Life Cognition Infocomputation, CiE 20140623

8/12/2019 Life Cognition Infocomputation, CiE 20140623

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Modeling Life asCognitiveInfo-computation

Gordana Dodig Crnkovic

Professor of Computer ScienceSchool of Innovation, Design andEngineeringMlardalen University, Sweden

http://www.idt.mdh.se/~gdc/

Computability in Europe 2014: Language, Life, Limits. June 23-27, Budapest
http://www.idt.mdh.se/~gdc/http://www.idt.mdh.se/~gdc/


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Mlardalen University Sweden12,000 students and around 900 employees, of which 67 are professors


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After half a century of research in cognitive science, cognitionstill 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 andunderstanding 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.


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Cognitive science

p. 4Biology /embodiment/embeddedness (situatedness)

Biology /embodiment/embedded

Biology /embodiment/embeddess

Biology /embodiment/embeddedness

http://en.wikipedia.org/wiki/Cognitive_science

http://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltext
http://en.wikipedia.org/wiki/Cognitive_sciencehttp://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltexthttp://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltexthttp://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltexthttp://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltexthttp://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltexthttp://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltexthttp://cacm.acm.org/magazines/2011/8/114944-cognitive-computing/fulltexthttp://en.wikipedia.org/wiki/Cognitive_science


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Traditional anthropogenic approach to cognition* onlyhumans are cognitive agents Biogenic approaches* cognition is ability of all livingorganisms, no matter how primitive goes a level below thecomplexity of human language to complex systems chemicalsignaling 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 ispossible to construct cognitive agents starting from abioticsystems 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)


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(Anthropogenic) Cognition is the PROCESS by whichhumans acquire, integrate and generate knowledge. It isthe result of attention , perception , memory , and executivefunctions of learning and behavior generation (information

integration and transformation of perception into higherorder symbols; comparison of incoming information withthe information stored in memory together with valuesystem and biological drives)

(Anthropogenic) Intelligence is the ABILITYto understand

and reason upon (i.e. structure and interrelate and operateupon) what is perceived, memorized and learned. Intelligence as ABILITY is based on cognition as PROCESS.

Anthropogenic Cognition vs. AnthropogenicIntelligence

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(Biogenic and Abiotic)Cognition is the PROCESS by whichsimple 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 ABILITYto

structure, interrelate and operate upon information that isperceived, memorized and learned.

Intelligence as ABILITY is based on cognition as PROCESS.

Similarly, Biogenic and Abiotic Cognition vs.Biogenic and Abiotic (Artifactual) Intelligence

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We focus on cognition and propose the common frameworkfor understanding Anthropogenic, Biogenic and AbioticCognition.

We argue that (as in the rest of biology) nothing makessense 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 cognitivebehavior with living organisms, and draw conclusions.

Connecting Anthropogenic with Biogenic andAbiotic Cognition

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A cognitive system is a system whose organization defines adomain of interactions in which it can act with relevance tothe maintenance of itself , and the process of cognition is theactual (inductive) acting or behaving in this domain . Livingsystems 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 ofcomputation asthe mechanism underlying biological processes throughself-generation of information by non-trivial change (self-modification) of systems (Kampis, 1991.Self-ModifyingSystems in Biology and Cognitive Science: A New Framework forDynamics, Information and Complexity).

Living as a process is a process of cognition

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Information, computation, cognitionAgent-centered Hierarchies of Levels

In this lecture I will present a unified framework for modeling of information,computation and cognitionfrom an agents perspective .

p. 10

information computation

cognition

Fruit fly brain micrograph

Fruit fly brain neurons

Fruit fly larva

http://www.sciencedirect.com/science/article/pii/S0378437104014839
http://www.sciencedirect.com/science/article/pii/S0378437104014839http://www.sciencedirect.com/science/article/pii/S0378437104014839


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p. 11

Starting from anthropogenic perspective: Thebrain development - Cognition as biological phenomenon

http://lcn.brain.riken.jp/tool_kit_evolution.htm

The brain development may be carried outbased on the basic body-organization-blueprintsthat are specific to an animal species dependingon their strategy to survive in an environment.

To understand how our brains are established inthe course of evolution, we have beenconducting a comparison of the structure andfunction of the gene that are essential forestablishing body organization and braindevelopment in a wide rage of animals with

nervous system .
http://lcn.brain.riken.jp/tool_kit_evolution.htmhttp://lcn.brain.riken.jp/tool_kit_evolution.htm


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Wonders of evolution the smallest insectwith brain, smaller than an amoeba

p. 12http://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 twoprotozoans in comparison.(A)Megaphragma mymaripenne.(B)Paramecium caudatum.(C)Amoeba proteus.

Scale bar for A C is 200 m.

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 340,000Honeybee has 850,000.95% of the waspss neurons have no nucleus.


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Information, computation,cognition Agent-centered Hierarchy of Levels

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Human connectomehttp://outlook.wustl.edu/2013/jun/human-connectome-project

http://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdf The Human Brain Project

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Natural information processing
http://outlook.wustl.edu/2013/jun/human-connectome-projecthttp://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdfhttp://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdfhttp://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdfhttp://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdfhttp://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdfhttp://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdfhttp://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdfhttp://outlook.wustl.edu/2013/jun/human-connectome-projecthttp://outlook.wustl.edu/2013/jun/human-connectome-projecthttp://outlook.wustl.edu/2013/jun/human-connectome-projecthttp://outlook.wustl.edu/2013/jun/human-connectome-projecthttp://outlook.wustl.edu/2013/jun/human-connectome-project


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The Human Brain Project (HBP) is a large scientific researchproject, directed by the cole polytechnique fdrale deLausanne and largely funded by the European Union, whichaims to simulate the complete human brain on

supercomputers to better understand how it functions.The BRAIN Initiative (Brain Research through AdvancingInnovative Neurotechnologies , also referred to as the BrainActivity Map Project ) is a proposed collaborative researchinitiative announced by the Obama administration on April 2,2013, with the goal of mapping the activity of every neuron inthe human brain . Based upon the Human Genome Project, theinitiative has been projected to cost more than $300 millionper year for ten years.

Source: Wikipedia

Current brain research initiatives

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http://en.wikipedia.org/wiki/European_Unionhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Supercomputershttp://en.wikipedia.org/wiki/Barack_Obamahttp://en.wikipedia.org/wiki/Neuronhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Human_Genome_Projecthttp://en.wikipedia.org/wiki/Human_Genome_Projecthttp://en.wikipedia.org/wiki/Human_Genome_Projecthttp://en.wikipedia.org/wiki/Human_Genome_Projecthttp://en.wikipedia.org/wiki/Human_Genome_Projecthttp://en.wikipedia.org/wiki/Human_Genome_Projecthttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Neuronhttp://en.wikipedia.org/wiki/Barack_Obamahttp://en.wikipedia.org/wiki/Barack_Obamahttp://en.wikipedia.org/wiki/Barack_Obamahttp://en.wikipedia.org/wiki/Supercomputershttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/European_Unionhttp://en.wikipedia.org/wiki/European_Unionhttp://en.wikipedia.org/wiki/European_Union


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The Allen Institute conducting and completing large-scalebrain mapping projects for the last 10 years. In early 2012launched three additional major research initiatives to drivecritical advances in understanding how the brain works anddevelops.

Neural Coding (understanding how information is encoded anddecoded in the mammalian brain)

Molecular Networks (understanding how information isencoded and decoded within a cell)

Cell Types (large-scale descriptive resources of human and

mouse brain cell types at molecular, morphological andconnectional levels) Atlasing (collection of online public resources integrating

extensive genomic and neuroanatomic data)

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Current brain research initiatives

http://www.alleninstitute.org/science/research_programs/index.html
http://www.alleninstitute.org/science/research_programs/index.htmlhttp://www.alleninstitute.org/science/research_programs/index.html


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Even though anthropogenic approach to cognition is theoldest and by far the most dominant one, it is the mostdifficult approach to the most complex problem embodied human brain.

The study of biogenic and abiotic cognition can help ustrace evolutionary roots of cognitive capacities in livingorganisms (biogenic) and construct (abiotic) artifact withcognitive and intelligent behavior (cognitive computing andcognitive robotics).

Therefore we start with simplest living systems such asbacteria to try to understand the basis of their cognitivebehavior in informational structures and their dynamics(computational processes).

The Strategy of Info-ComputationalApproach

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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 anothersystem . In other words, information is *-relative .

2. Computation is information processing (dynamics of

information). It is physical process of morphologicalchange in the informational structure (physicalimplementation of information, as there is no informationwithout 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.


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3. Both information and computation appear on manydifferent levels oforganisation /abstraction/resolution/granularity of

matter/energy in space/time.

4. Of all agents (entities capable of acting on their ownbehalf) only living agents have the ability to activelymake choices so to increase the probability of their own

continuing existence . This ability of living agents to actautonomously on its own behalf is based on the use ofenergy/matter and information from the environment.


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5. Cognition consists of all (info-computational) processesnecessary to keep living agent s organizational integrity on

all different levels of its existence.Cognition = info-computation6. Cognition is equivalent with the (process of) life.*

Its complexity increases with evolution.This complexification is a result of morphological

computation.


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* Maturana, H. & Varela, F., 1980. Autopoiesis and cognition: the realization of the living, DordrechtHolland: D. Reidel Pub. Co .


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Information is the difference that makes a difference. Gregory Bateson

It is the difference in the world that makes the difference foran agent . Here the world includes agents themselves too.

Information expresses the fact that a system is in a certainconfiguration that is correlated to the configuration ofanother system. Any physical system may contain informationabout 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.


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Informational structural realism (Floridi, Sayre) argues thatinformation (for an agent) constitutes the fabric of reality:

Reality consists of informational structures organized ondifferent levels of abstraction/resolution .

See also:

Van Benthem and Adriaans (2008) Philosophy of Information , In: Handbook of thephilosophy of science series. http://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.
http://www.illc.uva.nl/HPIhttp://www.illc.uva.nl/HPI


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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 certainconfiguration that is correlated to the configuration of anothersystem . Any physical system may contain information aboutanother physical system . (Hewitt, 2007), we get:

Information is defined as the difference in one physical systemthat makes the difference in another physical system .

The relational definition of information

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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 generaldefinition of computation .

This definition of computation has a profound consequence ifcomputation is the dynamics of informational structures of theuniverse, the dynamics of the universe is a network ofcomputational processes (natural computationalism).

Structure vs. process

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

2011.


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Information is defined as the difference in one physical systemthat makes the difference in another physical system .

This reflects the relational character of information and thusagent-dependency which calls for agent-based or actor models.

As asynthesis of informational structural realism and naturalcomputationalism , I propose info-computational structuralismthat 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 basicand inseparable elements necessary for naturalizing . (Dodig-

Crnkovic, 2009)

Reality for an agent - informational structurewith computational dynamics

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Computational modeling in cognitive science

Symbolic modeling evolved from the computer scienceparadigms using the technologies of Knowledge-based systems -"Good Old-Fashioned Artificial Intelligence" (GOFAI). Used inexpert systems and cognitive decision making, and extended to

socio-cognitive approach.

Subsymbolic modeling includes Connectionist/neural networkmodels.

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Computational modeling in cognitive science

Dynamical systems theory c losely related to ideas about theembodiment of mind and the environmental situatedness ofhuman cognition based on physiological and environmentalevents. The most important here is the dimension of time.

Neural-symbolic integration techniques putting symbolic modelsand connectionist models into correspondence.

Bayesian models of brain function which assume that thenervous system maintains internal probabilistic models that areupdated by neural processing of sensory information using

methods approximating those of Bayesian probability.

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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 toalgorithm and it may be used for description of differentprocesses in living organisms.

We need computational models for the basic characteristics oflife as the ability to differentiate and synthesize information,make a choice, to adapt, evolve and learn in an unpredictableworld. That requires computational mechanisms and modelswhich are not mechanistic and predefined as Turing machine. *

* We need learning such as PAC Probably Approximately Correct Leslie Valiantp. 27


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Computationalism is not what it used to be that is the thesis that persons are Turing machines

Computational approaches that are capable of modellingadaptation, evolution and learning are found in the field ofnatural computation and computing nature .

Cognitive computing and cognitive robotics are the attemptsto construct abiotic systems exhibiting cognitivecharacteristics .

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 continuingexistence, which was the evolutionary role of cognition inbiotic systems.

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Biological processes are often compared to computation andmodeled on the Universal Turing Machine. While manysystems or aspects of systems can be well described in thismanner, Turing computation can only compute what it has

been programmed for. ()Yet, adaptation, choice and learning are all hallmarks ofliving organisms. This suggests that there must be a differentform of computation capable of this sort of calculation. ( )Super-Turing model is both capable of modeling adaptivecomputation, and furthermore, a possible answer to thecomputational model searched for by Turing himself.

Turing computation: Turing on Super-Turingand adaptivity according to Siegelmann

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Hava T. Siegelmann, Turing on Super-Turing and adaptivity, Progress inBiophysics and Molecular Biology,http://www.sciencedirect.com/science/article/pii/S0079610713000278


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p. 30

In the Actor Model [Hewitt, Bishop andSteiger 1973; Hewitt 2010], computationis conceived as distributed in space,where computational devices

communicate asynchronously and theentire computation is not in any well-defined state .

(An Actor can have information about other Actorsthat it has received in a message about what it waslike when the message was sent.) Turing's Model is aspecial case of the Actor Model. (Hewitt, 2012)

Hewitts computational devices are conceived as computational agents

informational structures capable of acting on their own behalf.

Actor model of concurrent distributedcomputation


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Actors are the universal primitives of concurrent distributeddigital computation. In response to a message that it receives,an Actor can make local , create more Actors, sendmore messages, and designate how to respond to the next

message received.

For Hewitt, Actors become Agents only when they are able toprocess 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 webroadly interpret the above capacities, life-like Actors.

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p. 32

Summary of interactions between particles described by the StandardModel.

http://en.wikipedia.org/wiki/Standard_Model

Unlike other models ofcomputation that are based

on mathematical logic, settheory, algebra, etc. theActor model is based onphysics, especially quantumphysics and relativistic

physics. (Hewitt, 2006)

http://en.wikipedia.org/wiki/Standard_Modelhttp://en.wikipedia.org/wiki/Standard_Model


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Computing nature andnature 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 itcomputation?)

Peter J. Denning. 2007. Computing is a natural science.Commun. ACM 50, 7 (July 2007), 13-18. DOI=10.1145/1272516.1272529http://doi.acm.org/10.1145/1272516.1272529


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Complex biological systems must be modeled as self-referential, self-organizing "component-systems"(George Kampis) which are self-generating and whosebehavior, though computational in a general sense, goesfar 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 ahardware-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 ForDynamics, Information, and Complexity, Pergamon Press

Dodig Crnkovic, G. (2011). Significance of Models of Computation from Turing Model to NaturalComputation. Minds and Machines , (R. Turner and A. Eden guest eds.) Volume 21, Issue 2, p.301.

Computing cells: self-generating systems


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Computation is implemented at differentlevels of resolution Computing architecture

p. 35Some layered computational architectures


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Multisensori information integration

Information integration is critical for the brain tointeract effectively with our multisensoryenvironment. The human brain integratesinformation from multiple senses with priorknowledge to form a coherent and more reliablepercept of its environment. (learning)Within the cortical hierarchy, multisensoryperception emerges in an interactive process withtop-down prior information constraining theinterpretation of the incoming sensory signals.

Marcin Schrder in the book Computing Natureadresses the Dualism of Selective and StructuralInformation, describing information integration.

http://www.birmingham.ac.uk/research/activity/behavioural-neuro/comp-cog-neuro/index.aspx


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Cognition: Agency-based hierarchies of levels.World as information for an agent

From: http://www.alexeikurakin.org

Potential information Actual information for an agentCognition

http://www.tbiomed.com/content/8/1/4 scale-invariance of self-organizational dynamics ofenergy/matter at all levels of organizational hierarchy

b d h h f l l
http://www.alexeikurakin.org/http://www.tbiomed.com/content/8/1/4http://www.tbiomed.com/content/8/1/4http://www.alexeikurakin.org/


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38http://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.html

C. Elegans has 302 neurons (humans have 100 billion). The pattern ofconnections between neurons has been mapped out decades ago usingelectron microscopy, but knowledge of the connections is not sufficient tounderstand (or replicate) the information processor they represent, forsome connections are inhibitory while others are excitatory .

Potential informationOutside reality for C-elegans

Interaction interface for C-elegansCognition

Actual Information C-elegans

Agency-based hierarchies of levels.World as information for an agent
http://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.htmlhttp://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.htmlhttp://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.htmlhttp://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.htmlhttp://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.htmlhttp://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.htmlhttp://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.htmlhttp://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.htmlhttp://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.htmlhttp://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.html


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Reality for an agent is an informational structure with whichagent interacts. As systems able to act on their own behalfand make sense (use) of information, cognitive agents are ofspecial 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 fromwithin where an observer is being within the universe, unlikethe god-eye-perspective from the outside of the universe.

(Rssler, 1998)

Reality for an agent an observer-dependent reality

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* for a very simple agent can be the ability to optimize gains and minimizerisks.(Popper, 1999) p. 61 ascribes the ability to know to all living: Obviously, in the biologicaland evolutionary sense in which I speak of knowledge, not only animals and men have

expectations and therefore (unconscious) knowledge, but also plants; and, indeed, allorganisms.


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Info-computational framework and levels

The question of levels of organization/levels of abstraction for an agent isanalyzed within the framework of info-computational constructivism , withnatural phenomena modeled as computational processes on informationalstructures.

Info-computationalism is a synthesis of informational structuralism (natureis an informational structure for an agent) (Floridi, Sayre) and naturalcomputationalism/pancomputationalism (nature computes its future statesfrom 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. Bck, and J.N. Kok, eds. 2012.Handbook of Natural Computing. Berlin Heidelberg: Springer.p. 40


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Life as cognition. Autopoiesis as self-reflective process

Living systems are cognitive systems, andliving as a processis a process of cognition . This statement is valid for allorganisms, with and without a nervous system.

Humberto Maturana, Biology of Cognition, 1970

Maturana and Varela (1980) define "autopoiesis" as follows: An autopoietic system is asystem 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 anetwork.


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Although a detailed physical account of the agents capacity toperform work cycles and so persist* in the world is central forunderstanding 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 physicalimplementation (Landauer, 1991), computation as thedynamics of information is the execution of physical laws .

*Contragrade processes (that require energy and do not spontaneously appear innature) become possible by connecting with the orthograde (spontaneous) processeswhich provide source of energy.

Living agents basic levels of cognition

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Kauffmans 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 thatboth our understanding of life as well as physics will change.

We witness the emergence of information physics (Goyal,2012) (Chiribella, G.; DAriano, G.M.; Perinotti, 2012) as a

possible reformulation of physics that may bring physics andlife/cognition closer to each other.

Living agents basic levels of cognition

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The origin of in first living agents is not wellresearched, as the idea still prevails that only humans possesscognition and knowledge.However, there are different types of and we have

good reasons to ascribe simpler kinds of to otherliving beings.Bacteria collectivelycollects latent information from theenvironment 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 beargued to possess simple forms of cognition.

Levels of organization of life/cognition

45Ben-Jacob, E. (2009). Learning from Bacteria about Natural Information Processing. Annals of the New York Academy of

Sciences , 1178 , 78

90.


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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, 3rd ed


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Evolution of the Nervous System

Figure 9-1: Evolution of the nervous system


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p. 48http://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 informationprocessing

Modular and hierarchicallymodular organization ofbrain networksD. Meunie, R. Lambiotteand E. T. BullmoreFrontiers of Neuroscience

http://www.neuroinformatics2013.org Neuroinformatics
http://www.frontiersin.org/neuroscience/10.3389/fnins.2010.00200/fullhttp://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.neuroinformatics2013.org/http://www.neuroinformatics2013.org/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/http://www.frontiersin.org/neuroscience/10.3389/fnins.2010.00200/full


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Cognitive computing - Computation as cognition

Acognitive computer is a proposed computational devicewith a non-Von Neumann architecture that implementsHebbian learning. Instead of being programmable in atraditional sense, such a device learns by experience through

an input device that are aggregated within a computationalconvolution or neural network architecture consisting ofweights within a parallel memory system.

Example of such devices developed in 2012 under the DarpaSyNAPSEprogram at IBM directed by Dharmendra Modha.

http://en.wikipedia.org/wiki/Cognitive_computerModha

p. 49
http://en.wikipedia.org/wiki/Cognitive_computerModhahttp://en.wikipedia.org/wiki/Cognitive_computerModha


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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 acrosscomputer science, information science, cognitive science,brain science, intelligence science, knowledge scienceand cognitive linguistics, which investigates into theinternal information processing mechanisms and

processes of the brain, the underlying abstractintelligence theories and denotational mathematics, andtheir engineering applications in cognitive computing andcomputational intelligence.

Cognitive Computing (CC ) is a novel paradigm ofintelligent computing theories and methodologies basedon CI that implements computational intelligence byautonomous inferences and perceptions mimicking themechanisms of the brain.

http://www.ucalgary.ca/icic/

The International Institute of Cognitive Informatics and Cognitive Computing

(ICIC)


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An Example: Cognitive Computing at IBM


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Design and Construction of a Brain-LikeComputer

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A New Class of Frequency-FractalComputing Using WirelessCommunication in aSupramolecular Organic,Inorganic SystemSubrata Ghosh, Krishna Aswani,Surabhi Singh, Satyajit Sahu,Daisuke Fujita and AnirbanBandyopadhyay *

Information 2014 , 5, 28-100;doi:10.3390/info5010028

http://www.mdpi.com/2078-2489/5/1/28
http://www.mdpi.com/2078-2489/5/1/28http://www.mdpi.com/2078-2489/5/1/28http://www.mdpi.com/2078-2489/5/1/28http://www.mdpi.com/2078-2489/5/1/28


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Connecting informational structures andprocesses from quantum physics to living

organisms and societies

Nature is described as a complex informational structure for acognizing agent.

Information is the difference in one information structure thatmakes a difference in another information structure.

Computation is information dynamics (informationprocessing) constrained and governed by the laws of physicson the fundamental level.

p. 53


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Computing nature

The basic idea of computing nature is that all processes taking placein physical world can be described as computational processes fromthe world of quantum mechanics to living organisms, their societiesand ecologies. Emphasis is on regularities and typical behaviors.

Even though we all have our subjective reasons why we move andhow we do that, from the bird-eye-view movements of inhabitants ina city show striking regularities.In order to understand big picture and behavior of societies, we takecomputational approach based on data and information.

See the work of Albert-Lszl Barabsi who studies networks ondifferent scales:http://www.barabasilab.com/pubs-talks.php
http://www.barabasilab.com/pubs-talks.phphttp://www.barabasilab.com/pubs-talks.phphttp://www.barabasilab.com/pubs-talks.phphttp://www.barabasilab.com/pubs-talks.php


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We focus on cognition and propose the common frameworkfor understanding anthropogenic, biogenic and abioticcognition.

Cognition for biological system is synonymous with life.Cognition as a process can only be understood in the light ofevolution.

Within the framework of info-computationalism, reality for anagent is an informational structure with computationaldynamics. On different levels of organization, different kindsof cognition operate from cellular level to organismic andsocial cognition.

Conclusion: Modeling Life as Cognitive Info-computation by ConnectingAnthropogenic with Biogenic and Abiotic Cognition

p. 55


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RELATED BOOKS

p. 56


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p. 57

A computable universe


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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


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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 NaturalEnvironments

Andre 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


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Special Issue of the Journal InformationInformation and Energy/Matter"

60

Vlatko Vedral: Information and Physics

Philip Goyal: Information Physics Towards a New Conception of PhysicalReality

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 MeaningJoseph E. Brenner: On Representation in Information Theory


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Special Issue of the Journal InformationInformation and Energy/Matter"

61

Makoto Yoshitake and Yasufumi Saruwatari: Extensional Information Articulationfrom the Universe

Christopher D. Fiorillo: Beyond Bayes: On the Need for a Unified and JaynesianDefinition of Probability and Information within Neuroscience

William A. Phillips: Self-Organized Complexity and Coherent Infomax from theViewpoint of Jaynes s Probability Theory

Hector Zenil: Information Theory and Computational Thermodynamics: Lessonsfor Biology from PhysicsJoseph E. Brenner: On Representation in Information Theory

Gordana Dodig Crnkovic, Editorial: Information and Energy/Matter


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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 NatureEditor(s): Gordana Dodig Crnkovic and

Raffaela Giovagnoli, Springer, 2013

Information and computation


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Brier Sren: 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 Mller 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 Approachp. 63

Information and computationGordana Dodig-Crnkovic and Mark Burgin,World Scientific Publishing Co. 2011

Information and computation
https://portal.mdh.se/personal/webdisk/base/v-prostberget/Projects/idt-htdocs/idt/ECAP-2005/INFOCOMPBOOK/BarryCooper-Descartes.pdfhttps://portal.mdh.se/personal/webdisk/base/v-prostberget/Projects/idt-htdocs/idt/ECAP-2005/INFOCOMPBOOK/BarryCooper-Descartes.pdf


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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 computationGordana Dodig-Crnkovic and Mark Burgin, World ScientificPublishing Co. Series in Information Studies, 2011


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Computing natureGordana 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 NaturalComputation

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 ModellingDynamics of Information

p. 65


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Larry Bull, Julian Holley, Ben De Lacy Costello and Andrew Adamatzky:Toward Turing s

A-type Unorganised Machines in an Unconventional Substrate: A DynamicRepresentation In Compartmentalised Excitable Chemical Media

Francisco Hernndez-Quiroz and Pablo Padilla:Some Constraints On The Physical

Realizability Of A Mathematical Construction

Mark Burgin and Gordana Dodig Crnkovic:From the Closed Classical AlgorithmicUniverse to an Open World of Algorithmic Constellations

p. 66

Computing natureGordana Dodig-Crnkovic and Raffaela Giovagnoli,

Springer SAPERE book series, 2013

b d b k


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p. 67

Two brand new books

2013

On the topic of life,computation, evolution &cognition.Written by a computer scientist.

T b d b k


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p. 68

Two brand new books

2014

On the topic of on the topicof (physical) computation &cognition.Written by a philosopher.

N i l di


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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, complexityemerging from repeating very simple rules

See alsohttp://www.youtube.com/watch?v=_eC14GonZnU

A New Kind of Science - Stephen Wolfram

Books in the New Computational Paradigm

A N P di f C i


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A New Paradigm of Computing Interactive Computing

Interactive Computation: the New ParadigmSpringer-Verlag in September 2006

Dina Goldin, Scott Smolka, Peter Wegner, eds.

Dina Goldin, Peter WegnerThe Interactive Nature of Computing :Refuting the Strong Church - Turing ThesisMinds and MachinesVolume 18 , Issue 1 (March 2008) p 17 - 38http://www.cs.brown.edu/people/pw/strong-cct.pdf

Biology as Reactivityhttp://research.microsoft.com/pubs/144550/CACM_11.pdf

p. 70

S lf dif i S t i Bi l d


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p. 71

Self-modifying Systems in Biology andCognitive 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


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Programming the Universe: AQuantum Computer Scientist

Takes on the Cosmos

by Seth Lloyd

p. 72

The Universe as quantum information


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p. 73

The Universe as quantum information

Decoding Reality

By Valtko Vedral

Reality = Information

Under Google books there are partsof this book available.


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Self-Organization and Selection in Evolution

p. 74

Stuart Kauffman presents a brilliant newparadigm for evolutionary biology, one thatextends the basic concepts of Darwinianevolution to accommodate recent findings andperspectives from the fields of biology, physics,chemistry and mathematics. The book drives tothe heart of the exciting debate on the origins oflife and maintenance of order in complexbiological systems.

It focuses on the concept of self-organization:the spontaneous emergence of order widelyobserved throughout nature. Kauffman hereargues that self-organization plays an importantrole in the emergence of life itself and may playas fundamental a role in shaping life'ssubsequent evolution as does the Darwinianprocess of natural selection.

http://books.google.se/books/about/The_Origins_of_Order.html?id=lZcSpRJz0dgC&redir_esc=y
http://books.google.se/books/about/The_Origins_of_Order.html?id=lZcSpRJz0dgC&redir_esc=yhttp://books.google.se/books/about/The_Origins_of_Order.html?id=lZcSpRJz0dgC&redir_esc=y


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The relationship between mind and matter

p. 75

Incomplete Nature. How mind emerged

from matter

by Terrence Deacon


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We can only see a short distance ahead, but we can seeplenty 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

Let me finish by Turing quote

p. 76
http://cogprints.org/499/0/turing.htmlhttp://cogprints.org/499/0/turing.html


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Based on the following articles

Dodig-Crnkovic G. and Giovagnoli R. (Eds), Computing Nature A Network of Networksof Concurrent Information Processes, In: COMPUTING NATURE, (book) Springer,Heidelberg, SAPERE book series, pp. 1-22, May 2013.http://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 2010Beijing 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_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. 77http://www.idt.mdh.se/~gdc/work/publications.html
http://arxiv.org/abs/1210.7784http://www.mrtc.mdh.se/~gdc/work/InformationDynamics-2011-05-30-Rev1.pdfhttp://www.mdpi.com/journal/information/special_issues/selectedpap_beijinghttp://www.mdpi.com/2078-2489/2/3/460/http://livingbooksaboutlife.org/books/Energy_Connectionshttp://www.idt.mdh.se/~gdc/work/publications.htmlhttp://www.idt.mdh.se/~gdc/work/publications.htmlhttp://livingbooksaboutlife.org/books/Energy_Connectionshttp://www.mdpi.com/2078-2489/2/3/460/http://www.mdpi.com/2078-2489/2/3/460/http://www.mdpi.com/2078-2489/2/3/460/http://www.mdpi.com/journal/information/special_issues/selectedpap_beijinghttp://www.mrtc.mdh.se/~gdc/work/InformationDynamics-2011-05-30-Rev1.pdfhttp://arxiv.org/abs/1210.7784


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THANKS TO

Prof. Francisco Hernndez Quiroz for invitation to UNAM

Alberto Hernndez Espinosa for organizing my visit to Infotec

Also

Hector ZenilCarlos Gershenson

& Tom Froesefor their work that convinced me that UNAM is anextraordinary place!

p. 78


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A Mathematical Model for Info-computationalism- A. C. Ehresmann

p. 79

Open peer commentary on the articleInfo-computational Constructivism andCognition by Gordana Dodig-Crnkovic.

Ehresmann proposes a mathematicalapproach to the framework developed byDodig-Crnkovic. Based on the Property ofnatural computation, called themultiplicity principledevelopment ofincreasingly complex cognitive processesand knowledge is described.Local dynamics are classicallycomputable , a consequence of the MP isthat the global dynamics is not, thusraising the problem of developing moreelaborate computation models.


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An Info-Computational Model for (Neuro-)cognitive Systems Capable of Creativity -

Andre C. Ehresmann

The model, based on a dynamic Category Theory, accounting forthe functioning of the neural,cognitive and mental systems atdifferent levels of description andacross different timescales.

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Life Cognition Infocomputation, CiE 20140623

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