Computability in Europe 2014: Language, Life, Limits. June 23-27, Budapest
Modeling Life as
Cognitive
Info-computation
Gordana Dodig Crnkovic
Professor of Computer Science
School of Innovation, Design and
Engineering
Mälardalen University, Sweden
http://www.idt.mdh.se/~gdc/
Mälardalen University Sweden
12,000 students and around 900 employees, of which 67 are professors
What is Cognition?
After half a century of research in cognitive science, cognition
still lacks a commonly accepted definition (Lyon, 2005).
Textbook description of cognition:
“all the processes by which sensory input is transformed, reduced,
elaborated, stored, recovered and used” (Neisser, 1967)
is so broad that it includes present day robots.
On the other hand, the Oxford dictionary definition:
“the mental action or process of acquiring knowledge and
understanding through thought, experience, and the senses”
applies only to humans.
*Mental = relating to the mind. Mind is set of processes in which consciousness,
perception, affectivity, judgment, thinking, and will are based.
p. 3
Cognitive science
Biology
/embodiment/embedde
ess
Biology
/embodiment/embeddedness
http://cacm.acm.org/magazines/2011/8/114944cognitive-computing/fulltext
Biology
/embodiment/embedded
Biology /embodiment/embeddedness (situatedness)
http://en.wikipedia.org/wiki/Cognitive_science
p. 4
Cognition, different levels of understanding
●Traditional anthropogenic approach to cognition* – only
humans are cognitive agents
●Biogenic approaches* – cognition is ability of all living
organisms, no matter how “primitive” – goes a level below the
complexity of human language – to complex systems chemical
signaling and regulation processes. (Maturana & Varela, 1980;
Maturana, 1970), argued that cognition and life are identical
processes.
●New sub-biotic approaches to cognition assume that it is
possible to construct cognitive agents starting from abiotic
systems – a level below biogenic cognition.
The question is if abiotic systems can be considered cognitive,
in what sense and on which level.
* (Lyon, 2005)
p. 5
Anthropogenic Cognition vs. Anthropogenic
Intelligence
● (Anthropogenic) Cognition is the PROCESS by which
humans acquire, integrate and generate knowledge. It is
the result of attention, perception, memory, and executive
functions of learning and behavior generation (information
integration and transformation of perception into higher
order symbols; comparison of incoming information with
the information stored in memory together with value
system and biological drives)
● (Anthropogenic) Intelligence is the ABILITY to understand
and reason upon (i.e. structure and interrelate and operate
upon) what is perceived, memorized and learned.
● Intelligence as ABILITY is based on cognition as PROCESS.
p. 6
Similarly, Biogenic and Abiotic Cognition vs.
Biogenic and Abiotic (Artifactual) Intelligence
● (Biogenic and Abiotic) Cognition is the PROCESS by which
simple living organisms acquire, integrate and generate
<knowledge>. It is the result of attention, perception,
memory, and executive functions of learning and behavior
generation.
● (Biogenic and Abiotic) Intelligence is the ABILITY to
structure, interrelate and operate upon information that is
perceived, memorized and learned.
● Intelligence as ABILITY is based on cognition as PROCESS.
p. 7
Connecting Anthropogenic with Biogenic and
Abiotic Cognition
We focus on cognition and propose the common framework
for understanding Anthropogenic, Biogenic and Abiotic
Cognition.
We argue that (as in the rest of biology) – nothing makes
sense except for in the light of evolution (Dobzhansky, 1973)
and the cognition as a process can only be understood in the
light of evolution.
Regarding abiotic systems we will compare their “cognitive
behavior” with living organisms, and draw conclusions.
p. 8
Living as a process is a process of cognition
● “A cognitive system is a system whose organization defines a
domain of interactions in which it can act with relevance to
the maintenance of itself, and the process of cognition is the
actual (inductive) acting or behaving in this domain. Living
systems are cognitive systems, and living as a process is a
process of cognition. This statement is valid for all organisms,
with and without a nervous system.” (Maturana, 1970)
● In 1991, Kampis proposed a unified model of computation as
the mechanism underlying biological processes through
“self-generation of information by non-trivial change (selfmodification) of systems” (Kampis, 1991. Self-Modifying
Systems in Biology and Cognitive Science: A New Framework for
Dynamics, Information and Complexity).
9
Information, computation, cognition
Agent-centered Hierarchies of Levels
In this lecture I will present a unified framework for modeling of information,
computation and cognition
from an agents perspective.
information
computation
cognition
Fruit fly brain neurons
Fruit fly larva
p. 10
Fruit fly brain micrograph
http://www.sciencedirect.com/science/article/pii/S0378437104014839
Starting from anthropogenic perspective: The
brain development - Cognition as biological phenomenon
“The brain development may be carried out
based on the basic body-organization-blueprints
that are specific to an animal species depending
on their strategy to survive in an environment.
To understand how our brains are established in
the course of evolution, we have been
conducting a comparison of the structure and
function of the gene that are essential for
establishing body organization and brain
development in a wide rage of animals with
nervous system.”
http://lcn.brain.riken.jp/tool_kit_evolution.htm
p. 11
Wonders of evolution – the smallest insect
with brain, smaller than an amoeba
Size of the smallest insect and two
protozoans in comparison.
(A)Megaphragma mymaripenne.
(B)Paramecium caudatum.
(C)Amoeba proteus.
Scale bar for A–C is 200 μm.
B and C are made up of a single cell,
A the wasp complete with eyes, brain, wings,
muscles, guts – is actually smaller.
This wasp is the third smallest insect alive.
the smallest nervous systems of any insect,
consisting of just 7,400 neurons.
Housefly has 340,000
Honeybee has 850,000.
95% of the wasps’s neurons have no nucleus.
http://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
p. 12
Natural information processing
Human connectome
http://outlook.wustl.edu/2013/jun/human-connectome-project
Henry Markram (2012) The Human Brain Project, Scientific American 306, 50 – 55
Information, computation,
cognition Agent-centered Hierarchy of Levels
http://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientific
american0612-50.pdf The Human Brain Project
p. 13
Current brain research initiatives
The Human Brain Project (HBP) is a large scientific research
project, directed by the École polytechnique fédérale de
Lausanne and largely funded by the European Union, which
aims to simulate the complete human brain on
supercomputers to better understand how it functions.
The BRAIN Initiative (Brain Research through Advancing
Innovative Neurotechnologies, also referred to as the Brain
Activity Map Project) is a proposed collaborative research
initiative announced by the Obama administration on April 2,
2013, with the goal of mapping the activity of every neuron in
the human brain. Based upon the Human Genome Project, the
initiative has been projected to cost more than $300 million
per year for ten years.
Source: Wikipedia
14
Current brain research initiatives
The Allen Institute conducting and completing large-scale
brain mapping projects for the last 10 years. In early 2012
launched three additional major research initiatives to drive
critical advances in understanding how the brain works and
develops.
● Neural Coding (understanding how information is encoded and
decoded in the mammalian brain)
● Molecular Networks (understanding how information is
encoded and decoded within a cell)
● Cell Types (large-scale descriptive resources of human and
mouse brain cell types at molecular, morphological and
connectional levels)
● Atlasing (collection of online public resources integrating
extensive genomic and neuroanatomic data)
http://www.alleninstitute.org/science/research_programs/index.html
15
The Strategy of Info-Computational
Approach
● Even though anthropogenic approach to cognition is the
oldest and by far the most dominant one, it is the most
difficult approach to the most complex problem –
embodied human brain.
● The study of biogenic and abiotic cognition can help us
trace evolutionary roots of cognitive capacities in living
organisms (biogenic) and construct (abiotic) artifact with
cognitive and intelligent behavior (cognitive computing and
cognitive robotics).
● Therefore we start with simplest living systems such as
bacteria to try to understand the basis of their cognitive
behavior in informational structures and their dynamics
(computational processes).
16
Information, computation, cognition.
Agency-based Hierarchies of Levels
Introducing generalized concepts of information and computation.
Short summary of the argument:
1.
Information presents a structure consisting of differences
in one system that cause the differences in another
system. In other words, information is <observer>*relative.
2.
Computation is information processing (dynamics of
information). It is physical process of morphological
change in the informational structure (physical
implementation of information, as there is no information
without physical implementation.)
*<> brackets indicate that the term is used in a broader sense than usually.
p. 17
Information, computation, cognition.
Agency-based Hierarchies of Levels
3.
Both information and computation appear on many
different levels of
organisation/abstraction/resolution/granularity of
matter/energy in space/time.
4.
Of all agents (entities capable of acting on their own
behalf) only living agents have the ability to actively
make choices so to increase the probability of their own
continuing existence. This ability of living agents to act
autonomously on its own behalf is based on the use of
energy/matter and information from the environment.
p. 18
Information, computation, cognition.
Agency-based Hierarchies of Levels
5. Cognition consists of all (info-computational) processes
necessary to keep living agent’s organizational integrity on
all different levels of its existence.
Cognition = info-computation
6. Cognition is equivalent with the (process of) life.*
Its complexity increases with evolution.
This complexification is a result of morphological
computation.
* Maturana, H. & Varela, F., 1980. Autopoiesis and cognition: the realization of the living, Dordrecht
Holland: D. Reidel Pub. Co.
p. 19
Information as a fabric of reality
“Information is the difference that makes a difference. “
Gregory Bateson
It is the difference in the world that makes the difference for
an agent. Here the world includes agents themselves too.
“Information expresses the fact that a system is in a certain
configuration that is correlated to the configuration of
another system. Any physical system may contain information
about another physical system.” Carl Hewitt
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.
Information structures as a fabric of reality
(thus structured/organized data) for an agent
Informational structural realism (Floridi, Sayre) argues that
information (for an agent) constitutes the fabric of reality:
Reality consists of informational structures organized on
different levels of abstraction/resolution.
See also:
Van Benthem and Adriaans (2008) Philosophy of Information, In: Handbook of the
philosophy of science series. http://www.illc.uva.nl/HPI
Ladyman J. and Ross D., with Spurrett D. and Collier J. (2007)
Every Thing Must Go: Metaphysics Naturalized, Oxford UP
Floridi, L. (2008) A defence of informational structural realism, Synthese 161, 219253.
Sayre, K. M. (1976) Cybernetics and the Philosophy of Mind, Routledge & Kegan
Paul, London.
The relational definition of information
Combining definitions of Bateson:
“ Information is a difference that makes a difference.”
(Bateson, 1972)
and Hewitt:
”Information expresses the fact that a system is in a certain
configuration that is correlated to the configuration of another
system. Any physical system may contain information about
another physical system.” (Hewitt, 2007), we get:
Information is defined as the difference in one physical system
that makes the difference in another physical system.
22
Structure vs. process
For all living agents, information is the fabric of reality.
But: the knowledge of structures is only half a story.
The other half are changes, processes – information dynamics.
(In classical formulation: being and becoming.)
Information processing will be taken as the most general
definition of computation.
This definition of computation has a profound consequence – if
computation is the dynamics of informational structures of the
universe, the dynamics of the universe is a network of
computational processes (natural computationalism).
Dodig-Crnkovic, G., Dynamics of Information as Natural Computation,
Information 2011, 2(3), 460-477; Selected Papers from FIS 2010 Beijing,
2011.
p. 23
Reality for an agent - informational structure
with computational dynamics
Information is defined as the difference in one physical system
that makes the difference in another physical system.
This reflects the relational character of information and thus
agent-dependency which calls for agent-based or actor models.
As a synthesis of informational structural realism and natural
computationalism, I propose info-computational structuralism
that builds on two basic concepts: information (as a structure)
and computation (as a dynamics of an informational structure)
(Dodig-Crnkovic, 2011).
(Dodig-Crnkovic & Giovagnoli, 2013) Information and computation are two basic
and inseparable elements necessary for naturalizing <cognition>. (DodigCrnkovic, 2009)
24
Computational modeling in cognitive science
● Symbolic modeling evolved from the computer science
paradigms using the technologies of Knowledge-based systems "Good Old-Fashioned Artificial Intelligence" (GOFAI). Used in
expert systems and cognitive decision making, and extended to
socio-cognitive approach.
● Subsymbolic modeling includes Connectionist/neural network
models.
p. 25
Computational modeling in cognitive science
● Dynamical systems theory closely related to ideas about the
embodiment of mind and the environmental situatedness of
human cognition based on physiological and environmental
events. The most important here is the dimension of time.
● Neural-symbolic integration techniques putting symbolic models
and connectionist models into correspondence.
● Bayesian models of brain function which assume that the
nervous system maintains internal probabilistic models that are
updated by neural processing of sensory information using
methods approximating those of Bayesian probability.
p. 26
It is important to notice:
Computationalism is not what it used to be…
… that is, the thesis that persons are Turing machines.
Turing Machine is a model of computation equivalent to
algorithm and it may be used for description of different
processes in living organisms.
We need computational models for the basic characteristics of
life as the ability to differentiate and synthesize information,
make a choice, to adapt, evolve and learn in an unpredictable
world. That requires computational mechanisms and models
which are not mechanistic and predefined as Turing machine.*
* We need learning such as PAC Probably Approximately Correct – Leslie Valiant
p. 27
Computationalism is not what it used to be …
… that is the thesis that persons are Turing machines
Computational approaches that are capable of modelling
adaptation, evolution and learning are found in the field of
natural computation and computing nature.
Cognitive computing and cognitive robotics are the attempts
to construct abiotic systems exhibiting cognitive
characteristics.
It is argued that cognition comes in degrees, thus it is
meaningful to talk about cognitive capabilities of artifacts,
even though those are not meant to assure continuing
existence, which was the evolutionary role of cognition in
biotic systems.
p. 28
Turing computation: “Turing on Super-Turing
and adaptivity” according to Siegelmann
“Biological processes are often compared to computation and
modeled on the Universal Turing Machine. While many
systems or aspects of systems can be well described in this
manner, Turing computation can only compute what it has
been programmed for. (…)
Yet, adaptation, choice and learning are all hallmarks of
living organisms. This suggests that there must be a different
form of computation capable of this sort of calculation. (…)
Super-Turing model is both capable of modeling adaptive
computation, and furthermore, a possible answer to the
computational model searched for by Turing himself.”
Hava T. Siegelmann, Turing on Super-Turing and adaptivity, Progress in
Biophysics and Molecular Biology,
http://www.sciencedirect.com/science/article/pii/S0079610713000278
p. 29
Actor model of concurrent distributed
computation
“In the Actor Model [Hewitt, Bishop and
Steiger 1973; Hewitt 2010], computation
is conceived as distributed in space,
where
computational
devices
communicate asynchronously and the
entire computation is not in any welldefined state.
(An Actor can have information about other Actors
that it has received in a message about what it was
like when the message was sent.) Turing's Model is a
special case of the Actor Model.” (Hewitt, 2012)
Hewitt’s “computational devices” are conceived as computational agents –
informational structures capable of acting on their own behalf.
p. 30
Actor model of concurrent distributed
computation
Actors are the universal primitives of concurrent distributed
digital computation. In response to a message that it receives,
an Actor can make local <decisions>, create more Actors, send
more messages, and designate how to respond to the next
message received.
For Hewitt, Actors become Agents only when they are able to
process expressions for commitments including the following:
Contracts, Announcements, Beliefs, Goals, Intentions, Plans,
Policies, Procedures, Requests, Queries.
In other words, Hewitt’s Agents are human-like or if we
broadly interpret the above capacities, life-like Actors.
p. 31
Actor model of concurrent distributed
computation
Unlike other models of
computation that are based
on mathematical logic, set
theory, algebra, etc. the
Actor model is based on
physics, especially quantum
physics
and
relativistic
physics. (Hewitt, 2006)
Summary of interactions between particles described by the Standard
Model.
http://en.wikipedia.org/wiki/Standard_Model
p. 32
Computing nature and
nature inspired computation
If it looks like a duck,
if it walks like a duck
and it quacks like a duck,
is it a duck?
(If it looks like computation is it
computation?)
Peter J. Denning. 2007. Computing is a natural science.
Commun. ACM 50, 7 (July 2007), 13-18. DOI=10.1145/1272516.1272529
http://doi.acm.org/10.1145/1272516.1272529
Computing cells: self-generating systems
Complex biological systems must be modeled as selfreferential, self-organizing "component-systems"
(George Kampis) which are self-generating and whose
behavior, though computational in a general sense, goes
far beyond Turing machine model.
“a component system is a computer which, when executing its operations
(software) builds a new hardware.... [W]e have a computer that re-wires itself in a
hardware-software interplay: the hardware defines the software and the software
defines new hardware. Then the circle starts again.” Kampis (1991) p. 223
Kampis (1991) Self-Modifying Systems in Biology and Cognitive Science. A New Framework For
Dynamics, Information, and Complexity, Pergamon Press
Dodig Crnkovic, G. (2011). Significance of Models of Computation from Turing Model to Natural
Computation. Minds and Machines, (R. Turner and A. Eden guest eds.) Volume 21, Issue 2, p.301.
Computation is implemented at different
levels of resolution – Computing architecture
Some layered computational architectures
p. 35
Computation as information processing.
Data to information via computation
Computational processes on information structures
Elements of information
processing in an information
system
p. 36
Cognitive information processing is not
what it used to be …
Cognitive Information Processing theory of learning according to
(Gagné, 1985): This is an old and simplistic idea of cognition as
information processing. Missing in this scheme are feedback loops
that are absolutely essential for cognition and learning. Also missing
is information integration from different sensors and couplings to
actuators. Memory is not a passive storage but an active
ingredient in perception, that is both used for recognition and
anticipation.
Cognitive / Information Processing Theory of Learning according to (Gagné, 1985)
p. 37
Multisensori information integration
Information integration is critical for the brain to
interact effectively with our multisensory
environment. The human brain integrates
information from multiple senses with prior
knowledge to form a coherent and more reliable
percept of its environment. (learning)
Within the cortical hierarchy, multisensory
perception emerges in an interactive process with
top-down prior information constraining the
interpretation of the incoming sensory signals.
Marcin Schröder in the book Computing Nature
adresses the Dualism of Selective and Structural
Information, describing information integration.
http://www.birmingham.ac.uk/researc
h/activity/behavioural-neuro/compcog-neuro/index.aspx
38
Cognition: Agency-based hierarchies of levels.
World as information for an agent
Potential information
Cognition
Actual information for an agent
From: http://www.alexeikurakin.org
http://www.tbiomed.com/content/8/1/4 scale-invariance of self-organizational dynamics of
energy/matter at all levels of organizational hierarchy
39
Agency-based hierarchies of levels.
World as information for an agent
Actual Information C-elegans
Potential information
Outside reality for C-elegans
Interaction interface for C-elegans
Cognition
C. Elegans has 302 neurons (humans have 100 billion). The pattern of
connections between neurons has been mapped out decades ago using
electron microscopy, but knowledge of the connections is not sufficient to
understand (or replicate) the information processor they represent, for
some connections are inhibitory while others are excitatory.
http://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.html
40
Reality for an agent –
an observer-dependent reality
Reality for an agent is an informational structure with which
agent interacts. As systems able to act on their own behalf
and make sense (use) of information, cognitive agents are of
special interest with respect to <knowledge>* generation.
This relates to the idea of participatory universe, (Wheeler,
1990) “it from bit” as well as to endophysics or “physics from
within” where an observer is being within the universe, unlike
the “god-eye-perspective” from the outside of the universe.
(Rössler, 1998)
*<knowledge> for a very simple agent can be the ability to optimize gains and minimize
risks.
(Popper, 1999) p. 61 ascribes the ability to know to all living: ”Obviously, in the biological
and evolutionary sense in which I speak of knowledge, not only animals and men have
expectations and therefore (unconscious) knowledge, but also plants; and, indeed, all
organisms.”
41
An illustration: Agent-dependent multiscale
modeling of complex chemical system
Observer-centric model – enhanced
resolution where observation is
made – where chemical reaction
takes place
The Nobel Prize in Chemistry 2013 “for the development of
multiscale models for complex chemical systems” ...
Karplus, Levitt and Warshel managed to make Newton's
classical physics work side-by-side with the fundamentally
different quantum physics. The strength of classical physics was
that calculations were simple and could be used to model large
molecules but no way to simulate chemical reactions for which
chemists use quantum physics. But such calculations require
enormous computing power.
Nobel Laureates in chemistry devised methods that use both
classical and quantum physics.
In simulations of how a drug couples to its target protein in the
body, the computer performs quantum theoretical calculations on
those atoms in the target protein that interact with the drug. The
rest of the large protein is simulated using less demanding
classical physics.
Today the computer is just as important a tool for chemists as the
test tube. Simulations are so realistic that they predict the outcome
of traditional experiments.
http://www.nobelprize.org/nobel_prizes/chemistry/laureates/2013/advanced-chemistryprize2013.pdf
Info-computational framework and levels
The question of levels of organization/levels of abstraction for an agent is
analyzed within the framework of info-computational constructivism, with
natural phenomena modeled as computational processes on informational
structures.
Info-computationalism is a synthesis of informational structuralism (nature
is an informational structure for an agent) (Floridi, Sayre) and natural
computationalism/pancomputationalism (nature computes its future states
from its earlier states) (Zuse, Fredkin, Wolfram, Chaitin, Lloyd).
Two central books presenting the diversity of research on information and computation:
Adriaans P. and van Benthem J. eds. 2008. Philosophy of Information (Handbook of the Philosophy of Science)
North Holland.
Rozenberg, G., T. Bäck, and J.N. Kok, eds. 2012. Handbook of Natural Computing. Berlin Heidelberg: Springer.
p. 43
Life as cognition. Autopoiesis as selfreflective process
”Living systems are cognitive systems, and living as a process
is a process of cognition. This statement is valid for all
organisms, with and without a nervous system.”
Humberto Maturana, Biology of Cognition, 1970
Maturana and Varela (1980) define "autopoiesis" as follows: An autopoietic system is a
system organized (defined as a unity) as a network of processes of production
(transformation and destruction) of components that produces the components, such that:
(i) through their interactions and transformations continuously they regenerate and
realize the network of processes (relations) that produced them; and
(ii) they constitute it (the system) as a concrete unity in the space in which they (the
components) exist by specifying the topological domain of its realization as such a
network.
44
Living agents – basic levels of cognition
A living agent is an entity acting on its own behalf, with
autopoietic properties that is capable of undergoing at least
one thermodynamic work cycle. (Kauffman, 2000)
This definition differs from the common belief that (living)
agency requires beliefs and desires, unless we ascribe some
primitive form of <belief> and <desire> even to a very simple
living agents such as bacteria. The fact is that they act on
some kind of <anticipation> and according to some
<preferences> which might be automatic in a sense that they
directly derive from the organisms morphology. Even the
simplest living beings act on their own behalf.
45
Living agents – basic levels of cognition
Although a detailed physical account of the agents capacity to
perform work cycles and so persist* in the world is central for
understanding of life/cognition, as (Kauffman, 2000) (Deacon,
2007) have argued in detail, present argument is primarily
focused on the info-computational aspects of life.
Given that there is no information without physical
implementation (Landauer, 1991), computation as the
dynamics of information is the execution of physical laws.
*Contragrade processes (that require energy and do not spontaneously appear in
nature) become possible by connecting with the orthograde (spontaneous) processes
which provide source of energy.
46
Living agents – basic levels of cognition
Kauffman’s concept of agency (also adopted by Deacon)
suggests the possibility that life can be derived from physics.
That is not the same as to claim that life can be reduced to
physics that is obviously false.
However, in deriving life from physics one may expect that
both our understanding of life as well as physics will change.
We witness the emergence of information physics (Goyal,
2012) (Chiribella, G.; D’Ariano, G.M.; Perinotti, 2012) as a
possible reformulation of physics that may bring physics and
life/cognition closer to each other.
47
Levels of organization of life/cognition
The origin of <cognition> in first living agents is not well
researched, as the idea still prevails that only humans possess
cognition and knowledge.
However, there are different types of <cognition> and we have
good reasons to ascribe simpler kinds of <cognition> to other
living beings.
Bacteria collectively “collects latent information from the
environment and from other organisms, process the information,
develop common knowledge, and thus learn from past
experience” (Ben-Jacob, 2009)
Plants can be said to possess memory (in their bodily structures)
and ability to learn (adapt, change their morphology) and can be
argued to possess simple forms of cognition.
Ben-Jacob, E. (2009). Learning from Bacteria about Natural Information Processing. Annals of the New York Academy of
Sciences, 1178, 78–90.
48
Evolution of the Nervous System
●
●
●
●
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
Dee Unglaub Silverthorn, Human Physiology- an Integrated Approach, 3rd ed
Evolution of the Nervous System
Figure 9-1: Evolution of the nervous system
Cognition as computation – information
processing
http://www.neuroinformatics2013.org
Neuroinformatics
Modular and hierarchically
modular organization of
brain networks
D. Meunie, R. Lambiotte
and E. T. Bullmore
Frontiers of Neuroscience
http://www.frontiersin.org/neuroscience/10.3389/fnins.2010.00200/full
http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/
p. 51
Cognitive computing - Computation as cognition
A cognitive computer is a proposed computational device
with a non-Von Neumann architecture that implements
Hebbian learning. Instead of being programmable in a
traditional sense, such a device learns by experience through
an input device that are aggregated within a computational
convolution or neural network architecture consisting of
weights within a parallel memory system.
Example of such devices developed in 2012 under the Darpa
SyNAPSE program at IBM directed by Dharmendra Modha.
http://en.wikipedia.org/wiki/Cognitive_computerModha
p. 52
An Example: Cognitive Computing at ICIC
The International Institute of Cognitive Informatics and Cognitive Computing
(ICIC)
Cognitive Informatics (CI) is a discipline across
computer science, information science, cognitive science,
brain science, intelligence science, knowledge science
and cognitive linguistics, which investigates into the
internal information processing mechanisms and
processes of the brain, the underlying abstract
intelligence theories and denotational mathematics, and
their engineering applications in cognitive computing and
computational intelligence.
Cognitive Computing (CC) is a novel paradigm of
intelligent computing theories and methodologies based
on CI that implements computational intelligence by
autonomous inferences and perceptions mimicking the
mechanisms of the brain.
http://www.ucalgary.ca/icic/
http://www.kurzweilai.net/ibm-unveils-cognitive-computing-chips-combining-digitalneurons-and-synapses
An Example: Cognitive Computing at IBM
54
Design and Construction of a Brain-Like
Computer
A New Class of Frequency-Fractal
Computing Using Wireless
Communication in a
Supramolecular Organic,
Inorganic System
Subrata Ghosh, Krishna Aswani,
Surabhi Singh, Satyajit Sahu,
Daisuke Fujita and Anirban
Bandyopadhyay *
Information 2014, 5, 28-100;
doi:10.3390/info5010028
http://www.mdpi.com/2078-2489/5/1/28
55
Connecting informational structures and
processes from quantum physics to living
organisms and societies
Nature is described as a complex informational structure for a
cognizing agent.
Information is the difference in one information structure that
makes a difference in another information structure.
Computation is information dynamics (information
processing) constrained and governed by the laws of physics
on the fundamental level.
p. 56
Computing nature
The basic idea of computing nature is that all processes taking place
in physical world can be described as computational processes – from
the world of quantum mechanics to living organisms, their societies
and ecologies. Emphasis is on regularities and typical behaviors.
Even though we all have our subjective reasons why we move and
how we do that, from the bird-eye-view movements of inhabitants in
a city show striking regularities.
In order to understand big picture and behavior of societies, we take
computational approach based on data and information.
See the work of Albert-László Barabási who studies networks on
different scales:
http://www.barabasilab.com/pubs-talks.php
A computable universe
p. 58
Special Issue of the Journal Entropy
"Selected Papers from Symposium on Natural/Unconventional
Computing and Its Philosophical Significance"
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, SelfReference
Gordana Dodig Crnkovic and Mark Burgin:
Complementarity of Axiomatics and Construction
59
Special Issue of the Journal Entropy
"Selected Papers from Symposium on Natural/Unconventional
Computing and Its Philosophical Significance"
Hector Zenil, Carlos Gershenson, James A. R. Marshall and David A.
Rosenblueth:
Life as Thermodynamic Evidence of Algorithmic Structure in Natural
Environments
Andrée C. Ehresmann: MENS, an Info-Computational Model for (Neuro)cognitive Systems Capable of Creativity
Gordana Dodig Crnkovic and Raffaela Giovagnoli, Editorial:
Natural/Unconventional Computing and Its Philosophical Significance
60
Special Issue of the Journal Information
“Information and Energy/Matter"
Vlatko Vedral: Information and Physics
Philip Goyal: Information Physics—Towards a New Conception of Physical
Reality
Chris Fields: If Physics Is an Information Science, What Is an Observer?
Gerhard Luhn: The Causal-Compositional Concept of Information Part I.
Elementary Theory: From Decompositional Physics to Compositional Information
Koichiro Matsuno and Stanley N. Salthe:
Chemical Affinity as Material Agency for Naturalizing Contextual Meaning
Joseph E. Brenner: On Representation in Information Theory
61
Special Issue of the Journal Information
“Information and Energy/Matter"
Makoto Yoshitake and Yasufumi Saruwatari: Extensional Information Articulation
from the Universe
Christopher D. Fiorillo: Beyond Bayes: On the Need for a Unified and Jaynesian
Definition of Probability and Information within Neuroscience
William A. Phillips: Self-Organized Complexity and Coherent Infomax from the
Viewpoint of Jaynes’s Probability Theory
Hector Zenil: Information Theory and Computational Thermodynamics: Lessons
for Biology from Physics
Joseph E. Brenner: On Representation in Information Theory
Gordana Dodig Crnkovic, Editorial: Information and Energy/Matter
62
Computing Nature
Computation, Information, Cognition
Information and Computation
Computing Nature
Editor(s): Gordana Dodig Crnkovic and Susan
Editor(s): Gordana Dodig Crnkovic and
Editor(s): Gordana Dodig Crnkovic and
Stuart, Cambridge Scholars Publishing, 2007
Mark Burgin, World Scientific, 2011
Raffaela Giovagnoli, Springer, 2013
p. 63
Information and computation
Gordana Dodig-Crnkovic and Mark Burgin,
World Scientific Publishing Co. 2011
Brier Søren: Cybersemiotics and the question of knowledge
Burgin Mark: Information Dynamics in a Categorical Setting
Chaitin Greg: Leibniz, Complexity & Incompleteness
Collier John: Information, Causation and Computation
Cooper Barry: From Descartes to Turing: The computational Content of Supervenience
Dodig Crnkovic Gordana and Müller Vincent: A Dialogue Concerning Two Possible World
Systems
Hofkirchner Wolfgang: Does Computing Embrace Self-Organisation?
Kreinovich Vladik & Araiza Roberto: Analysis of Information and Computation in Physics
Explains Cognitive Paradigms: from Full Cognition to Laplace Determinism to
Statistical Determinism to Modern Approach
p. 64
Information and computation
Gordana Dodig-Crnkovic and Mark Burgin, World Scientific
Publishing Co. Series in Information Studies, 2011
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. 65
Computing nature
Gordana Dodig-Crnkovic and Raffaela Giovagnoli,
Springer SAPERE book series, 2013
Barry Cooper: What Makes A Computation Unconventional?
Hector Zenil: Nature-like Computation and a Measure of Programmability
Gianfranco Basti: Intelligence And Reference. Formal Ontology Of The Natural
Computation
Ron Cottam, Willy Ranson and Roger Vounckx: A Framework for Computing Like Nature
Gordana Dodig Crnkovic: Alan Turing’s Legacy: Info-Computational Philosophy of Nature
Marcin J. Schroeder: Dualism of Selective and Structural Information in Modelling
Dynamics of Information
p. 66
Computing nature
Gordana Dodig-Crnkovic and Raffaela Giovagnoli,
Springer SAPERE book series, 2013
Larry Bull, Julian Holley, Ben De Lacy Costello and Andrew Adamatzky: Toward Turing’s
A-type Unorganised Machines in an Unconventional Substrate: A Dynamic
Representation In Compartmentalised Excitable Chemical Media
Francisco Hernández-Quiroz and Pablo Padilla: Some Constraints On The Physical
Realizability Of A Mathematical Construction
Mark Burgin and Gordana Dodig Crnkovic: From the Closed Classical Algorithmic
Universe to an Open World of Algorithmic Constellations
p. 67
Two brand new books
On the topic of life,
computation, evolution &
cognition.
Written by a computer scientist.
2013
p. 68
Two brand new books
On the topic of on the topic
of (physical) computation &
cognition.
Written by a philosopher.
2014
p. 69
New computational paradigm:
Generative computing – cellular automata
A New Kind of Science
Book available at:
http://www.wolframscience.com
Based on cellular automata, complexity
emerging from repeating very simple rules
See also
http://www.youtube.com/watch?v=_eC14GonZnU
A New Kind of Science - Stephen Wolfram
Books in the New Computational Paradigm
p. 70
A New Paradigm of Computing
– Interactive Computing
Interactive Computation: the New Paradigm
Springer-Verlag in September 2006
Dina Goldin, Scott Smolka, Peter Wegner, eds.
Dina Goldin, Peter Wegner
The Interactive Nature of Computing:
Refuting the Strong Church - Turing Thesis
Minds and Machines
Volume 18 , Issue 1 (March 2008) p 17 - 38
http://www.cs.brown.edu/people/pw/strong-cct.pdf
Biology as Reactivity
http://research.microsoft.com/pubs/144550/CACM_11.pdf
p. 71
Self-modifying Systems in Biology and
Cognitive Science
The topic of the book is the self-generation of
information by the self-modification of
systems. The author explains why biological
and cognitive processes exhibit identity
changes in the mathematical and logical
sense. This concept is the basis of a new
organizational principle which utilizes shifts of
the internal semantic relations in systems.
ftp://wwwc3.lanl.gov/pub/users/joslyn/kamp_rev.pdf
p. 72
The Universe as quantum information
Programming the Universe: A
Quantum Computer Scientist
Takes on the Cosmos
by Seth Lloyd
p. 73
The Universe as quantum information
Decoding Reality
By Valtko Vedral
Reality = Information
Under Google books there are parts
of this book available.
p. 74
Self-Organization and Selection in Evolution
Stuart Kauffman presents a brilliant new
paradigm for evolutionary biology, one that
extends the basic concepts of Darwinian
evolution to accommodate recent findings and
perspectives from the fields of biology, physics,
chemistry and mathematics. The book drives to
the heart of the exciting debate on the origins of
life and maintenance of order in complex
biological systems.
It focuses on the concept of self-organization:
the spontaneous emergence of order widely
observed throughout nature. Kauffman here
argues that self-organization plays an important
role in the emergence of life itself and may play
as fundamental a role in shaping life's
subsequent evolution as does the Darwinian
process of natural selection.
http://books.google.se/books/about/The_Origins_of_Order.html?id=lZcSpRJz0dgC&redir_esc=y
p. 75
The relationship between mind and matter
Incomplete Nature. How mind emerged
from matter
by Terrence Deacon
p. 76
Let me finish by Turing quote
“We can only see a short distance ahead, but we can see
plenty there that needs to be done.”
(Turing 1950)
Turing, A. M. (1950). Computing machinery and intelligence, Mind LIX, 433-60.
http://cogprints.org/499/0/turing.html
p. 77
Based on the following articles
● Dodig-Crnkovic G. and Giovagnoli R. (Eds), Computing Nature – A Network of Networks
of Concurrent Information Processes, In: COMPUTING NATURE, (book) Springer,
Heidelberg, SAPERE book series, pp. 1-22, May 2013. http://arxiv.org/abs/1210.7784
● Dodig-Crnkovic G., Dynamics of Information as Natural Computation, Information 2011,
2(3), 460-477; doi:10.3390/info2030460 Special issue: Selected Papers from FIS 2010
Beijing Conference, 2011.
http://www.mdpi.com/journal/information/special_issues/selectedpap_beijing
http://www.mdpi.com/2078-2489/2/3/460/ See also:
http://livingbooksaboutlife.org/books/Energy_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:
http://www.idt.mdh.se/~gdc/work/publications.html
p. 78
THANKS TO
Prof. Francisco Hernández Quiroz for invitation to UNAM
Alberto Hernández Espinosa for organizing my visit to Infotec
Also
Hector Zenil
Carlos Gershenson
& Tom Froese
for their work that convinced me that UNAM is an
extraordinary place!
p. 79
A Mathematical Model for Infocomputationalism- A. C. Ehresmann
Open peer commentary on the article
“Info-computational Constructivism and
Cognition” by Gordana Dodig-Crnkovic.
Ehresmann proposes a mathematical
approach to the framework developed by
Dodig-Crnkovic. Based on the Property of
natural computation, called the
multiplicity principle development of
increasingly complex cognitive processes
and knowledge is described.
“Local dynamics are classically
computable, a consequence of the MP is
that the global dynamics is not, thus
raising the problem of developing more
elaborate computation models.”
p. 80
An Info-Computational Model for (Neuro)cognitive Systems Capable of Creativity Andrée C. Ehresmann
The model, based on a ‘dynamic’
Category Theory, accounting for
the functioning of the neural,
cognitive and mental systems at
different levels of description and
across different timescales.
p. 81
Andrée C. Ehresmann http://www.mdpi.com/1099-4300/14/9/1703