Dodig-Crnkovic-Information and Computation


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Presentation at the Workshop on Open System Thinking, University of León, 15-17 May 2013.

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Dodig-Crnkovic-Information and Computation

  1. 1. Open System Thinking: at the crossroads of a complex worldFundación Sierra‐Pambley, León (Spain)Information andInformation andComputation.Dynamics of ComplexityDynamics of Complexityas Natural ComputationGordana Dodig CrnkovicProfessor of Computer ScienceMälardalen University,Mälardalen University, School of Innovation, Design and Engineering
  2. 2. Mälardalen Universityy2
  3. 3. My Teachingy gCDT403 - Research Methods in Natural Sciences and EngineeringCDT403 - Research Methods in Natural Sciences and EngineeringCDT415 - Computing and PhilosophyDVA403 - Research Thinking and Writing ToolboxDVA403 - Research Thinking and Writing ToolboxCDT314 - Formal Languages, Automata and Theory of ComputationCDT212 Information Kunskap Vetenskap (Information Knowledge Science)CDT212 - Information, Kunskap, Vetenskap (Information, Knowledge, Science)CDT409 - Professional Ethics 3
  4. 4. My ResearchComputing Paradigms, Natural/Unconventional ComputingInfo-Computationalism, Computational aspects of Science off / f f S C )Information/Foundations of Information; Social Computing);Computational knowledge generation, Computational aspects ofI lli d C i i Th f S i / Phil h f S iIntelligence and Cognition; Theory of Science/ Philosophy of Science;Computing and Philosophy andEthics (Especially Ethics of Computing, Information Ethics, Roboethics andEngineering Ethics). 4
  5. 5. AbstractInformation is understood as representing the world (reality as an informational web) for a cognizingagent, while information dynamics (information processing, computation) realizes physical lawsthrough which all the changes of informational structures unfoldthrough which all the changes of informational structures unfold.Processes considered rendering information dynamics have been studied, among others in:questions and answers, observations, communication, learning, belief revision, logical inference,game-theoretic interactions and computation. This lecture will put the computational approaches tof f finformation dynamics into a broader context of natural computation, where information dynamics isnot only found in human communication and computational machinery but also in the entire nature.Computation as it appears in the natural world is more general than the human process of calculationmodeled by the Turing machine Natural computing is epitomized through the interactions ofmodeled by the Turing machine. Natural computing is epitomized through the interactions ofconcurrent, in general asynchronous computational processes which are adequately represented bywhat Abramsky names “the second generation models of computation” which we argue to be themost general representation of information dynamics.We will relate the layered organization of informational structures of complex systems find in naturaland social world to their dynamics understood as natural computation.Keywords: information dynamics; natural computationalism; info-computationalism;unified theoriesKeywords: information dynamics; natural computationalism; info computationalism;unified theoriesof information; philosophy of informationp. 5
  6. 6. Based on the Articles● Dodig-Crnkovic G., Dynamics of Information as Natural Computation, Information2011, 2(3), 460-477; doi:10.3390/info2030460 Special issue: Selected Papersfrom FIS 2010 Beijing Conference, 2011.htt // d i /j l/i f ti / i l i / l t d b iji See also:● Dodig-Crnkovic G. and Burgin M., Unconventional Algorithms: Complementarityof Axiomatics and Construction, Special issue of the journal Entropy, 14(11),2066-2080; "Selected Papers from the Symposium on Natural/UnconventionalComputing and its Philosophical Significance", doi:10.3390/e14112066g g, 2012.p. 6
  7. 7. Conceptual Basics:Computation as Information ProcessingComputation as Information Processing7
  8. 8. Conceptual Basics: Levels ofDescription – Levels of Abstraction –pLevels of Organizationp. 8
  9. 9. Conceptual Basics: Network ModellspProtein network in yeast cells Human protein interaction networkProtein network in yeast cells Human protein interaction networkSocial networkHuman connectomep. 9
  10. 10. General Remark.Knowledge & Hinders to KnowledgeKnowledge – what we believe we know: (what we have strongKnowledge what we believe we know: (what we have strongreasons to believe we understand and hold for correct/true).“Philosophy: true, justified belief; certain understanding, as opposedto opinion.” http://oxforddictionaries.comto op o ttp //o o dd ct o a es coIgnorance – white spots on the map of knowledge – what we knowthat we do not know.False believes that look like knowledge – a part of our knowledge,what we believe we know, but actually we are wrong!False beliefs are much more dangerous and difficult epistemicproblems than things that we understand that we do not know.Those are biggest hiders to new knowledge. Questioning of the “self-evident” is necessary, but self-evident is by its nature something weseldom suspect… (Fallibility of scientific knowledge – Popper) p. 10
  13. 13. Complexityp yA complex system is a system composed of interconnectedparts that as a whole exhibit one or more propertiesparts that as a whole exhibit one or more properties(behavior among the possible properties) not obvious fromthe properties of the individual parts.[1]A system’s complexity may be of one of two forms:disorganized complexity and organized complexity.[2]Disorganized complexity is a matter of a very large numberDisorganized complexity is a matter of a very large numberof parts, and organized complexity is a matter of the subjectsystem exhibiting emergent properties. systemComplexity is found between orderly systems with highinformation compressibility and low information content andrandom systems with low compressibility and highttp //e ped a o g/ /Co p e _systerandom systems with low compressibility and highinformation content.p. 13
  14. 14. Complexityp yIn a complex system what we see is dependent on where we are and whatIn a complex system, what we see is dependent on where we are and whatsort of interaction is used to study the system.Study of complex systems:Generative ModelsHow does the complexity arize?Evolution is the most well knownti h igenerative mechanism. 14
  15. 15. Complexity topicsAdaptation; Agent-based Modeling; Artificial Intelligence; ArtificialLife; W. Ross Ashby; Biological Order and Levels of Organization;p y p; y; g g ;Cellular Automata; Chaos and Non-linear Dynamics; CognitiveScience; Collective Cognition; Complexity Measures;Cybernetics; Darwin Machines; Developmental Biology;Dissipative Structures; Edge of Chaos; Emergent Properties;Dissipative Structures; Edge of Chaos; Emergent Properties;Ergodic Theory; Evolution; Evolution of Complexity; EvolutionaryComputation; Evolutionary Economics; Information Theory;Institutions and Organizations; Learning in Games; Machineg gLearning, Statistical inference and Induction; Multi-AgentSystems; Neural Nets, Connectionism, Perceptrons;Neuroscience; Nonequilibrium Statistical Mechanics andThermodynamics; Pattern Formation; Physics of ComputationThermodynamics; Pattern Formation; Physics of Computationand Information; Physical Principles in Biology; Physics; PowerLaw Distributions and Long-Range Correlations; Ilya Prigogine;Random Boolean Networks, Self-organization; Simulations;p. 15
  16. 16. Complexity from Simplicityp y p yExampleshttp://www youtube com/watch?v=gdQgoNitl1g - Complexity from Simplicity, Order from Chaos (1 of 2)(4.54) Gell-Mann On Emergence (1.30)htt // t b / t h? Lk6QU94 Ab8 - The Colors Of Infinity (53:45) CaCie8R4U&feature=related u_CaCie8R4U&feature relatedPatterns in Nature (3.04)p. 16
  17. 17. Generative Models:From the Origins up in ComplexityExamplesp Origin of Life - Abiogenesis (10)The Origin of Life Abiogenesis (10) Origin of the Genetic Code (9.37) Origin of the Brain (9.29) Origin of Intelligence (5.15)p. 17
  18. 18. Self-OrganizationgExampleshttp://www youtube com/watch?v=SkvpEfAPXn4&feature=fvwrel with a mind of their own (1.38)http://www youtube com/watch?v=QdQBH 5Aabs&feature=related Kinematic Cellular Automata (0.06)http://www youtube com/watch?v=KPP 4 LEHXQ Organization of vertical magnetic dipoles floating on water (2.53)“SELF STAR” PROPERTIES IN ORGANIC SYSTEMS:“SELF-STAR” PROPERTIES IN ORGANIC SYSTEMS:self-organization, self-configuration (auto-configuration), self-optimisation(automated optimization), self-repair (self-healing), self-protection (automatedcomputer security), self-explaining, and self-awareness (context-awareness).computer security), self explaining, and self awareness (context awareness).p. 18
  19. 19. Complex Adaptive Systemsp p yExamples adaptive systems (1.02) New Frontier: Systems Biology (12.55) y p yBDF86A6Cities and Countries Part Two: Cultural Systems (4:45) E. Santos receives 2010 IEEE Computer Society Technical AchievementAward (1.38)p. 19
  20. 20. Modelling of Complexityg p yLi t d ibilit● Linear systems – decomposibility -Modelled by Analysis – Top-down – Global (Reductionism)● Non-linear systems – behave as a whole –Modelled by Synthesis - Bottom-up, Distributed(Holism, System approaches)● Complex behavior can emerge from SIMPLE GENERATORS! & Chaos. Emergence & Complexity (5.43)p. 20
  21. 21. Agent-based ModelsgAn agent-based model (ABM) is a computational model for simulatingthe actions and interactions of autonomous individuals in a network,with a view to assessing their effects on the system as a whole.It combines elements of game theory, complex systems, emergence,computational sociology, multi agent systems, and evolutionaryprogramming.Monte Carlo Methods are used to introduce randomness.The basic of ABMs the study of complexity and emergence. Agents (5.06)http://en wikipedia org/wiki/Agent based model 21
  22. 22. Information“The word “information” has been given different meanings byvarious writers in the general field of information theory It isvarious writers in the general field of information theory. It islikely that at least a number of these will prove sufficiently usefulin certain applications to deserve further study and permanentrecognition.It is hardly to be expected that a single concept of informationwould satisfactorily account for the numerous possibleapplications of this general field. “C Shannon, 1993, “The Lattice Theory of Information”22
  23. 23. Information“Yet, despite its ubiquity in everyday experience, the concept ofinformation is conspicuously absent in the theories of classical physicsinformation is conspicuously absent in the theories of classical physics(such as classical mechanics and electromagnetism). The reason forthis is two-fold. First, in the classical framework, the universe evolvesdeterministically and reversibly - the past fully determines the futureand conversely the future determines the past so that no informationand, conversely, the future determines the past, so that no informationis lost or gained over the passage of time.Second, agents can always, in principle, perform measurements thatyield perfect and complete information about the state of reality.Th f t i i i l h th “G d " i fTherefore, every agent in principle has the same “Gods eye" view ofreality. (…)For these two reasons, the concept of information is essentiallyredundant in classical physics.”p yGoyal P., Deciphering Quantum Theory. In "Are we there yet? The search for a theory ofeverything", Ed. M. Emam (Bentham Press, 2011)
  24. 24. What is Information?A special issue of the Journal of Logic, Language andInformation (Volume 12 No 4 2003) is dedicated to theInformation (Volume 12 No 4 2003) is dedicated to thedifferent facets of information.A H db k h Phil h f I f i (VA Handbook on the Philosophy of Information (VanBenthem, Adriaans) is in preparation as one volumeHandbook of the philosophy of science. 24
  25. 25. Information as The Primary Stuff ofythe Universe - PaninformationalismIf information is to replace matter/energy as the primary stuff ofthe universe, as H von Baeyer (2003) suggests, it will provide anew basic unifying framework for describing and predictingnew basic unifying framework for describing and predictingreality in the twenty-first century.L Floridi proposes Informational Structural Realism - a view ofp pthe world as the totality of informational objects dynamicallyinteracting with each other (Synthese Vol.161, Number 2, 219253, A defence of informational structural realism).p. 25
  26. 26. Computation as InformationpProcessingWith information as the primary stuff of the universe, the mostgeneral view of computation is as time-dependent behavior(dynamics) of information.(dynamics) of information.This results in a Dual-aspect Universe: informational structure withcomputational dynamics (Info Computationalism Dodig Crnkovic)computational dynamics. (Info-Computationalism, Dodig Crnkovic)Information and computation are closely related – no computationith t i f ti ( t t ) d i f ti ith twithout information (structure), and no information withoutcomputation (dynamics).p. 26
  27. 27. Computation. Computing Nature andNature Inspired ComputationNature Inspired ComputationIf it looks like a duck,if it walks like a duckand it quacks like a duck,is it a duck?(If it looks like computation is it computation?)
  28. 28. The Universe as a Computer -P t ti liPancomputationalismWe are all living inside a gigantic computer.No, not The Matrix: the Universe.Every process, every change that takesplace in the Universe, may be consideredas a kind of computation.K Zuse, N Wiener, E Fredkin, S Wolfram, GChaitin, S Lloyd, G ‘t Hooft, C Seife, DDeutsch CF von Weizsäcker JA WheelerDeutsch, CF von Weizsäcker, JA Wheeler,among othersSimulation of the formation ofcosmological structures
  29. 29. Turing Machine Model of ComputationTape............Control UnitRead-Write headControl Unit1. Reads a symbolW i b l2. Writes a symbol3. Moves Left or RightThe definition of computation is currently under debate, and an entire issue of thejournal Minds and Machines (1994, 4, 4) was devoted to the question “What isComputation?”Computation? 29
  30. 30. Beyond Conventional Computingy p gMachinery: Natural Computing● According to the Handbook of Natural Computingnatural computing is “the field of research thatinvestigates both human-designed computinginvestigates both human designed computinginspired by nature and computing taking place innature.”● It includes among others areas of cellular automata● It includes among others areas of cellular automataand neural computation, evolutionary computation,molecular computation, quantum computation, nature-inspired algorithms and alternative models ofp gcomputation.p. 30
  31. 31. Beyond Conventional Computingy p gMachinery: Natural Computing● Denning argue that computing today is a naturalscience. Natural computation provides a basis for aunified understanding of phenomena of embodiedunified understanding of phenomena of embodiedcognition, intelligence and knowledge generation.● ”I invite readers not on a visit to an archaeological● I invite readers not on a visit to an archaeologicalmuseum, but rather on an adventure in science inmaking” Prigogine in The End of Certainty: Time,Chaos and New Laws of Nature, 1997Chaos and New Laws of Nature, 1997p. 31
  32. 32. Turing Machines Limitations –Self-generating SystemsAccording to George Kampis, complexbiological systems must be modeled as self-referential, self-organizing systems called "component-systems" (self-generating systems), whose behavior, though computational ina generalized sense, goes far beyond Turing machine model.p. 32
  33. 33. The Wildfire Spread of Computationalp pIdeas"Everyone knows that computational and information technology hasspread like wildfire throughout academic and intellectual life. But thespread of computational ideas has been just as impressive. Biologists notonly model life forms on computers; they treat the gene, and even wholeorganisms, as information systems. Philosophy, artificial intelligence, andcognitive science dont just construct computational models of mind; theyg j p ; ytake cognition to be computation, at the deepest levels. …Physicists dont just talk about the information carried by a subatomicparticle; they propose to unify the foundations of quantum mechanics withnotions of information “notions of information.Brian Cantwell Smith, The Wildfire Spread of Computational Ideas, 2003p. 33
  34. 34. Cognition as Computation(Processing of Information)(Processing of Information)Networks at the Basis of Cognition100 billions of neurons connectedwith tiny "wires" in total longermore than two times the earthcircumference. This intricate andapparently messy neural circuitth t i ibl fthat is responsible for ourcognition and behavior. of Computation: Information Processing in Single NeuronsChristof Koch, 1999. p. 34
  35. 35.
  36. 36. What is Cognition?● Process of perceivingg● Process of perceiving,reasoning, decisionmaking and thinking.● Body’s & brain ’s way ofprocessing information tocreate meaning / makecreate meaning / makesense for an agent.36
  37. 37. Cognitive Network TechnologyCog t e et o ec o ogy 37
  38. 38. The Human Brain Projectj(€500 million 10-year, FET flagship)The human brain project has ten years to build the brain withThe human brain project has ten years to build the brain withsupercomputers in collaboration between 80 researchinstitutions in Europe. Lausanne, CHProf Henry MarkramProf. Henry Markram28th January 2013 p. 38
  39. 39. Naturalized EpistemologyData – Information - KnowledgeData Information - KnowledgeInformation Processing“Dynamics lead to statics, statics leads to dynamics, and thesimultaneous analysis of the two provides the beginning of anunderstanding of that mysterious process called mind ”understanding of that mysterious process called mind.Goertzel, Chaotic LogicSelf-generating computation systems“a component system is a computer which, when executing itsoperations (software) builds a new hardware.... [W]e have acomputer that re-wires itself in a hardware-software interplay:the hardware defines the software and the software definesthe hardware defines the software and the software definesnew hardware. Then the circle starts again.”George Kampis p 223 Self-Modifying Systems in Biology andGeorge Kampis, p. 223 Self-Modifying Systems in Biology andCognitive Science39
  40. 40. Naturalized EpistemologyData – Information - Knowledge asData Information - Knowledge asInformation ProcessingInteractive explanation is future oriented, based on the fact that theagent is concerned with the anticipated future potentialities ofagent is concerned with the anticipated future potentialities ofinteraction.The actions are oriented internally to the system, which optimizestheir external outcome.their external outcome.The environment in the interactive case represents resources andconstraints for the agent.Information exchange with the environment is a part of interactiveInformation exchange with the environment is a part of interactiverelation.40
  41. 41. How Does Nature Compute?Evolution + Self-OrganizationEvolution + Self-OrganizationC iti f th l ti h ti thCritics of the evolutionary approach mention theimpossibility of “blind chance” to produce suchhighly complex structures as intelligent livingorganisms Proverbial monkeys typingorganisms. Proverbial monkeys typingShakespeare are often used as illustration (aninteresting account is given by Gell-Man in hisQuark and the Jaguar )Quark and the Jaguar.)Chaitin and Bennet: Typing monkeys’ argumentdoes not take into account physical laws of theuniverse which dramatically limit what can beuniverse, which dramatically limit what can betyped. Moreover, the universe is not a typewriter,but a computer, so a monkey types random inputinto a computer The computer interprets theinto a computer. The computer interprets thestrings as programs.41
  42. 42. Naturalizing EpistemologyNaturalized epistemology (Feldman, Kornblith, Stich) is, in general, anp gy ( , , ) , g ,idea that knowledge may be studied as a natural phenomenon --that the subject matter of epistemology is not our concept ofknowledge, but the knowledge itself.The stimulation of his sensory receptors is all the evidence anybodyhas had to go on, ultimately, in arriving at his picture of the world. Whyt j t h thi t ti ll d ? Wh t ttlnot just see how this construction really proceeds? Why not settlefor psychology? ("Epistemology Naturalized", Quine 1969; emphasismine)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. 43. Why not Settle for Computingh M d li C iti ?when Modeling Cognition?In this framework knowledge is seen as a result of the structuring ofinput data:data → information → knowledgedata → information → knowledgeby an interactive computational process going on in the nervoussystem during the adaptive interplay of an agent with theenvironment which clearly increases its ability to cope with theenvironment, which clearly increases its ability to cope with thedynamical changing of the world.Hebbian learningHebbian learning 43
  44. 44. Data – Information - Knowledge asgInformation ProcessingPragmatism suggests that interaction is the most appropriateframework for understanding of cognition.Interactive explanation is future oriented; based on the fact thatthe agent is concerned with anticipated future potentialities ofinteraction.44
  45. 45. Data – Information - Knowledge asgInformation ProcessingThe actions are oriented internally to the system, whichoptimizes their internal outcome, while the environment in theinteractive case represents primarily resources for the agent.Correspondence (mutual influence) with the environment is apart of interactive relation.[One can say that living organisms are “about” the environment, that they havedeveloped adaptive strategies to survive by internalizing environmentalconstraints. The interaction between an organism and its environment is realizedthrough the exchange of physical signals that might be seen as data, or whenstructured, as information. Organizing and mutually relating different pieces ofinformation results in knowledge. In that context, computationalism appears asthe most suitable framework for naturalizing epistemology. ]45
  46. 46. Cognition as ComputationInformation/computation mechanisms areInformation/computation mechanisms arefundamental for evolution of intelligent agents.Their role is to adapt the physical structure andbehavior that will increase organisms chances ofgsurvival, or otherwise induce some otherbehavior that might be a preference of an agent. this pragmatic framework, meaning in generalis use, which is also the case with meaning ofinformation.
  47. 47. NaturalistUnderstanding ofUnderstanding ofCognitionA great conceptual advantage of cognition as a central focus of study isthat all living organisms possess some cognition, in some degree.g g p g , gMaturana and Varela (1980)Maturana’s and Varelas’ understanding ofgcognition is most suitable as the basis for acomputationalist account of the naturalizedevolutionary epistemology.See also:Gordana Dodig-CrnkovicWhere do New Ideas Come From?How do they Emerge?Epistemology as Computation (InformationEpistemology as Computation (InformationProcessing)in Randomness & Complexity, from Leibniz toChaitin, C. Calude ed. 2007p. 47
  48. 48. Unified Info-ComputationalN t li i f E i t lNaturalizing of EpistemologyAt the physical level, living beings are open complex computational systemsin a regime on the edge of chaos, characterized by maximal informationalcontent.Complexity is found between orderly systems with high informationcompressibility and low information content and random systems with lowibilit d hi h i f ti t tcompressibility and high information content.Fixed points are like crystals in that they are for the most part static andorderly Chaotic dynamics are similar to gases which can be described onlyorderly. Chaotic dynamics are similar to gases, which can be described onlystatistically. Periodic behavior is similar to a non-crystal solid, andcomplexity is like a liquid that is close to both the solid and the gaseousstates. In this way, we can once again view complexity and computation asexisting on the edge of chaos and simplicity. (Flake 1998)p. 48
  49. 49. Unified Info-ComputationalN t li i f E i t lNaturalizing of EpistemologyDynamics of Open SystemsBased on the natural phenomena understood as info computationalBased on the natural phenomena understood as info-computational,computer in general is conceived as an open interactive system (digitalor analogue; discrete or continuous) in the communication with theenvironment.Classical Turing machine is a subset of a more generalinteractive/adaptive/self-organizing universal natural computer. Livingfsystem is defined as "open, coherent, space- time structure maintainedfar from thermodynamic equilibrium by a flow of energy through it."(Chaisson, 2002.)On a computationalist view, the dynamics of an organism areconstituted by computational processes. In the open system of livingcell an info-computational process takes place using DNA, exchangingp p p g g ginformation, matter and energy with the environment.p. 49
  50. 50. Cognition as Re-structuring anA t th h I t ti ithInfo computationalist project of naturalizing epistemology byAgent through Interaction withthe EnvironmentInfo-computationalist project of naturalizing epistemology bydefining cognition as information processing phenomenon isclosely related to the development of multilevel dynamicalcomputational models and simulations of cognizing systemscomputational models and simulations of cognizing systems,and has important consequences for the development ofartificial intelligence and artificial life.Natural computation opens possibilities to implement embodiedcognition into artificial agents, and perform experiments onsimulated eco-systemssimulated eco systems.p. 50
  51. 51. Unified Info-Computational TheoryContinuum-discrete controversy is bridged by the same dual-aspectapproach (Info-Computationalism).Thi t th t i t t ti l i d hi h l iThis counters the argument against computational mind which claimsthat computational mind must be discrete.It is also an answer to the critique that the universe might not beIt is also an answer to the critique that the universe might not becomputational as it might not be entirely digital.Computation as information processing can be both continuous anddiscretediscrete.p. 51
  52. 52. Unified Info-Computational TheoryComputationalist and informationalist frameworks meet in the commondomain of complex systemsdomain of complex systems.The Turing Machine model is about mechanical, syntactic symbolmanipulation as implemented on the hardware level.C l it i t b f d th ft l l Diff t l l fComplexity is to be found on the software level. Different levels ofcomplexity have different meanings for different cognizing agents.p. 52
  53. 53. Computation in Closed vs. Openp pSystems● Since the 1950s computational machinery has been● Since the 1950s computational machinery has beenincreasingly used to exchange information and computersgradually started to connect in networks and communicate. Inthe 1970s computers were connected via telecommunicationsthe 1970s computers were connected via telecommunications.● The emergence of networking involved a rethinking of thenature of computation and boundaries of a computernature of computation and boundaries of a computer.Computer operating systems and applications were modified toaccess the resources of other computers in the network. In1991 CERN created the World Wide Web which resulted in1991 CERN created the World Wide Web, which resulted incomputer networking becoming a part of everyday life forcommon people.p. 53
  54. 54. Computation in Closed vs. Openp pSystems● With the development of computer networks two characteristics● With the development of computer networks, two characteristicsof computing systems have become increasingly important:parallelism/concurrency and openness – both based oncommunication between computational unitscommunication between computational units.● Comparing new open-system with traditional closed-systemcomputation models Hewitt characterizes the Turing machinecomputation models, Hewitt characterizes the Turing machinemodel as an internal (individual) framework and his ownActor model of concurrent computation as an external(sociological) model of computing(sociological) model of computing.p. 54
  55. 55. Computation as Interaction andpInteractive ComputingThe following characteristics distinguish this new interactiveThe following characteristics distinguish this new, interactivenotion of computation (Wegner, Goldin):C t ti l bl i d fi d f i t k [i● Computational problem is defined as performing a task, [in adynamical environment – my addition] rather than(algorithmically) producing an answer to a question.● Dynamic input and output are modelled by dynamic streamswhich are interleaved; later values of the input stream mayd d li l i th t t t d idepend on earlier values in the output stream and vice versa.p. 55
  56. 56. Computation as Interaction andpInteractive Computing● The environment of the computation is a part of the model,playing an active role in the computation by dynamicallysupplying the computational system with the inputs, andsupplying the computational system with the inputs, andconsuming the output values from the system.● Concurrency: the computing system (agent) computes inparallel with its environment, and with other agents. (Agents canparallel with its environment, and with other agents. (Agents canconsist of agents networks, recursively.)● Effective non-computability: the environment cannot beassumed to be static or effectively computable. We cannotassumed to be static or effectively computable. We cannotalways pre-compute input values or predict the effect of thesystems output on the environment.p. 56
  57. 57. ConcurrencyyThe advantages of concurrency theory that is used to simulateobservable natural phenomena are that:“it is possible to express much richer notions of time and spacein the concurrent interactive framework than in a sequentialone. In the case of time, for example, instead of a unique totalorder, we now have interplay between many partial orders ofevents--the local times of concurrent agents--with potentialsynchronizations, and the possibility to add global constraintson the set of possible scheduling. This requires a much morecomplex algebraic structure of representation if one wants tocomplex algebraic structure of representation if one wants to"situate" a given agent in time, i.e., relatively to the occurrenceof events originated by herself or by other agents.“V Schachter “How Does Concurrency Extend the Paradigm ofV. Schachter, How Does Concurrency Extend the Paradigm ofComputation?,” Monist, vol. 82, no. 1, pp. 37–58, 1999.p. 57
  58. 58. Digital vs. Analog, Discrete vs.g g,Continuous and Symbolic vs.Sub-symbolic Computationy p● “Symbolic simulation is thus a two-stage affair: first thei f i f t t f th th t h dmapping of inference structure of the theory onto hardwarestates which defines symbolic computation; second, themapping of inference structure of the theory onto hardwarestates which (under appropriate conditions) qualifies thestates which (under appropriate conditions) qualifies theprocessing as a symbolic simulation.● Analog simulation, in contrast, is defined by a singlemapping from causal relations among elements of themapping from causal relations among elements of thesimulation to causal relations among elements of thesimulated phenomenon.”R. Trenholme, “Analog Simulation,” Philosophy of Science, vol. 61, no. 1,pp. 115–131, 1994. p. 58
  59. 59. Symbolic vs. Sub-symbolicy yComputationDouglas Hofstadter in his dialogue “Prelude…Ant fugue” in Godel, Escher, Bach.p. 59
  60. 60. The Unreasonable Ineffectivenessof Mathematics in Biology and Biasof Mathematicians● “The unreasonable effectiveness of mathematics” asobserved in physics by Wigner is missing in biologyobserved in physics by Wigner is missing in biology.● Mathematics is disembodied* while computing isb di d i hi th t i t ith thembodied in a machine that can communicate with thephysical world in real time.* Actually embodied in a human who takes the embodiment as grantedand disregards its constraints. p. 60
  61. 61. The Unreasonable IneffectivenessOf Mathematics In Biology And BiasOf MathematiciansBeyond the limits of TM model:● Embodiment invalidating the `machine as data and universalityparadigm.● The organic linking of mechanics and emergent outcomesdelivering a clearer model of supervenience of mentality onbrain functionality, and a reconciliation of different levels ofeffectivity.● A reaffirmation of experiment and evolving hardware, for bothAI and extended computing generally.● The validating of a route to creation of new information throughinteraction and emergence.S. B. Cooper, “Turing’s Titanic Machine?,” Communications ofthe ACM, vol. 55, no. 3, pp. 74–83, 2012p. 61
  62. 62. Info-computationalism,p ,in a Nutshell● Nature can be described as a complex informationalstructure for a cognizing agent.● Computation is information dynamics (informationprocessing)● Computation is constrained and governed by the lawsof physics on the fundamental level.p yp. 62
  63. 63. Morphological Computing.p g p gMeaning Generation From RawData To Semantic Information● Turing proposed diffusion reaction model of morphogenesis asthe explanation of the development of biological patterns such asthe spots and stripes on animal skin.● Morphogenesis is a process of morphological computing.Physical process – though not computational in the traditionalsense, presents natural (unconventional), morphologicalcomputation. Essential element in this process is the interplaybetween the informational structure and the computationali f ti lf t t i d i f ti i t tiprocess - information self-structuring and information integration,both synchronic and diachronic, going on in different time andspace scales in physical bodies.p. 63
  64. 64. Morphological Computing.p g p gMeaning Generation From RawData To Semantic Information● Info-computational naturalism describes nature asinformational structure – a succession of levels ofinformational structure a succession of levels oforganization of information.● Morphological computing on that informational● Morphological computing on that informationalstructure leads to new informational structures viaprocesses of self-organization of information.● Evolution itself is a process of morphologicalcomputation on a long-term scale.p. 64
  65. 65. Conclusions & Future WorkPresent account highlights several topics ofPresent account highlights several topics ofimportance for the development of new understandingof computation and its role in the physical world:● Natural computation● Natural computation● The relationship between model and physicalimplementationI t ti it f d t l f t ti l● Interactivity as fundamental for computationalmodelling of concurrent information processingsystems such as living organisms and their networksN d l t i th ti l d lli d d● New developments in mathematical modelling neededto support this generalized framework.p. 65
  66. 66. Conclusions & Future Work● Besides the Turing machine as a well developed and● Besides the Turing machine as a well developed andgenerally established model of computation, variety ofnew ideas, still under development are taking shapeand have good prospects to extend our understandingand have good prospects to extend our understandingof computation and its relationship to physicalimplementations.● It will be instructive within the info-computationalframework to study processes of self organization ofinformation in physical agents (as well as in populationinformation in physical agents (as well as in populationof agents) able to re-structure themselves throughinteractions with the environment as a result ofmorphological (morphogenetic) computation.p g ( p g ) pp. 66
  67. 67. “(O)ur task is nothing less than to discover a new,( ) g ,broader, notion of computation, and to understand theworld around us in terms of information processing.”G. Rozenberg and L. Kari, “The many facets of natural computing,”g , y p g,Communications of the ACM, vol. 51, pp. 72–83, 2008.p. 67
  68. 68. Qualitative Complexityp yEcology Cognitive Processes and the Re-Ecology, Cognitive Processes and the ReEmergence of Structures in Post-HumanistSocial TheoryBy John Smith, Chris Jenksp. 68
  69. 69. Special Sciences andSpecial Sciences andthe Unity of Sciencep. 69
  70. 70. Books in the New Paradigm:A New Kind of ScienceBook available at:http://www.wolframscience.comBased on cellular automata, complexity emergingfrom repeating very simple rulesSee also New Kind of Science - Stephen Wolframp. 70
  71. 71. Programming the Universe:A Quantum ComputerA Quantum ComputerScientist Takes on theCosmosCosmosb S th Ll dby Seth Lloydp. 71
  72. 72. The Universe as QuantumI f iInformationp. 72
  73. 73. A New Paradigm of ComputingI i C i– Interactive ComputingInteractive Computation: the New ParadigmSpringer-Verlag in September 2006g gDina Goldin, Scott Smolka, Peter Wegner, eds.--------------------------------------Dina Goldin, Peter WegnerThe Interactive Nature of Computing:Refuting the Strong Church - Turing ThesisRefuting the Strong Church - Turing ThesisMinds and MachinesVolume 18 , Issue 1 (March 2008) p 17 - 38 73
  74. 74. Self-modifying Systems inBi l d C i i S iBiology and Cognitive ScienceThe theme of this book is the self-generationof information by the self-modification ofysystems. The author explains why biologicaland cognitive processes exhibit identitychanges in the mathematical and logicalsense This concept is the basis of a newsense. This concept is the basis of a neworganizational principle which utilizes shifts ofthe internal semantic relations in systems.More about the book: 74
  75. 75. A Computable Universep. 75
  76. 76. Computation, Information, Cognitionp gComputation, Information,CognitionEditor(s): Gordana Dodig Crnkovic andInformation and ComputationEditor(s): Gordana Dodig Crnkovic andMark Burgin, World Scientific, 2011Computing NatureEditor(s): Gordana Dodig Crnkovic andRaffaela Giovagnoli Springer 2013Editor(s): Gordana Dodig Crnkovic andSusan Stuart, Cambridge ScholarsPublishing, 2007g , , Raffaela Giovagnoli, Springer, 2013 p. 76