guest eds.) Volume 21, Issue 2, p.301. Complex biological systems must be modeled as self-referential, self-organizing "component-systems" (George Kampis) which are self-generating and whose behavior, though computational in a general sense, goes far beyond Turing machine model.

  31. World as information for an agent Actual Information processing that creates reality for C-elegans Potential information - outside world for C-elegans Cognition as interaction interface for C-elegans between potential information of the outside world and actual information of its inner world C. Elegans has 302 neurons (humans have 100 billion). The pattern of connections between neurons has been mapped out decades ago using electron microscopy, but knowledge of the connections is not sufficient to understand (or replicate) the information processor they represent, for some connections areinhibitorywhile others are excitatory. http://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.html

  32. Reality for an agent – an observer-dependent reality Reality for an agent is an informational structure with which agent interacts. As systems able to act on their own behalf and make sense (use) of information, cognitive agents are of special interest with respect to <knowledge>* generation. This relates to the idea of participatory universe, (Wheeler, 1990) “it from bit” as well as to endophysics or “physics from within” where an observer is being within the universe, unlike the “god-eye-perspective” from the outside of the universe. (Rössler, 1998) *<knowledge> for a very simple agent can be the ability to optimize gains and minimize risks. (Popper, 1999) p. 61 ascribes the ability to know to all living: ”Obviously, in the biological and evolutionary sense in which I speak of knowledge, not only animals and men have expectations and therefore (unconscious) knowledge, but also plants; and, indeed, all organisms.”

  33. Info-computational framework and levels The question of levels of organization/levels of abstraction for an agent is analyzed within the framework of info-computational constructivism, with natural phenomena modeled as computational processes on informational structures. Info-computationalism is a synthesis of informational structuralism (nature is an informational structure for an agent) (Floridi, Sayre) and natural computationalism/pancomputationalism(nature computes its future states from its earlier states) (Zuse, Fredkin, Wolfram, Chaitin, Lloyd). Two central books presenting the diversity of research on information and computation: Adriaans P. and van Benthem J. eds. 2008. Philosophy of Information (Handbook of the Philosophy of Science) North Holland. Rozenberg, G., T. Bäck, and J.N. Kok, eds. 2012. Handbook of Natural Computing. Berlin Heidelberg: Springer.

  34. Living agents – basic levels of cognition A living agent is an entity acting on its own behalf, with autopoietic properties that is capable of undergoing at least one thermodynamic work cycle. (Kauffman, 2000) This definition differs from the common belief that (living) agency requires beliefs and desires, unless we ascribe some primitive form of <belief> and <desire> even to a very simple living agents such as bacteria. The fact is that they act on some kind of <anticipation> and according to some <preferences> which might be automatic in a sense that they directly derive from the organisms morphology. Even the simplest living beings act on their own behalf.

  35. Living agents – basic levels of cognition Although a detailed physical account of the agents capacity to perform work cycles and so persist* in the world is central for understanding of life/cognition, as (Kauffman, 2000) (Deacon, 2007) have argued in detail, present argument is primarily focused on the info-computational aspects of life. Given that there is no information without physical implementation (Landauer, 1991), computation as the dynamics of information is the execution of physical laws. *Contragrade processes (that require energy and do not spontaneously appear in nature) become possible by connecting with the orthograde (spontaneous) processes which provide source of energy.

  36. Living agents – basic levels of cognition Kauffman’s concept of agency (also adopted by Deacon) suggests the possibility that life can be derived from physics. That is not the same as to claim that life can be reduced to physics that is obviously false. However, in deriving life from physics one may expect that both our understanding of life as well as physics will change. We witness the emergence of information physics (Goyal, 2012) (Chiribella, G.; D’Ariano, G.M.; Perinotti, 2012) as a possible reformulation of physics that may bring physics and life/cognition closer to each other.

  37. Levels of organization of life/cognition The origin of <cognition> in first living agents is not well researched, as the idea still prevails that only humans possess cognition and knowledge. However, there are different types of <cognition> and we have good reasons to ascribe simpler kinds of <cognition> to other living beings. Bacteria collectively “collects latent information from the environment and from other organisms, process the information, develop common knowledge, and thus learn from past experience”(Ben-Jacob, 2009) Plants can be said to possess memory (in their bodily structures) and ability to learn (adapt, change their morphology) and can be argued to possess simple forms of cognition. Ben-Jacob, E. (2009). Learning from Bacteria about Natural Information Processing. Annals of the New York Academy of Sciences, 1178, 78–90.

  38. Evolution of the nervous system Figure 9-1: Evolution of the nervous system

  39. Information processing in the brain http://www.neuroinformatics2013.org Neuroinformatics Modular and hierarchically modular organization of brain networks D. Meunie, R. Lambiotte and E. T. Bullmore Frontiers of Neuroscience http://www.frontiersin.org/neuroscience/10.3389/fnins.2010.00200/full http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/

  40. An example: developing cognitive computing at IBM

  41. Design and construction of a brain-like computer A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System SubrataGhosh, Krishna Aswani, Surabhi Singh, SatyajitSahu, Daisuke Fujita and AnirbanBandyopadhyay* Information 2014, 5, 28-100; doi:10.3390/info5010028 http://www.mdpi.com/2078-2489/5/1/28

  42. Connecting informational structures and processes from quantum physics to living organisms and societies Nature is described as a complexinformationalstructure for a cognizing agent. Information is the difference in one information structurethat makes a difference in anotherinformation structure. Computation is information dynamics (information processing)constrained and governed by the lawsofphysics on the fundamental level.

  43. Computing nature The basic idea of computing nature is that all processes taking place in physical world can be described as computational processes – from the world of quantum mechanics to living organisms, their societies and ecologies. Emphasis is on regularities and typical behaviors. Even though we all have our subjective reasons why we move and how we do that, from the bird-eye-view movements of inhabitants in a city show striking regularities. In order to understand big picture and behavior of societies, we take computational approach based on data and information. See the work of Albert-László Barabási who studies networks on different scales: http://www.barabasilab.com/pubs-talks.php

  44. Conclusion: Modeling Life as Cognitive Info-computation by Connecting Anthropogenic with Biogenic and Abiotic Cognition We focus on cognition and propose the common framework for understanding anthropogenic, biogenic and abiotic cognition. Cognition for biological system is synonymous with life. Cognition as a process can only be understood in the light of evolution. Within the framework of info-computationalism, reality for an agent is an informational structure with computational dynamics. On different levels of organization, different kinds of cognition operate – from cellular level to organismic and social cognition.

  45. A brain-like computer quantum level up Ghosh, Subrata; Aswani, Krishna; Singh, Surabhi; Sahu, Satyajit; Fujita, Daisuke; Bandyopadhyay, Anirban. 2014. "Design and Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System.” Information 5, no. 1: 28-100. http://www.mdpi.com/2078-2489/5/1/28

  46. Summary. The strategy of info-computational approach to cognition & mind • Even though anthropogenic approach to cognition and mind is the oldest and by far the most dominant one, it is the most difficult approach to the most complex problem – embodied human brain. • The study of biogenic and abiotic cognition can help us trace evolutionary roots of cognitive capacities in living organisms (biogenic) and construct (abiotic) artifacts with cognitive and intelligent behavior (cognitive computing and cognitive robotics). • Therefore we start with simplest living systems such as bacteria to try to understand the basis of their cognitive behavior in informational structures and their dynamics (computational processes). At the same time brain studies provide us with new understanding of human brain and its function (mind).

  47. Information, computation, cognition. Hierarchies of levels based on agency Info-computational approach is based on generalized concepts of information and computation. Short summary of the argument: • <Information> presents a structure consisting of differences in one system that cause the differences in another system. In other words, information is <observer>*-relative. Information is organized/structured data for an agent. • <Computation> is information processing (dynamics of information). It is physical process of morphological change in the informational structure (physical implementation of information, as there is no information without physical implementation.) *<> brackets indicate that the term is used in a broader sense than usually.

  48. Information, computation, cognition. Hierarchies of levels based on agency • Both <information> and <computation> appear on many different levels of organisation/abstraction/resolution/granularity of matter/energy in space/time. • Of all agents (entities capable of acting on their own behalf) only living agentshave the ability to actively make choices so to increase the probability of their own continuing existence. This ability of living agents to act autonomously on its own behalf is based on the use of energy/matter and information from the environment. Mind as a layered network of computational processes all the way down to quantum

  49. Information, computation, cognition. Hierarchies of levels based on agency • <Cognition> consists of all (info-computational) processes necessary to keep living agent’s organizational integrity on all different levels of its existence. <Cognition> = <info-computation> • <Cognition> is equivalent with the (process of) life.* Its complexity increases with evolution.This complexification is a result of morphological computation. • <Mind> is a result of a succession of info-computational processes from quantum level up. * Maturana, H. & Varela, F., 1980. Autopoiesis and cognition: the realization of the living, Dordrecht Holland: D. Reidel Pub. Co.

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