1. Modeling Life as Cognitive Info-computation Gordana Dodig Crnkovic Professor of Computer Science School of Innovation, Design and Engineering Mälardalen University, Sweden http://www.idt.mdh.se/~gdc/ Computability in Europe 2014: Language, Life, Limits. June 23-27, Budapest

2. Mälardalen University Sweden 12,000 students and around 900 employees, of which 67 are professors

3. 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. What is Cognition? p. 3 *Mental = relating to the mind. Mind is set of processes in which consciousness, perception, affectivity, judgment, thinking, and will are based.

4. Cognitive science p. 4 Biology /embodiment/embeddedness (situatedness) Biology /embodiment/embeddedness http://en.wikipedia.org/wiki/Cognitive_science http://cacm.acm.org/magazines/2011/8/114944- cognitive-computing/fulltext

5. ● 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. Cognition, different levels of understanding p. 5 * (Lyon, 2005)

6. ● (Anthropogenic) Cognition is the PROCESS by which humans acquire, integrate and generate knowledge. It is the result of attention, perception, memory, and executive functions of learning and behavior generation (information integration and transformation of perception into higher order symbols; comparison of incoming information with the information stored in memory together with value system and biological drives) ● (Anthropogenic) Intelligence is the ABILITY to understand and reason upon (i.e. structure and interrelate and operate upon) what is perceived, memorized and learned. ● Intelligence as ABILITY is based on cognition as PROCESS. Anthropogenic Cognition vs. Anthropogenic Intelligence p. 6

7. ● (Biogenic and Abiotic) Cognition is the PROCESS by which simple living organisms acquire, integrate and generate. It is the result of attention, perception, memory, and executive functions of learning and behavior generation. ● (Biogenic and Abiotic) Intelligence is the ABILITY to structure, interrelate and operate upon information that is perceived, memorized and learned. ● Intelligence as ABILITY is based on cognition as PROCESS. Similarly, Biogenic and Abiotic Cognition vs. Biogenic and Abiotic (Artifactual) Intelligence p. 7

8. 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. Connecting Anthropogenic with Biogenic and Abiotic Cognition p. 8

9. ● “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 ). Living as a process is a process of cognition 9

10. Information, computation, cognition Agent-centered Hierarchies of Levels In this lecture I will present a unified framework for modeling of information, computation and cognition from an agents perspective. p. 10 informationcomputation cognition Fruit fly brain micrograph Fruit fly brain neurons Fruit fly larva http://www.sciencedirect.com/science/article/pii/S0378437104014839

11. p. 11 Starting from anthropogenic perspective: The brain development - Cognition as biological phenomenon http://lcn.brain.riken.jp/tool_kit_evolution.htm “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.”

12. Wonders of evolution – the smallest insect with brain, smaller than an amoeba p. 12 http://www.sciencedirect.com/science/article/pii/S1467803911000946http://www.sciencedirect.com/science/article/pii/S1467803911000946 The smallest insects evolve anucleate neurons Arthropod Structure & Development, Volume 41, Issue 1, January 2012, Pages 29–34 Size of the smallest insect and two protozoans in comparison. (A)Megaphragma mymaripenne. (B)Paramecium caudatum. (C)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.

13. Information, computation, cognition Agent-centered Hierarchy of Levels p. 13 Human connectome http://outlook.wustl.edu/2013/jun/human-connectome-project http://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientific american0612-50.pdfhttp://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientific american0612-50.pdf The Human Brain Project Henry Markram (2012) The Human Brain Project, Scientific American 306, 50 – 55 Natural information processing

14. 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.European Unionhuman brain supercomputers 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.Obama administrationneuronhuman brainHuman Genome Project Source: Wikipedia Current brain research initiatives 14

15. The Allen Institute conducting and completing large-scale brain mapping projects for the last 10 years. In early 2012 launched three additional major research initiatives to drive critical advances in understanding how the brain works and develops. ● 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) 15 Current brain research initiatives http://www.alleninstitute.org/science/research_programs/index.html

16. ● Even though anthropogenic approach to cognition 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) artifact 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). The Strategy of Info-Computational Approach 16

17. Introducing generalized concepts of information and computation. Short summary of the argument: 1.Information presents a structure consisting of differences in one system that cause the differences in another system. In other words, information is *- relative. 2.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.) Information, computation, cognition. Agency-based Hierarchies of Levels p. 17 *<> brackets indicate that the term is used in a broader sense than usually.

18. 3.Both information and computation appear on many different levels of organisation/abstraction/resolution/granularity of matter/energy in space/time. 4.Of all agents (entities capable of acting on their own behalf) only living agents have 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. Information, computation, cognition. Agency-based Hierarchies of Levels p. 18

19. 5.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 6. Cognition is equivalent with the (process of) life.* Its complexity increases with evolution. This complexification is a result of morphological computation. Information, computation, cognition. Agency-based Hierarchies of Levels p. 19 * Maturana, H. & Varela, F., 1980. Autopoiesis and cognition: the realization of the living, Dordrecht Holland: D. Reidel Pub. Co.

20. “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 Information as a fabric of reality 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. 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/HPIhttp://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 Information structures as a fabric of reality (thus structured/organized data) for an agent Floridi, L. (2008) A defence of informational structural realism, Synthese 161, 219- 253. Sayre, K. M. (1976) Cybernetics and the Philosophy of Mind, Routledge & Kegan Paul, London.

22. 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. The relational definition of information 22

23. 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). Structure vs. process p. 23 Dodig-Crnkovic, G., Dynamics of Information as Natural Computation, Information 2011, 2(3), 460-477; Selected Papers from FIS 2010 Beijing, 2011.

24. 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. (Dodig- Crnkovic, 2009) Reality for an agent - informational structure with computational dynamics 24

25. 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. p. 25

26. 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 which assume that the nervous system maintains internal probabilistic models that are updated by neural processing of sensory information using methods approximating those of Bayesian probability. p. 26

27. 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 p. 27

28. 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. p. 28

29. “Biological processes are often compared to computation and modeled on the Universal Turing Machine. While many systems or aspects of systems can be well described in this manner, Turing computation can only compute what it has been programmed for. (…) Yet, adaptation, choice and learning are all hallmarks of living organisms. This suggests that there must be a different form of computation capable of this sort of calculation. (…) Super-Turing model is both capable of modeling adaptive computation, and furthermore, a possible answer to the computational model searched for by Turing himself.” Turing computation: “Turing on Super-Turing and adaptivity” according to Siegelmann p. 29 Hava T. Siegelmann, Turing on Super-Turing and adaptivity, Progress in Biophysics and Molecular Biology, http://www.sciencedirect.com/science/article/pii/S0079610713000278 http://www.sciencedirect.com/science/article/pii/S0079610713000278

30. p. 30 “In the Actor Model [Hewitt, Bishop and Steiger 1973; Hewitt 2010], computation is conceived as distributed in space, where computational devices 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.) 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. Actor model of concurrent distributed computation

31. Actors are the universal primitives of concurrent distributed digital computation. In response to a message that it receives, an Actor can make local, 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. p. 31 Actor model of concurrent distributed computation

32. p. 32 Summary of interactions between particles described by the Standard Model. http://en.wikipedia.org/wiki/Standard_Model Unlike other models of computation that are based on mathematical logic, set theory, algebra, etc. the Actor model is based on physics, especially quantum physics and relativistic physics. (Hewitt, 2006) Actor model of concurrent distributed computation

33. Computing nature and nature inspired computation If it looks like a duck, if it walks like a duck and it quacks like a duck, is it a duck? (If it looks like computation is it computation?) 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

34. 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. “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. Computing cells: self-generating systems

35. Computation is implemented at different levels of resolution – Computing architecture p. 35 Some layered computational architectures

36. 36 Multisensori information integration Information integration is critical for the brain to interact effectively with our multisensory environment. The human brain integrates information from multiple senses with prior knowledge to form a coherent and more reliable percept of its environment. (learning) Within the cortical hierarchy, multisensory perception emerges in an interactive process with top-down prior information constraining the interpretation of the incoming sensory signals. Marcin Schröder in the book Computing Nature adresses the Dualism of Selective and Structural Information, describing information integration. http://www.birmingham.ac.uk/researc h/activity/behavioural-neuro/comp- cog-neuro/index.aspx

37. 37 Cognition: Agency-based hierarchies of levels. World as information for an agent From: http://www.alexeikurakin.orghttp://www.alexeikurakin.org Potential informationActual information for an agentCognition http://www.tbiomed.com/content/8/1/4http://www.tbiomed.com/content/8/1/4 scale-invariance of self-organizational dynamics of energy/matter at all levels of organizational hierarchy

38. 38 http://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.html 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 are inhibitory while others are excitatory. Potential information Outside reality for C-elegans Interaction interface for C-elegans Cognition Actual Information C-elegans Agency-based hierarchies of levels. World as information for an agent

39. 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 * 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) Reality for an agent – an observer-dependent reality 39 * 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.”

40. 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. p. 40

41. 41 Life as cognition. Autopoiesis as self- reflective process ”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.” Humberto Maturana, Biology of Cognition, 1970 Maturana and Varela (1980) define "autopoiesis" as follows: An autopoietic system is a system organized (defined as a unity) as a network of processes of production (transformation and destruction) of components that produces the components, such that: (i)through their interactions and transformations continuously they regenerate and realize the network of processes (relations) that produced them; and (ii)they constitute it (the system) as a concrete unity in the space in which they (the components) exist by specifying the topological domain of its realization as such a network.

42. 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 and even to a very simple living agents such as bacteria. The fact is that they act on some kind of and according to some 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. Living agents – basic levels of cognition 42

43. 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. Living agents – basic levels of cognition 43

44. 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. Living agents – basic levels of cognition 44

45. The origin of 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 and we have good reasons to ascribe simpler kinds of 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. Levels of organization of life/cognition 45 Ben-Jacob, E. (2009). Learning from Bacteria about Natural Information Processing. Annals of the New York Academy of Sciences, 1178, 78–90.

46. ● Nerve net – jellyfish ● Simple brain & nerve cord – flatworm ● Brain & nerve cord with ganglia – earthworm ● Increasing forebrain – fish, bird & human ● Olfactory – fish ● Complex behavior – birds ● Reasoning & cognition – humans Evolution of the Nervous System Dee Unglaub Silverthorn, Human Physiology- an Integrated Approach, 3 rd ed

47. Evolution of the Nervous System Figure 9-1: Evolution of the nervous system

48. p. 48 http://www.frontiersin.org/neuroscience/10.3389/fnins.2010.00200/full http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/ Cognition as computation – information processing Modular and hierarchically modular organization of brain networks D. Meunie, R. Lambiotte and E. T. Bullmore Frontiers of Neuroscience http://www.neuroinformatics2013.org Neuroinformatics

49. Cognitive computing - Computation as cognition A cognitive computer is a proposed computational device with a non-Von Neumann architecture that implements Hebbian learning. Instead of being programmable in a traditional sense, such a device learns by experience through an input device that are aggregated within a computational convolution or neural network architecture consisting of weights within a parallel memory system. Example of such devices developed in 2012 under the Darpa SyNAPSE program at IBM directed by Dharmendra Modha. http://en.wikipedia.org/wiki/Cognitive_computerModha p. 49

50. http://www.kurzweilai.net/ibm-unveils-cognitive-computing-chips-combining-digital- neurons-and-synapses An Example: Cognitive Computing at ICIC Cognitive Informatics (CI) is a discipline across computer science, information science, cognitive science, brain science, intelligence science, knowledge science and cognitive linguistics, which investigates into the internal information processing mechanisms and processes of the brain, the underlying abstract intelligence theories and denotational mathematics, and their engineering applications in cognitive computing and computational intelligence. Cognitive Computing (CC ) is a novel paradigm of intelligent computing theories and methodologies based on CI that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. http://www.ucalgary.ca/icic/ The International Institute of Cognitive Informatics and Cognitive Computing (ICIC)

51. 51 An Example: Cognitive Computing at IBM

52. Design and Construction of a Brain-Like Computer 52 A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System Subrata Ghosh, Krishna Aswani, Surabhi Singh, Satyajit Sahu, Daisuke Fujita and Anirban Bandyopadhyay * Information 2014, 5, 28-100; doi:10.3390/info5010028 http://www.mdpi.com/2078-2489/5/1/28

53. Connecting informational structures and processes from quantum physics to living organisms and societies Nature is described as a complex informational structure for a cognizing agent. Information is the difference in one information structure that makes a difference in another information structure. Computation is information dynamics (information processing) constrained and governed by the laws of physics on the fundamental level. p. 53

54. 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

55. 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. Conclusion: Modeling Life as Cognitive Info-computation by Connecting Anthropogenic with Biogenic and Abiotic Cognition p. 55

56. RELATED BOOKS p. 56

57. p. 57 A computable universe

58. Special Issue of the Journal Entropy "Selected Papers from Symposium on Natural/Unconventional Computing and Its Philosophical Significance" 58 Giulio Chiribella, Giacomo Mauro D’Ariano and Paolo Perinotti: Quantum Theory, Namely the Pure and Reversible Theory of Information Susan Stepney: Programming Unconventional Computers: Dynamics, Development, Self- Reference Gordana Dodig Crnkovic and Mark Burgin: Complementarity of Axiomatics and Construction

59. Special Issue of the Journal Entropy "Selected Papers from Symposium on Natural/Unconventional Computing and Its Philosophical Significance" 59 Hector Zenil, Carlos Gershenson, James A. R. Marshall and David A. Rosenblueth: Life as Thermodynamic Evidence of Algorithmic Structure in Natural Environments Andrée C. Ehresmann: MENS, an Info-Computational Model for (Neuro- )cognitive Systems Capable of Creativity Gordana Dodig Crnkovic and Raffaela Giovagnoli, Editorial: Natural/Unconventional Computing and Its Philosophical Significance

60. Special Issue of the Journal Information “Information and Energy/Matter" 60 Vlatko Vedral: Information and Physics Philip Goyal: Information Physics—Towards a New Conception of Physical Reality Chris Fields: If Physics Is an Information Science, What Is an Observer? Gerhard Luhn: The Causal-Compositional Concept of Information Part I. Elementary Theory: From Decompositional Physics to Compositional Information Koichiro Matsuno and Stanley N. Salthe: Chemical Affinity as Material Agency for Naturalizing Contextual Meaning Joseph E. Brenner: On Representation in Information Theory

61. Special Issue of the Journal Information “Information and Energy/Matter" 61 Makoto Yoshitake and Yasufumi Saruwatari: Extensional Information Articulation from the Universe Christopher D. Fiorillo: Beyond Bayes: On the Need for a Unified and Jaynesian Definition of Probability and Information within Neuroscience William A. Phillips: Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory Hector Zenil: Information Theory and Computational Thermodynamics: Lessons for Biology from Physics Joseph E. Brenner: On Representation in Information Theory Gordana Dodig Crnkovic, Editorial: Information and Energy/Matter

62. Computation, Information, Cognition Editor(s): Gordana Dodig Crnkovic and Susan Stuart, Cambridge Scholars Publishing, 2007 Computing Nature p. 62 Information and Computation Editor(s): Gordana Dodig Crnkovic and Mark Burgin, World Scientific, 2011 Computing Nature Editor(s): Gordana Dodig Crnkovic and Raffaela Giovagnoli, Springer, 2013

63. Brier Søren: Cybersemiotics and the question of knowledge Burgin Mark: Information Dynamics in a Categorical Setting Chaitin Greg: Leibniz, Complexity & Incompleteness Collier John: Information, Causation and Computation Cooper Barry: From Descartes to Turing: The computational Content of Supervenience Dodig Crnkovic Gordana and Müller Vincent: A Dialogue Concerning Two Possible World Systems Hofkirchner Wolfgang: Does Computing Embrace Self-Organisation? Kreinovich Vladik & Araiza Roberto: Analysis of Information and Computation in Physics Explains Cognitive Paradigms: from Full Cognition to Laplace Determinism to Statistical Determinism to Modern Approach p. 63 Information and computation Gordana Dodig-Crnkovic and Mark Burgin, World Scientific Publishing Co. 2011

64. MacLennan Bruce J.: Bodies — Both Informed and Transformed Menant Christophe: Computation on Information, Meaning and Representations. An Evolutionary Approach Mestdagh C.N.J. de Vey & Hoepman J.H.: Inconsistent information as a natural phenomenon Minsky Marvin: Interior Grounding, Reflection, and Self-Consciousness Riofrio Walter: Insights into the biological computing Roglic Darko: Super-recursive features of natural evolvability processes and the models for computational evolution Shagrir Oron: A Sketch of a Modeling View of Computing Sloman Aaron: What's information, for an organism or intelligent machine? How can a machine or organism mean? Zenil Hector & Delahaye Jean-Paul: On the algorithmic nature of the world p. 64 Information and computation Gordana Dodig-Crnkovic and Mark Burgin, World Scientific Publishing Co. Series in Information Studies, 2011

65. Computing nature Gordana Dodig-Crnkovic and Raffaela Giovagnoli, Springer SAPERE book series, 2013 Barry Cooper: What Makes A Computation Unconventional? Hector Zenil: Nature-like Computation and a Measure of Programmability Gianfranco Basti: Intelligence And Reference. Formal Ontology Of The Natural Computation Ron Cottam, Willy Ranson and Roger Vounckx: A Framework for Computing Like Nature Gordana Dodig Crnkovic: Alan Turing’s Legacy: Info-Computational Philosophy of Nature Marcin J. Schroeder: Dualism of Selective and Structural Information in Modelling Dynamics of Information p. 65

66. Larry Bull, Julian Holley, Ben De Lacy Costello and Andrew Adamatzky: Toward Turing’s A-type Unorganised Machines in an Unconventional Substrate: A Dynamic Representation In Compartmentalised Excitable Chemical Media Francisco Hernández-Quiroz and Pablo Padilla: Some Constraints On The Physical Realizability Of A Mathematical Construction Mark Burgin and Gordana Dodig Crnkovic: From the Closed Classical Algorithmic Universe to an Open World of Algorithmic Constellations p. 66 Computing nature Gordana Dodig-Crnkovic and Raffaela Giovagnoli, Springer SAPERE book series, 2013

67. p. 67 Two brand new books 2013 On the topic of life, computation, evolution & cognition. Written by a computer scientist.

68. p. 68 Two brand new books 2014 On the topic of on the topic of (physical) computation & cognition. Written by a philosopher.

69. New computational paradigm: Generative computing – cellular automata p. 69 A New Kind of Science Book available at: http://www.wolframscience.com Based on cellular automata, complexity emerging from repeating very simple rules See also http://www.youtube.com/watch?v=_eC14GonZnU A New Kind of Science - Stephen Wolfram Books in the New Computational Paradigm

70. A New Paradigm of Computing – Interactive Computing Interactive Computation: the New Paradigm Springer-Verlag in September 2006 Dina Goldin, Scott Smolka, Peter Wegner, eds. Dina Goldin, Peter Wegner The Interactive Nature of Computing: Refuting the Strong Church - Turing Thesis Minds and Machines Volume 18, Issue 1 (March 2008) p 17 - 38 http://www.cs.brown.edu/people/pw/strong-cct.pdf Biology as Reactivity http://research.microsoft.com/pubs/144550/CACM_11.pdf p. 70

71. p. 71 Self-modifying Systems in Biology and Cognitive Science The topic of the book is the self-generation of information by the self-modification of systems. The author explains why biological and cognitive processes exhibit identity changes in the mathematical and logical sense. This concept is the basis of a new organizational principle which utilizes shifts of the internal semantic relations in systems. ftp://wwwc3.lanl.gov/pub/users/joslyn/kamp_rev.pdf

72. Programming the Universe: A Quantum Computer Scientist Takes on the Cosmos by Seth Lloyd p. 72 The Universe as quantum information

73. p. 73 The Universe as quantum information Decoding Reality By Valtko Vedral Reality = Information Under Google books there are parts of this book available.

74. Self-Organization and Selection in Evolution p. 74 Stuart Kauffman presents a brilliant new paradigm for evolutionary biology, one that extends the basic concepts of Darwinian evolution to accommodate recent findings and perspectives from the fields of biology, physics, chemistry and mathematics. The book drives to the heart of the exciting debate on the origins of life and maintenance of order in complex biological systems. It focuses on the concept of self-organization: the spontaneous emergence of order widely observed throughout nature. Kauffman here argues that self-organization plays an important role in the emergence of life itself and may play as fundamental a role in shaping life's subsequent evolution as does the Darwinian process of natural selection. http://books.google.se/books/about/The_Origins_of_Order.html?id=lZcSpRJz0dgC&redir_esc=y

75. The relationship between mind and matter p. 75 Incomplete Nature. How mind emerged from matter by Terrence Deacon

76. “We can only see a short distance ahead, but we can see plenty there that needs to be done.” (Turing 1950) Turing, A. M. (1950). Computing machinery and intelligence, Mind LIX, 433-60. http://cogprints.org/499/0/turing.html http://cogprints.org/499/0/turing.html Let me finish by Turing quote p. 76

77. Based on the following articles ● Dodig-Crnkovic G. and Giovagnoli R. (Eds), Computing Nature – A Network of Networks of Concurrent Information Processes, In: COMPUTING NATURE, (book) Springer, Heidelberg, SAPERE book series, pp. 1-22, May 2013. http://arxiv.org/abs/1210.7784http://arxiv.org/abs/1210.7784 ● Dodig-Crnkovic G., Dynamics of Information as Natural Computation, Information 2011, 2(3), 460-477; doi:10.3390/info2030460 Special issue: Selected Papers from FIS 2010 Beijing Conference, 2011. http://www.mdpi.com/journal/information/special_issues/selectedpap_beijing http://www.mdpi.com/2078-2489/2/3/460/ See also: http://livingbooksaboutlife.org/books/Energy_ConnectionsDynamics of Information as Natural Computation http://www.mdpi.com/journal/information/special_issues/selectedpap_beijing http://www.mdpi.com/2078-2489/2/3/460/ http://livingbooksaboutlife.org/books/Energy_Connections ● Dodig Crnkovic, G. Information and Energy/Matter. Information 2012, 3(4), 751-755. Special Issue "Information and Energy/Matter" doi:10.3390/info3040751 All articles can be found under: p. 77 http://www.idt.mdh.se/~gdc/work/publications.html

78. THANKS TO Prof. Francisco Hernández Quiroz for invitation to UNAM Alberto Hernández Espinosa for organizing my visit to Infotec Also Hector Zenil Carlos Gershenson & Tom Froese for their work that convinced me that UNAM is an extraordinary place! p. 78

79. A Mathematical Model for Info- computationalism- A. C. Ehresmann p. 79 Open peer commentary on the article “Info-computational Constructivism and Cognition” by Gordana Dodig-Crnkovic. Ehresmann proposes a mathematical approach to the framework developed by Dodig-Crnkovic. Based on the Property of natural computation, called the multiplicity principle development of increasingly complex cognitive processes and knowledge is described. “Local dynamics are classically computable, a consequence of the MP is that the global dynamics is not, thus raising the problem of developing more elaborate computation models.”

80. An Info-Computational Model for (Neuro- )cognitive Systems Capable of Creativity - Andrée C. Ehresmann Andrée C. Ehresmann http://www.mdpi.com/1099-4300/14/9/1703http://www.mdpi.com/1099-4300/14/9/1703 p. 80 The model, based on a ‘dynamic’ Category Theory, accounting for the functioning of the neural, cognitive and mental systems at different levels of description and across different timescales.