1. 1 Gordana Dodig Crnkovic School of Innovation, Design and Engineering, Mälardalen University, Sweden http://www.idt.mdh.se/personal/gdc/ Computing and Philosophy Global Course 2008 http://www.idt.mdh.se/kurser/comphil http://www.idt.mdh.se/kurser/comphil COMPUTING, PANCOMPUTATIONALISM AND INFO-COMPUTATIONALISM Hand with Reflecting Sphere (Self-Portrait in Spherical Mirror), M.C. Escher

2. 2 Eye, Maurits Cornelis Escher PART 1 THE UNIVERSE FOR A HUMAN

3. In much the same way as sciences develop through successive paradigm shifts, on a more general level there is also a succession of paradigm shifts in our understanding of the universe.. Our present-day knowledge of the universe spans over many orders of magnitude in space with variety of organizational (functional) levels. http://www.youtube.com/watch?v=XcbrKPj1V_Uhttp://www.youtube.com/watch?v=XcbrKPj1V_U Tour of Universe http://www.youtube.com/watch?v=9BjHvwSvpOw&feature=relatedhttp://www.youtube.com/watch?v=9BjHvwSvpOw&feature=related 100 Million Light Years zoom http://www.youtube.com/watch?v=U6QYDdgP9eghttp://www.youtube.com/watch?v=U6QYDdgP9eg The Origin of Life - Abiogenesis 3

4. The Mytho-Poetic Universe Egyptian, Hindu and Ancient Greek 4 In old Hindu myth, the tortoise supports elephants that hold up the world, and everything is encircled by the world serpent. http://www.mythencyclopedia.com/Go- Hi/Hinduism-and-Mythology.html In Egyptian mythology, the sun god Ra (or Re) was the supreme power in the universe. The giver of life, he was often merged with the god Amun as Amun-Ra. Some myths present Ra as the ruler of all the gods. Others say that he was the only god and that all other Egyptian deities were merely aspects of Ra. http://www.mythencyclopedia.com/Pr-Sa/Ra- Re.html Zeus is the greatest of all the Greek gods. He is the ultimate authority among all the immortals of Greek myth and legend. He basically governs the entire universe even though he was by no means infallible or omnipotent. http://ancienthistory.about.com/od/z eusmyth/ig/Zeus/Zeus-Courting- Ganymede.htm

5. The Mechanical Universe The Medieval Geocentric Universe 5 The Nuremberg Chronicle (1493) Aristotle Libri de caelo (1519)

6. 6 The mechanistic paradigm systematically revealed physical structure of the Universe in analogy with the artificial. The self-functioning automaton is a basis and canon of the form of the Universe. Newton Principia, 1687 The Mechanical Universe The Clockwork Universe

7. The Computational Universe We are all living inside a gigantic computer. No, not The Matrix: the Universe. Every process, every change that takes place in the Universe, may be considered as a kind of computation. The universe is on a fundamental level an info-computational phenomenon. GDC 2005 7 http:// http:// www.nature.com/nsu/020527/020527-16.htm l

8. Konrad Zuse was the first to suggest in 1967 that the physical behavior of the entire universe is being computed on a basic level, possibly on cellular automata, by the universe itself which he referred to as "Rechnender Raum" or Computing Space/Cosmos. Computationalists: Zuse, Wiener, Fredkin, Wolfram, Chaitin, Lloyd, Seife, 't Hooft, Deutsch, Tegmark, Schmidhuber, Weizsäcker, Wheeler.. 8 http://http://www.nature.com/nature/journal/v435/ n7042/full/435572a.html http://en.wikipedia.org/wiki/Pancomputationalism

9. The Major Paradigm Shifts in our View of the Universe Info-Computational Universe: Self-reflective observer is an integral part of the explanatory model. Mechanic Universe: Nature objectivized through agreement of neutral observers agents who are outside the model. Mytho-poetic Universe: God-centric. Gods decide rules for nature and humans. 9

10. Universe of Natural Objects and Processes Arcimboldo Giuseppe, Summer Both mechanistic and computational universe describe the natural world as physical. Historically, scientific study of nature was part of Natural Philosophy. 10

11. 11 Konrad von Megenberg. Buch der Natur (1481) Natural philosophy or the philosophy of nature (Latin Philosophia Naturalis), is a study of nature and the physical universe dominant before the development of modern science in the 19th century. Newton was natural philosopher. At older universities, long-established Chairs of Natural Philosophy are nowadays occupied mainly by physics professors. At present, interesting complexity phenomena are studied on the intersection of several research fields such as computing, biology, neuroscience, cognitive science, philosophy, physics, and similar information/computation intensive fields which seem again form a core of a new, now life-centric natural philosophy. Natural Philosophy – Historical Unity of Knowledge http://en.wikipedia.org/wiki/Natural_philosophy

12. Info-Computationalism as a Framework for Knowledge Generation Information and computation are two interrelated and mutually defining phenomena and basic blocks of Info-Computationalism. There is no computation without information (computation understood as information processing), and vice versa, there is no information without computation (all information is a result of computational processes). Being interconnected, information is studied as a structure, while computation presents a process on an informational structure. In order to learn about foundations of information, we must also study computation as its dynamics. 12

13. Info-computationalism has a potential of connecting not only sciences and philosophy but also humanities and the arts within the same universe.* The common ground is formed by the possibility of communication and the expectation on agents to be well informed both about their own fields of expertise but also about a wide variety of connected knowledge fields. Intelligence is no longer taken to be exclusively rational intelligence expressed in form of propositional knowledge but also as embodied, emotional intelligence which is a crucial part of the cognitive repertoire of living organisms. So the whole of embodied organism has place in the Info-Computational universe in which life is understood as an eminently info-computational phenomenon. 13 * A study of the relationships among mathematics, music and art seen through I/mind´s eye can be found in Gödel, Escher, Bach: An Eternal Golden Braid by Douglas R. Hofstadter (1979)

14. 14 Gordana Dodig Crnkovic School of Innovation, Design and Engineering, Mälardalen University, Sweden http://www.idt.mdh.se/personal/gdc/ Computing and Philosophy Global Course 2008 http://www.idt.mdh.se/kurser/comphil http://www.idt.mdh.se/kurser/comphil COMPUTING, PANCOMPUTATIONALISM AND INFO-COMPUTATIONALISM Hand with Reflecting Sphere (Self-Portrait in Spherical Mirror), M.C. Escher

15. 15 Eye, Maurits Cornelis Escher PART 2 THE INFO-COMPUTATIONAL UNIVERSE

16. Info-Computationalism Information and computation are two interrelated and mutually defining phenomena – there is no computation without information (computation understood as information processing), and vice versa, there is no information without computation (all information is a result of computational processes). Being interconnected, information is studied as a structure, while computation presents a process on an informational structure. In order to learn about foundations of information, we must also study computation. GDC 2006 16

17. Computing Nature. Dual-Aspect Info-Computational Metaphysics AGENCY/ COMPUTATION ONTOLOGY/ INFORMATION 17 http://www.diva-portal.org/mdh/theses/abstract.xsql?dbid=153http://www.diva-portal.org/mdh/theses/abstract.xsql?dbid=153 GDC, 2006

18. Structure and Process “It is hard to think of information in isolation from the processes which create, modify, and convey it. This combination of structure and process is natural in many disciplines. In computer science, one designs data structures in tandem with the processes that manipulate them, and the tasks which the latter should perform. But the same point is familiar from philosophy ([David Lewis]), when saying that 'Meaning Is what Meaning Does'. We can only give good representations of meanings for linguistic expressions when we state how they are going to be used: in communication, disambiguation, inference, and so on. In a slogan: Structure should always come in tandem with a process!” van Benthem & Martinez, The Stories of Logic and Information, forthcoming in Handbook on the Philosophy of Information, 2008 18

19. A special issue of the Journal of Logic, Language and Information (Volume 12 No 4 2003) dedicated to the different facets of information. A Handbook on the Philosophy of Information (van Benthem, Adriaans) is in preparation as one volume Handbook of the philosophy of science. The Internet http://www.sdsc.edu/News%20Items/PR022008_moma.html http:// Information 19

20. “It is tempting to suppose that some concept of information could serve eventually to unify mind, matter, and meaning in a single theory.” Dennett and Haugeland. Intentionality. In Richard L. Gregory, Editor. The Oxford Companion to the Mind. Oxford University Press, 1987. Paninformationalism 20

21. Information Concept Family According to L Floridi, four kinds of mutually compatible phenomena are commonly referred to as "information":  Information about something (e.g. a train timetable)  Information as something (e.g. DNA, or fingerprints)  Information for something (e.g. algorithms or instructions)  Information in something (e.g. a pattern or a constraint). More often than not, information has all above four of dimensions. For example, a train timetable is about trains, but it is also something (a message on a monitor or on a piece of paper), it might be understood as instruction for me to do something (go to the train station at certain time) and it is obviously a pattern or a constraint in several ways (pattern on a monitor or a paper, constraint on which trains are possible to take.) Floridi, Luciano, "Semantic Conceptions of Information", The Stanford Encyclopedia of Philosophy (Winter 2008 Edition), Edward N. Zalta (ed.), forthcoming URL =.http://plato.stanford.edu/archives/win2008/entries/information-semantic 21

22. Computation Computation is generally defined as information processing. (See Burgin, M., Super-Recursive Algorithms, Springer Monographs in Computer Science, 2005) For different views see e.g. http://people.pwf.cam.ac.uk/mds26/cogsci/program.htmlhttp://people.pwf.cam.ac.uk/mds26/cogsci/program.html Computation and Cognitive Science 7–8 July 2008, King's College Cambridge The definition of computation is widely debated, and an entire issue of the journal Minds and Machines (1994, 4, 4) was devoted to the question “What is Computation?” as well as the special issue of Theoretical Computer Science 317 (2004) 22

23. The Universe as a Computer - Pancomputationalism Every process, every change that takes place in the Universe, may be considered as a kind of computation.. Seth Lloyd, Programming the Universe: A Quantum Computer Scientist Takes On the Cosmos, 2006 Charles Seife, Decoding the Universe: How the New Science of Information Is Explaining Everything in the Cosmos, from Our Brains to Black Holes, 2006 Marcin Milkowsky, Is Computationalism Trivial? In Computation, Information, Cognition – The Nexus and The Liminal, G. Dodig-Crnkovic and S. Stuart (Editors), CSP, Cambridge 2007 23

24. Computing Nature and Nature Inspired Computation In 1623, Galileo in his book The Assayer - Il Saggiatore, claimed that the language of nature's book is mathematics and that the way to understand nature is through mathematics. Generalizing ”mathematics” to ”computing” we may agree with Galileo – the great book of nature is an e-book! Journals: Natural Computing and IEEE Transactions on Evolutionary Computation. http://www.youtube.com/watch?v=JA5QoTMvsiE&feature=related 24

25. Natural Computation Computing Inspired by nature: Evolutionary computation Neural networks Artificial immune systems Swarm intelligence Simulation and emulation of nature: Fractal geometry Artificial life and bio-inspired robotics Synthetic biology Computing with natural materials: DNA computing Quantum computing 25 Self-Organizing Systems, BioMolecular Computing Chemistry Computing Membrane Computing Metabolic Systems Multi-agent systems Complex adaptive systems The use of multi-agent models. to model complex organisations and social systems http://www.iwinac.uned.es

26. Tape Present Model of Computation: Turing Machine...... Read-Write head Control Unit 1. Reads a symbol 2. Writes a symbol 3. Moves Left or Right http://plato.stanford.edu/entries/turing-machine/http://plato.stanford.edu/entries/turing-machine/ A new paradigm, Interactive Computation: http://www.cse.uconn.edu/~dqg/inter_book.htmlhttp://www.cse.uconn.edu/~dqg/inter_book.html Interactive Computation: the New Paradigm Wegner, et al.Interactive Computation: the New Paradigm http://en.wikipedia.org/wiki/Super-recursive_algorithmhttp://en.wikipedia.org/wiki/Super-recursive_algorithm On TM’s limitations and computation as information processing: Super-Recursive Algorithms, Mark Burgin 26

27. Beyond Turing Machines: Self-Generating Living Systems 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, p. 223 Self-Modifying Systems in Biology and Cognitive Science) 27

28. Beyond Turing Machines Ever since Turing proposed his machine model which identifies computation with the execution of an algorithm, there have been questions about how widely the Turing Machine (TM) model is applicable. With the advent of computer networks, which are the main paradigm of computing today, the model of a computer in isolation, represented by a Universal Turing Machine, has become insufficient. The basic difference between an isolated computing box and a network of computational processes (nature itself understood as a computational mechanism) is the interactivity of computation. The most general computational paradigm today is interactive computing (Wegner, Goldin). 28

29. The challenge to deal with computability in the real world (such as computing on continuous data, biological computing/organic computing, quantum computing, or generally natural computing) has brought new understanding of computation. Natural computing has different criteria for success of a computation, halting problem is not a central issue, but instead the adequacy of the computational response in a network of interacting computational processes/devices. In many areas, we have to computationally model emergence not being clearly algorithmic. (Barry Cooper) Important to note is the fact that many among pancomputationalists were interested in discrete Turing-type algorithmic model of the universe. As in the case of arguments about computational models of mind, the limitation to Turing machines is not an imperative when universe is seen as entirety of natural computing processes. 29

30. Correspondence Principle NATURAL COMPUTATION TM 30 Picture after Stuart A. Umpleby http://www.gwu.edu/~umpleby/recent_papers/2004_what_i_learned_from_heinz_von_foerster_figures_by_umpleby.ht m http://www.gwu.edu/~umpleby/recent_papers/2004_what_i_learned_from_heinz_von_foerster_figures_by_umpleby.ht m

31. Computability Theory Barry Cooper 31 http://www.amsta.leeds.ac.uk/~pmt6sbc/

32. What is computation? How does nature compute? Learning from Nature * “It always bothers me that, according to the laws as we understand them today, it takes a computing machine an infinite number of logical operations to figure out what goes on in no matter how tiny a region of space, and no matter how tiny a region of time … So I have often made the hypothesis that ultimately physics will not require a mathematical statement, that in the end the machinery will be revealed, and the laws will turn out to be simple, like the chequer board with all its apparent complexities.” Richard Feynman “The Character of Physical Law” * 2008 Midwest NKS Conference, Fri Oct 31 - Sun Nov 2, 2008 Indiana University Bloomington http://www.cs.indiana.edu/~dgerman/2008midwestNKSconference/index.html http://www.cs.indiana.edu/~dgerman/2008midwestNKSconference/index.html 32

33. The Discrete/Continuum Dichotomy “In a quantum computer, however, there is no distinction between analog and digital computation. Quanta are by definition discrete, and their states can be mapped directly onto the states of qubits without approximation. But qubits are also continuous, because of their wave nature; their states can be continuous superpositions. Analog quantum computers and digital quantum computers are both made up of qubits, and analog quantum computations and digital quantum computations both proceed by arranging logic operations between those qubits. 33

34. Our classical intuition tells us that analog computation is intrinsically continuous and digital computation is intrinsically discrete. As with many other classical intuitions, this one is incorrect when applied to quantum computation. Analog quantum computers and digital quantum computers are one and the same device.” (Lloyd, 2006) The discussion about the discrete/continuous nature of computing is relevant in the context of discussions about mind – if computing is always discrete and mind is continuous, then mind can not be computational. 34

35. A New Paradigm of Computing, Interactive Computing (2006) See also: 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 35 Computing agents that interact with an environment are more expressive than Turing machines according to a notion of expressiveness that measures problem-solving ability and is specified by observation equivalence. Distributed models of coordination, collaboration, and true concurrency are shown to be more expressive than sequential models.

36. A New Kind of Science (2002) http://www.wolframscience.com http://www.wolframscience.com (free online access to the book) 36 Contents Chapter 1 The Foundations for a New Kind of Science 1 Chapter 2 The Crucial Experiment 23 Chapter 3 The World of Simple Programs 51 Chapter 4 Systems Based on Numbers 115 Chapter 5 Two Dimensions and Beyond 169 Chapter 6 Starting from Randomness 223 Chapter 7 Mechanisms in Programs and Nature 297 Chapter 8 Implications for Everyday Systems 363 Chapter 9 Fundamental Physics 433 Chapter 10 Processes of Perception and Analysis 547 Chapter 11 The Notion of Computation 637 Chapter 12 The Principle of Computational Equivalence 715

37. A new kind of Phenomena Nonlinear Dynamics and Complexity As it becomes ever more apparent that Newtonian mechanics is inadequate for modeling nonlinear systems, or systems that have too many degrees of freedom to handle easily, researchers in all fields are turning toward nonlinear dynamics as a refreshing alternative. This is a paradigm shift à la Kuhn, and Klaus Mainzer guides us through it with an astounding range of historical and scientific knowledge. From quantum physics to consciousness to economics, Mainzer shows us how thinking complexly can solve problems over which standard, linear thinking continually stumbles. Amazon.com Review 37

38. Randomness, and Complexity, From Leibniz to Chaitin, 2007 38 Contents: On Random and Hard-to-Describe Numbers (C H Bennett) The Implications of a Cosmological Information Bound for Complexity, Quantum Information and the Nature of Physical Law (P C W Davies) What is a Computation? (M Davis) A Berry-Type Paradox (G Lolli) The Secret Number. An Exposition of Chaitin’s Theory (G Rozenberg & A Salomaa) Omega and the Time Evolution of the n-Body Problem (K Svozil) God's Number: Where Can We Find the Secret of the Universe? In a Single Number! (M Chown) Omega Numbers (J-P Delahaye) Some Modern Perspectives on the Quest for Ultimate Knowledge (S Wolfram) An Enquiry Concerning Human (and Computer!) [Mathematical] Understanding (D Zeilberger) and other papers

39. A New Kind of Philosophy Metaphysics Naturalized (2007) Every Thing Must Go argues that the only kind of metaphysics that can contribute to objective knowledge is one based specifically on contemporary science as it really is, and not on philosophers' a priori intuitions, common sense, or simplifications of science. In addition to showing how recent metaphysics has drifted away from connection with serious scholarly inquiry, they demonstrate how to build a metaphysics compatible with current fundamental physics ("ontic structural realism"). 39

40. 40 Gordana Dodig Crnkovic School of Innovation, Design and Engineering, Mälardalen University, Sweden http://www.idt.mdh.se/personal/gdc/ Computing and Philosophy Global Course 2008 http://www.idt.mdh.se/kurser/comphil http://www.idt.mdh.se/kurser/comphil COMPUTING, PANCOMPUTATIONALISM AND INFO-COMPUTATIONALISM Hand with Reflecting Sphere (Self-Portrait in Spherical Mirror), M.C. Escher

41. 41 Eye, Maurits Cornelis Escher PART 3 THE INFO-COMPUTATIONAL HUMAN

42. Info-Computationalism Applied: Epistemology Naturalized Naturalized epistemology (Feldman, Kornblith, Stich) is, in general, an idea that knowledge may be studied as a natural phenomenon -- that the subject matter of epistemology is not our concept of knowledge, but the knowledge itself. “The stimulation of his sensory receptors is all the evidence anybody has had to go on, ultimately, in arriving at his picture of the world. Why not just see how this construction really proceeds? Why not settle for psychology? “ "Epistemology Naturalized", Quine 1969 I will re-phrase the question to be: Why not settle for computing? Epistemology is the branch of philosophy that studies the nature, methods, limitations, and validity of knowledge and belief. 42

43. Cognition in Computing Nature According to Maturana and Varela (1980) even the simplest organisms possess cognition and their meaning-production apparatus is contained in their metabolism. (Of course, there are also non-metabolic interactions with the environment, such as locomotion, that also generates meaning for an organism by changing its environment and providing new input data.) Maturana’s and Varelas’ understanding that all living organisms possess some cognition, in some degree. is most suitable as the basis for a computationalist account of the naturalized evolutionary epistemology. 43

44. Natural computing as a new paradigm of computing goes beyond the Turing Machine model and applies to all physical processes including those going on in our brains. The next great change in computer science and information technology will come from mimicking the techniques by which biological organisms process information. To do this computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Info-Computational Account of Knowledge Generation 44

45. At the physical level, living beings are open complex computational systems in a regime on the edge of chaos, characterized by maximal informational content. Complexity is found between orderly systems with high information compressibility and low information content and random systems with low compressibility and high information content. (Flake) The essential feature of cognizing living organisms is their ability to manage complexity, and to handle complicated environmental conditions with a variety of responses which are results of adaptation, variation, selection, learning, and/or reasoning. (Gell-Mann) All mentioned capacities of living organisms are eminently info-computational. Info-Computational Account of Knowledge Generation 45

46. Cognition as Restructuring of an Agent in Interaction with the Environment As a result of evolution, increasingly complex living organisms arise that are able to survive and adapt to their environment. It means they are able to register inputs (data) from the environment, to structure those into information, and in more developed organisms into knowledge. The evolutionary advantage of using structured, component-based approaches is improving response-time and efficiency of cognitive processes of an organism. The Dual network model, suggested by Goertzel for modeling cognition in a living organism describes mind in terms of two superposed networks: a self- organizing associative memory network, and a perceptual-motor process hierarchy, with the multi-level logic of a flexible command structure. 46

47. Naturalized knowledge generation acknowledges the body as our basic cognitive instrument. All cognition is embodied cognition, in both microorganisms and humans (Gärdenfors, Stuart). In more complex cognitive agents, knowledge is built upon not only reasoning about input information, but also on intentional choices, dependent on value systems stored and organized in agents memory. It is not surprising that present day interest in knowledge generation places information and computation (communication) in focus, as information and its processing are essential structural and dynamic elements which characterize structuring of input data (data  information  knowledge) by an interactive computational process going on in the agent during the adaptive interplay with the environment. 47

48.  Agent: biological organism/cognizing machine  Information and computation is in the agent  Interaction with the physical world and other agents  Physicalism with information as a stuff of the universe  Agents are parts of different cognitive communities  Self-organization and adaptation  Self-reflection/circularity / recursiveness is central Natural Computing in Living Agents 48

49. Self-Reflection http://brain.oxfordjournals.org/cgi/content/full/125/8/1808 49 Multi-step explanatory reference Self-reference: levels of the same order – circular causation A=B(C(D(A))) A= B ( C ( D ( E ))) Reference to lower levels with diminishing significance – linear causation, cut off at low enough level Neural correlates of self-reflection

50. Consciousness Computationalist Way (2007) "consciousness is not an [added] option" for beings evolved to engage in symbolic thought, recognize patterns, create categories, reason via analogies and wonder about the self. Consciousness is "the upper end of a continuous spectrum of self- perception levels that brains automatically possess as a result of their design." “Hofstadter points to a level at which self might exist, up among the symbols and patterns -- or rather, to various levels on which self exists simultaneously. His conclusions mesh well with those of psychotherapy. We are not selves first and social creatures later. It's through empathy that we develop a rich sense of self. Nor is the self neatly demarcated. We contain multitudes.” The Washington Post 50

51. Computation, Information, Cognition Editor(s): Gordana Dodig Crnkovic and Susan Stuart, 2007 Written by world-leading experts, this book draws together a number of important strands in contemporary approaches to the philosophical and scientific questions that emerge when dealing with the issues of computing, information, cognition and the conceptual issues that arise at their intersections. It discovers and develops the connections at the borders and in the interstices of disciplines and debates. 51 http://www.amazon.ca/Computation-Information-Cognition-Gordana-Dodig-Crnkovic/dp/1847180906

52. InfoComputationalism, Fundamental Aims  InfoComputationalism (ICON) is providing a common language and a unified platform for specialist sciences to communicate and create complex holistic multi-disciplinary/inter-disciplinary/ transdisciplinary knowledge networks and so present an integrative tools for existing information and for generation of new information  deepening our understanding of info-computational mechanisms and processes and their relationship to life and cognition  prompting development of new unconventional computational methods 52

53.  helping understanding and improvement of learning processes providing broader, more general context and agendas  contributing to argument for evolution of biological life, cognition and intelligence  encouraging learning from nature about optimizing solutions with of finite resources constraints .. 53

54. Answering Criticism Info-Computationalist views may be interpreted as claims that the whole world is ”nothing but a machine” and that we humans are essentially robots with no free will or real feelings. That is certainly not the case. The view that the universe is an info- computational network means that the universe as it is may be understood and modeled as an info-computational network. In the introduction to this lecture we mentioned paradigm shift from mytho-poetic to mechanistic to computationalist universe. So we can ask the same question about mechanist universe. Was that understanding of the universe true? Was it real? Or merely metaphoric? Even though mechanicism was primarily the outlook at the inanimate matter, and mechanistic approaches to robotics did not work for any other purpose but the entertainment, mechanistic worldview neverteless helped us learn a lot about the universe. 54

55. The parallel development goes on in the course of computationalism now again. We will learn in interaction with the universe much more about its informational and computational resources and capabilities, and we will develop even more powerful ways of learning, probably via intelligent systems. Knowing that biological organisms (including humans) are information- processing “machines” does not make them less fascinating. In the same way as knowing that all of us are made of atoms does not mean that we do not have free will and real feelings. Understanding fundamental level processes does not make music, arts and philosophy obsolete. Info-computationalism help us both by supplying the tools for art production and even tools for understanding of the phenomenon of art on many different levels. That is also why philosophy is coming back to sciences based on info-computational knowledge – holistic, high level of abstraction view is necessary as a sort of self-reflective process of knowledge itself – seeing itself from both inside and the outside. 55

56. An Ongoing Paradigm Shift in Sciences (Object Level) and Philosophy (Meta Level)  Information/Computation - basic building blocks  Discrete/Continuum - two complementary levels of description (also relevant for Connectionism vs. computationalism argument)  Natural interactive computing beyond Turing limit – not only computing as is but also computing as it may be  With new info-computational tools we are equipped to address complex dynamic systems including information and knowledge generating systems (grounds for future communication across cultural gaps of research fields) 56

57.  We are starting to understand the importance of emergency (emergent properties/behaviors which are qualities possessed by the whole but not by its parts)  Logical pluralism (contemporary development in the field of logic, closely related to computing, brings about necessity of addressing logical pluralism)  Philosophy (“Everything must go” approach, philosophy informed by sciences)  Human-centric (agent-centric) models with circularity and self-reflection (computing, cybernetics)  Ethics returns to researchers agenda (Science as a constructivist project – what is it we construct and why?) 57

58. References Gordana Dodig-Crnkovic Semantics of Information as Interactive Computation in Manuel Moeller, Wolfgang Neuser, and Thomas Roth-Berghofer (eds.), Fifth International Workshop on Philosophy and Informatics, Kaiserslautern 2008 (DFKI Technical Reports; Berlin: Springer) http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-332/ Semantics of Information as Interactive Computation http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-332/ Gordana Dodig-Crnkovic Where do New Ideas Come From? How do They Emerge? Epistemology as Computation (Information Processing) Chapter for a book celebrating the work of Gregory Chaitin, Randomness & Complexity, from Leibniz to Chaitin, C. Calude ed., World Scientific, Singapore, 2007 Book Cover Where do New Ideas Come From? How do They Emerge? http://www.idt.mdh.se/personal/gdc/work/NewIdeas.pdf Gordana Dodig-Crnkovic Epistemology Naturalized: The Info-Computationalist Approach APA Newsletter on Philosophy and Computers, Spring 2007 Volume 06, No 2 Epistemology Naturalized: The Info-Computationalist Approach http://www.idt.mdh.se/personal/gdc/work/APA-Newsletter-Spring07.pdf 58

59. Gordana Dodig-Crnkovic Knowledge Generation as Natural Computation, Proceedings of International Conference on Knowledge Generation, Communication and Management (KGCM 2007), Orlando, Florida, USA, 2007 http://www.idt.mdh.se/personal/gdc/work/KGCM-KnowledgeGeneration.pdf Knowledge Generation as Natural Computation http://www.idt.mdh.se/personal/gdc/work/KGCM-KnowledgeGeneration.pdf Gordana Dodig-Crnkovic Investigations into Information Semantics and Ethics of Computing PhD Thesis, Mälardalen University Press, September 2006 Investigations into Information Semantics and Ethics of Computing http://www.diva-portal.org/mdh/theses/abstract.xsql?dbid=153 Dodig-Crnkovic G. and Stuart S., eds. Computation, Information, Cognition – The Nexus and The Liminal Cambridge Scholars Publishing, Cambridge 2007 Computation, Information, Cognition – The Nexus and The Liminal http://www.idt.mdh.se/ECAP-2005/Intro-Preface-ComputationInformationCognition.pdf Gordana Dodig-Crnkovic Shifting the Paradigm of the Philosophy of Science: the Philosophy of Information and a New Renaissance Minds and Machines: Special Issue on the Philosophy of Information,November 2003, Volume 13, Issue 4 http://www.springerlink.com/content/g14t483510156726/ Shifting the Paradigm of the Philosophy of Science: the Philosophy of Information and a New Renaissance http://www.springerlink.com/content/g14t483510156726/ 59