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AISB50 – Celebrating 50 years of the AISB 1st – 4th April 2014, Goldsmiths, University of London. Representation of Reality: Humans, Animals and Machines Symposium. Reality Construction Through Info-Computation. Gordana Dodig Crnkovic Professor of Computer Science

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Reality Construction Through Info-Computation


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    1. AISB50 – Celebrating 50 years of the AISB 1st – 4th April 2014, Goldsmiths, University of London Representation of Reality: Humans, Animals and Machines Symposium Reality Construction Through Info-Computation Gordana Dodig Crnkovic Professor of Computer Science Mälardalen University, School of Innovation, Design and Engineering gordana.dodig-crnkovic@mdh.se

    2. Mälardalen University Sweden

    3. IDT PhD School @ Mälardalen University Sweden

    4. REALITY Representation of Reality: Humans, Animals and Machines Symposium What is reality for an agent? How does reality of a bacterium differ from a reality of a human brain? Do we need representation in order to understand reality?

    5. REALITY 1 something that actually exists Synonyms: actuality, case, materiality Related Words: certainty, inevitability; circumstance, event, occurrence, phenomenon; element, item, particular, thing Near Antonyms: eventuality, possibility, potentiality, probability Antonymsfantasy: (also phantasy), fiction, illusion 2 the fact of being or of being real Synonyms: actuality, corporality, corporeality, reality, subsistence, thingness Related Words: realness; presence, prevalence Near Antonyms: absence, potentiality, virtuality Antonyms: inexistence, nonbeing, nonexistence, nothingness, unreality

    6. REALITY 3 the quality of being actual Synonyms: actuality, factuality, materiality Related Words: authenticity, genuineness, truth, verity Near Antonyms: fancy, fantasy, fiction, surreality Antonyms: irreality, unreality 4 one that has a real and independent existence Synonyms: being, substance, thing Related Words: body, subject; material, matter, quantity, stuff Near Antonyms: nonentity

    7. 1 WHAT IS REALITY (FOR AN AGENT)? When discussing cognition as a bioinformatic process of special interest, we use the notion of agent, i.e. a system able to act on its own behalf [1]. Agency in biological systems has been explored in [2][3]. The world (reality) as it appears to an agent depends on the type of interaction through which the agent acquires information and on agents own information-processing [1]. Groups of agents communicate by exchanging messages (information) that help them coordinate their actions based on the (partial) information they possess and share as a part of social cognition.

    8. COGNITION AS LIFE Agency and cognition is a property of all living organisms. Agents themselves can consist of networks of agents, recursively. A single biological cell consists of network of agents. Networks of cells form tissues that form organs that form organisms that organize in ecologies. The question is how artifactual agents should be built in order to possess different degrees of cognition and eventually even consciousness. Is it possible at all, for an artifactual agent to be cognitive given that cognition in living organisms is a deeply biologically rooted process connected to survival?

    9. LANGUAGE AS A TOOL OF HIGH LEVEL COGNITION Increasing levels of cognition developed in living organisms evolutionary, starting from basic automatic minimally adaptive behaviours such as found in bacteria and even insects (even though insects have nervous system and brain, they lack the limbic system that controls emotional response to physical stimuli, suggesting they don't process physical stimuli emotionally) to increasingly complex behaviour in higher organisms such as mammals.

    10. LANGUAGE AS A TOOL OF HIGH LEVEL COGNITION Recent advances in natural language processing, such as Watson computer that wins Geopardy, present examples of developments towards machines capable of both “understanding natural language” and “speaking” in a human way. Along with reasoning, language is often considered a high-level cognitive activity that only humans are capable of. Can AI “jump over” evolutionary steps in the development of cognition and base language use on pure machine learning from vast data sets?

    11. INFO-COMPUTATIONAL FRAMEWORK FOR STUDY OF COGNITION The framework for the discussion here is thecomputing nature in the form of info-computationalism. It takes reality to be information for an agent with a dynamics of information understood as computation. Information is a structure and computation its dynamics. Information is observer relative and so is computation. [1][4][5]

    12. INFO-COMPUTATIONAL FRAMEWORK FOR STUDY OF COGNITION Cognition is studied as information processing in such simple organisms as bacteria [6], [7] as well as cognitive processes in other, more complex multicellular life forms. We discuss computational mind and consciousness that have recently been widely debated in the work of Giulio Tononi [8] and Christoph Koch. [9]

    13. INFO-COMPUTATIONAL FRAMEWORK FOR STUDY OF COGNITION While the idea that cognition is a biological process in all living organisms, as argued by Humberto Maturana and Francisco Varela [10], [11], it is not at all clear that all cognitive processes in different kinds of organisms are accompanied by anything akin to (human) consciousness. The suggestion is made that cognitive agents with nervous systems are the step in evolution that first enabled consciousness of the kind that humans possess. Argument is advanced that ascribing consciousness to the whole of the universe is not justified.

    14. REALITY AS INFORMATION FOR AN AGENT • Defining reality as information leaves us with the question:what is it in the world that corresponds to information and its dynamics, computation?How do we model information/ computation? Answers are many and they are not unambiguous.

    15. We can compare the present situation regarding information, computation and cognitionwith the history of the development of other basic scientific concepts. Ideas about matter, energy, space and time in physics have their history. The same is true of the idea of number in mathematics or the idea of life in biology. So, we should not be surprised to notice the development in the theory of computation that goes hand in hand with the development of information science, cognitive science, computability, robotics, new computational devices and new domains of the real world that can be understood info-computationally.

    16. 1 WHAT IS REALITY (FOR AN AGENT)? “Whatever is a reality today, whatever you touch and believe in and that seems real for you today, is going to be, like the reality of yesterday, an illusion tomorrow.” Luigi Pirandello, Six Characters in Search of an Author The father, in Six Characters in Search of an Author, act 3 (1921).

    17. 1 WHAT IS REALITY (FOR AN AGENT)? Chun Siong Soon, Marcel Brass, Hans-Jochen Heinze & John-Dylan Haynes, “Unconscious Determinants of Free Decisions in the Human Brain.” Nature Neuroscience, April 13th, 2008. Would we agree that reality resides in that which is now, taking into account that our cognitive apparatus has a finite resolution in time (it might be as much as 7 seconds delay between decision and action*) – where now would be measured, perhaps in minutes? What about phenomena that change more slowly? For such phenomena, “now” could be days, or years depending on the phenomenon. But if it is longer time than what we immediately observe, then the reality must be based not only on current perception/understanding but also on memory. How about reality of future (anticipated) events? What is a difference of a highly probable event (such that the Earth revolves the Sun hundred years from now)?

    18. 1 WHAT IS REALITY (FOR AN AGENT)? Undobtedly, we base our decisions/actions on both memory, current observations and anticipations. There is a difference between fiction or virtual reality and anticipated event based on firm past evidence. Degree of reality varies between anticipated highly probable event and fiction or virtual representation of any state of similar event.

    19. 2 THE COMPUTING NATURE. COMPUTATIONAL NATURALISM AND MINIMAL COGNITION

    20. 3 INFORMATIONAL STRUCTURE OF REALITY FOR A COGNITIVE AGENT

    21. 4 COMPUTATION IN NETWORKS OF AGENTS

    22. 5 MORPHOLOGICAL/ MORPHOGENETIC COMPUTING

    23. 6 GENERATION OF REALITY FROM RAW DATA

    24. 7 INFO-COMPUTATION, AGENCY AND MATTER-ENERGY

    25. 8 COMPUTATION ALL THE WAY DOWN TO QUANTUM

    26. 9 COMPUTATIONAL MODELS OF MIND EXCULPATED

    27. 10 WHY PANCOMPUTATIONALISM IS USEFUL AND PANPSYCHISM IS NOT

    28. 11 CONCLUSIONS AND FUTURE WORK

    29. COMPUTATIONALISM IS NOT WHAT IT USED TO BE… … that is the thesis that persons are Turing machines. Turing Machine following a given algorithm may be used for description of certain aspects of the functioning of living organisms. However, modeling the basic characteristics of life is 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 and exhibiting constantly the same behavior.

    30. COMPUTATIONALISM IS NOT WHAT IT USED TO BE… … that is the thesis that persons are Turing machines. Computational models that are capable of adaptation, evolution and learning are found in the field of natural computation and computing nature. Cognitive computing is one of the attempts to construct abiotic system exhibiting cognitive characteristics.

    31. Cognitive Information Processing Theory of Learning According to (Gagné, 1985) This is an old and simplistic idea of cognition as information processing. Missing in this scheme are feedback loops that are absolutely essential for cognition and learning. Also missing is information integration from different sensors and couplings to actuators. Memory is not a passive storage but active ingredient in perception, that is both used for recognition and anticipation. Cognitive / Information Processing Theory of Learning according to (Gagné, 1985)

    32. Info-computational Framework The open question about levels of abstraction 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 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) Whatever exists for an agent comes in a form of information. Information stands for matter-energy of the physical world.

    33. PROPOSED INFO-COMPUTATIONAL FRAMEWORK Keywords: Computing nature, Info-computationalism, Morphological computing, Information physics, Evolution with Self-organization and Autopoiesis, Actors and Agent Networks. The world presents potential information for an agent. Information is relational. Computation is in general information processing. Suitable model for computation within info-computational framework is Hewitt’s Actor model. Hewit’s actors can be seen as agents. Info-computationalism is a kind of physicalism where physical matter is represented by information, and information processing is physical computation.

    34. LIFE AS INFO-COMPUTATIONAL GENERATIVE PROCESS OF COGNITION AT DIFFERENT LEVELS OF ORGANIZATION An agent is an entity capable of acting on its own behalf. It can be seen as an "actor" in the Actor model of computation in which "actors" are the basic elements of concurrent computation exchanging messages, capable of making local decisions and creating new actors. Computation is thus distributed in space where computational units communicate asynchronously and 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.) (Hewitt, 2012)

    35. ACTOR MODEL OF 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.

    36. 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 rise to the level of “Agenthood ” when they competently process expressions for commitments including the following: Contracts, Announcements, Beliefs, Goals, Intentions, Plans, Policies, Procedures, Requests, Queries. In other words, his agents are human-like.

    37. LIFE AS INFO-COMPUTATIONAL GENERATIVE PROCESS OF COGNITION AT DIFFERENT LEVELS OF ORGANIZATION This paper presents a study within info-computational constructive framework of the life process as <knowledge> generation in living agents from the simplest living organisms to the most complex ones. Here <knowledge> of a primitive life form is very basic indeed – it is <knowledge> how to act in the world. An amoeba <knows> how to search for food and how to avoid dangers.

    38. LIVING AGENTS A living agent is a special kind of actor that can reproduce and 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.

    39. LIVING AGENTS 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, this work is primarily interested of the info-computational aspects of life. Info-computational approach takes information an computation to be the two basic building block concepts, corresponding to structure and process, being and becoming. Given that there is no information without physical implementation (Landauer, 1991), computation as the dynamics of information is the execution of physical laws.

    40. LIVING AGENTS 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. This development smoothly connects to info-computational understanding of nature (Dodig-Crnkovic & Giovagnoli, 2013).

    41. THE COMPUTING NATURE Life can be analyzed as cognitive processes unfolding in a layered structure of nested information network hierarchies with corresponding computational dynamics (information processes) – from molecular, to cellular, organismic and social levels. In order to construct life as cognitive process we will introduce two fundamental theories about the nature of the universe and propose their synthesis: The first one with focus on processes is the idea of computing universe (naturalist computationalism/ pancomputationalism) in which one sees the dynamics of physical states in nature as information processing (natural computation).

    42. THE COMPUTING NATURE The parallel fundamental theory with focus on structures is Informational structural realism (Floridi, 2003) that takes information to be the fabric of the universe (for an agent). 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.

    43. THE COMPUTING NATURE information is defined as the difference in one physical system that makes the difference in another physical system. This implies relational character of information and thus agent-dependency in agent-based or actor model. As a synthesis of informational structural realism and natural computationalism, info-computational structuralism adopts two basic concepts: information (as a structure) and computation (as a dynamics of an informational structure) (Dodig-Crnkovic, 2011) (Chaitin, 2007). In consequence the process of dynamical changes of the universe makes the universe a huge computational network where computation is information processing. (Dodig-Crnkovic & Giovagnoli, 2013) Information and computation are two basic and inseparable elements necessary for naturalizing cognition and <knowledge>. (Dodig-Crnkovic, 2009)

    44. THE COMPUTING NATURE Agents -systems able to act on their own behalf and make sense (use) of information are of special interest with respect to <knowledge> generation. This relates to the ideas of participatory universe, (Wheeler, 1990) endophysics (Rössler, 1998) and observer-dependent <knowledge> production.

    45. MORPHOLOGICAL COMPUTATION – FROM SIMPLEST TO THE MOST COMPLEX ORGANISMS In the computing nature, <knowledge> generation should be studied as a natural process. That is the main idea of Naturalized epistemology (Harms, 2006), where the subject matter is not our concept of <knowledge>, but the knowledge itself as it appears in the world as specific informational structures of an agent. Maturana and Varela were the first to suggest that knowledge is a biological phenomenon. They argued that life should be understood as a process of cognition, which enables an organism to adapt and survive in the changing environment. (Maturana & Varela, 1980)

    46. NETWORK AGENT/ACTOR MODELLS Protein network in yeast cells Human protein interaction network Human connectome Social network

    47. Human brain is biological information processor - network of neurons processing information http://neuralethes-en.blogspot.se/2012/04/human-connectome-project.html Human Connectome Project

    48. WORLD AS INFORMATION FOR AN AGENT From: http://www.alexeikurakin.org

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

    50. 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 big 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