Gordana Dodig Crnkovic School of Innovation, Design and Engineering, Mälardalen University, Sweden

1. COMPUTING, PANCOMPUTATIONALISM AND INFO-COMPUTATIONALISM  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 Hand with Reflecting Sphere (Self-Portrait in Spherical Mirror) , M.C. Escher

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

3. Info-Computationalism “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. Under this conception information is that which constitutes the computing structure, the Universe, at any given moment; the structure changes continuously and that change can be understood as computation. Thus, computation is what happens dynamically to information from one moment to the next.”

4. Computing Nature. Dual-Aspect Info-Computational Metaphysics ONTOLOGY/ INFORMATION AGENCY/ COMPUTATION http://www.diva-portal.org/mdh/theses/abstract.xsql?dbid=153GDC, 2006 Metaphysics is the branch of philosophy investigating principles of reality transcending those of any particular science. It is concerned with explaining the ultimate nature of being and the world.

5. 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

6. Information 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

7. Paninformationalism “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.

8. 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 >.

9. 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.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 ofTheoretical Computer Science 317 (2004)

10. 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 MarcinMilkowsky, Is Computationalism Trivial? In Computation, Information, Cognition – The Nexus and The Liminal, G. Dodig-Crnkovic and S. Stuart (Editors), CSP, Cambridge 2007

11. Computing Nature and Nature Inspired Computation In 1623, Galileo in his book The - 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” wemayagree with Galileo – the great book of nature is an e-book! http://www.youtube.com/watch?v=JA5QoTMvsiE&feature=related Journals: Natural Computing and IEEE Transactions on Evolutionary Computation.

12. Natural Computation • Self-organizing systems • Complex adaptive systems • Bio-molecular computing • Chemistry computing • Membrane computing • Metabolic systems • Complex organizations • Social systems • 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 objects: • DNA computing • Quantum computing http://www.iwinac.uned.es

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

14. A Turing machine is an abstract representation of a computing device. It is more like a computer program (software) than a computer (hardware). Turing's thesis: LCMs [logical computing machines: Turing's expression for Turing machines] can do anything that could be described as "rule of thumb" or "purely mechanical". (Turing 1948:7.) CHURCH-TURING THESIS* *Source: Stanford Encyclopaedia of Philosophy (B. Jack Copeland)

15. MISUNDERSTANDINGS OF THE TURING THESIS Turing did not show that his machines can solve any problem that can be solved "by instructions, explicitly stated rules, or procedures" and nor did he prove that a universal Turing machine "can compute any function that any computer, with any architecture, can compute".

16. Turing has shown that his universal machine can compute any function that any Turing machine can compute; and he put forward, and advanced philosophical arguments in support of, the thesis called Turing’s thesis. “A man provided with paper, pencil, and rubber, and subject to strict discipline, is in effect a universal machine.” (Turing) “Turing’s "Machines". These machines are humans who calculate. (Wittgenstein)

17. A thesis concerning the extent of effective methods - procedures that a human being unaided by machinery is capable of carrying out - has no implication concerning the extent of the procedures that machines are capable of carrying out, even machines acting in accordance with ‘explicitly stated rules’. Among a machine’s repertoire of atomic operations there may be those that no human being unaided by machinery can perform.

18. Beyond Turing Machines: Self-Generating Living Systems Complex biological systems must be modeled as self-referential, self-organizing "component-systems" which are self-generating and whose behavior, though computational in a general sense, goes far beyond Turing machine model. (George Kampis) “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)

19. Beyond Turing Machines Ever since Turing proposed his machine model identifying computation with the execution of analgorithm, there have been questions about how widely the Turing Machine 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).

20. The challenge to deal with computability in the real world (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 passing we can note that many among pancomputationalists were interested in a discrete Turing-type algorithmic models of the universe. As evident from the arguments about computational models of mind, the limitation of computation to Turing machines is not an imperative when universe is seen as a network of natural computing processes.

21. CorrespondencePrinciple NATURAL COMPUTATION TM Picture after Stuart A. Umplebyhttp://www.gwu.edu/~umpleby/recent_papers/2004_what_i_learned_from_heinz_von_foerster_figures_by_umpleby.htm

22. 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 chequerboard 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 Bloomingtonhttp://www.cs.indiana.edu/~dgerman/2008midwestNKSconference/index.html

23. 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.

24. 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.