What am I doing here?• Introducing recent subject areas in contemporary science all which are

somewhat related to the observation of nature by computerization.

• Describing the links among the topics in holistic fashion : packing existing areas into a cohesive whole

– Each field has its own History, Heroes, Prizes, Theories, Methodologies, Jargon

• Syncretism

– “Anything seems commonplace, once explained” Dr. Watson to Sherlock Holmes

– All truths are easy to understand once they are discovered; the point is to discover them. Galileo Galilei

• Relating these topics to artificial Information Systems,

– presumably any artificial system can be built to better suit the natural environment it is supposed to perform in.

– Information systems can be equipped to handle problems or domains that are regarded as unmanageable, too complex or time-consuming

• Not lecturing on any one of the areas in details

• Not questioning the plausibility of any approach

Why am I doing it?One Truth, Many Faces: Take “intelligence”

• Linguists talk of intelligence in terms such as "syntax" and "semantics." To them, the brain and intelligence is all about language.

• Vision scientists refer to 2D, 2½D, and 3D sketches. To them, the brain and intelligence is all about visual pattern recognition.

• Computer scientists talk of heuristics, schemas and frames. They make up new terms (ontology) to represent knowledge. They don’t consider the structure of the brain and how it would implement any of their theories.

• Social scientist, science philosphers give verbal accounts of their observations with experimental data from surveys which are regarded as inconclusive for the computer people.

• Anatomists and neurophysiologists wrote extensively about the structure of the brain and how neurons behave, but they mostly avoided any attempt for a large-scale theory.

Why am I doing it?

So what are these topics?

What will you take away?• difference between true, good, beautiful and real

• types of knowledge: factual, conceptual, metacognitive.

• taxonomies, ontologies, and maps: how to organise information

• how do our bodies and environment shape the way we think/frame

• why is a story more than a flow of events

• being goal oriented, and ability to formulate/story goals and path/project to the goal

• the story of data AND processing in a system

• what does the brain do, how does it do it, what is consciousness

• brain based learning, learning as an evolutionary process, how to boost learning

• how do the neurological differences between man and woman effect the way they learn and act

• transactional analysis, persuasion, play theory, effective communications

• favoring preventive action instead of reactive, negotiation instead of war to resolve conflict

• sharpening beliefs to make better decisions: train your mind change your brain! Mental causation

• understanding risk, conditional probability, possibility, uncertainty

• how does information "make" markets, how it prevents anarchy to emerge

• free market economy, how can it be perfect, why is it better than any controlled system

• networks everywhere, hierarchy of networks, network of relations, ruling/feeding networks

• how does social evolution begets population thinking, swarm intelligence, wisdom of crowds

• evolution and acquistion of language

• evolutionary diversity by means of horizontal gene transfer (instead of mutation)

• how does evolution begets "structure" out of nothing at all; modern heuristics

• open (dissipative) systems and autopoesis

• difference between analog and digital, natural and artificial: orderly::alive::chaotic::disorderly

• how nature conforms on herself

• life being a pocket of conserved order against increasing disorder, choosing life by avoiding disorder

A Sneak Preview

What is the relation between

• Information

• Ontology

• Order and

• Complexity

Answer: Nature is conformable on herself

A Sneak Preview• Example 1: Self-organisation (tipping point) not only

observed on sand waves in the desert but also in evolution when a new species is born

• Example 2: Memory to establish a feedback loop is not only kept in neuronal junctions but also DNA and memes

• Example 3: Consider the pattern: Separation-Containment-Movement-Resistence-Information-Reproduce-Possibilities-Ensembles-Structure. Observe the pattern in

– evolution: Fitness~Resistence, Survive~Information, Population~Possibities, New species

~Structure.

– inertia,

– anekāntavāda

– every “story” that has meaning

Separation

ContainmentStructure

MovementEnsembles

Possibilities Resistence

Reproduction Information

PROSPECTIVE VALUE FUNCTION

RISK SEEKING

RISK AVERSION

NIS Topics : Extelligence

Before you become too entranced with gorgeous gadgets and mesmerizing video displays, let me remind you that information is not knowledge, knowledge is not wisdom, and wisdom is not foresight. Each grows out of the other and we need them all.

– Arthur C. Clarke

NIS Topics : Extelligence• Contextual and cultural analogue of internal, personal intelligence.

The pervading set of man-made creations that embody the knowledge stored outside our minds.

• We are born into it. Mirror Neurons helps to assimilate it. Forms basis of our memories, symbols and rules (Together with internal sensations). Effects the nature of and wiring between our cognitive agents.

• Extelligence evolves (e.g oral myths → clay tablets → print books → Internet). It evolves with a speed dependent on human lifetime limitations.

• Knowledge gained through extelligence is inherited (epigenesis or morphic resonance ?)

• Plays tennis with intelligence: It is the domain where language evolves and language, in turn, co-evolves with the brain.

• There are forms of extelligence : mainstay, supportive, symbolic, communicative, viral

Is the universe around us a figment of our imagination? Or are our minds figments of reality? (Mind-Body Problem)

Extelligence Topics

• Knowledge

– We share a common repertoire of reliable, valid and conductive pieces of information, don’t we?

• Assimilation vs Accomodation

– Our interaction with extelligence is studied by psycholinguistics, theoretical linguistics, semiology, social psychology

• Social Pschology

– Scientifically exploring how we think about,

influence, and relate to one another

• Memetics

– Persuasion, Viral influences, Indoctrination

A Sneak PreviewHeseinberg’s Uncertainty Principle : Down to an atomic particle matter gains a state only when being observed

Maya : The environment we observe is just an illusion in our mind, except for the artificial components which are figments of someone’s reality

The sensory organs are higly evolved and already process much of the information before relaying to the brain

Our brain which keeps the illusion is formed of a plastic neural network, which mimics patterns, relations, rules

The neural network of the brain contains autocatalytic strange loops at number of levels

A major part of the network is organized to reflect causality. This part also governs symbolic languages and analytical thinking

Agents of the Mind works in an autonomous but differential fashion with predictions using both causal and holist networks. The emergent consciousness makes sense out of some of their behaviour

Some of the behaviours are reactive or proactive but some are simply decisive.

Observations on the results induce permanent change in the network, hence we learn

Another major part is organized in denser loops with emergents (holistic). This part makes creative analogies.

Our creations just add to the extelligence

NIS Topics : Flux• Complex Systems (e.g. ecosystems) are in constant disequilibrium.

Indeed, equilibrium is a good definition of death.

• It is this disequilibrium from which creativity is born : A fluid concept. A pattern that remains on a sea of change. Like a good theme, every variation retains the flavor of the original.

• Variations are recognized by “analogy making”.

• Once the pattern is “perceived” it can be applied to other domains by analogies.

• Such applications are the basis of our predictions

• Pattern seeking mind is the fertile soil for replicators memes

• Fluid Concepts can be found at all grounds of life.

• Natural Processes do not progress like a sequential computer program, but rather “flow” in directions induced by the “observers”. Therefore, it is futile to try explaining them with ever complex formulations

Punctuated Equilibrium on Duality

• Flux in Nature

– Sustainable change, instability is the root of diversity

– Quantum mechanics: Copenhagen Interpretation

• Flux in the Mind

– Did consciousness evolve to sense flux?

• Flux in the Machine

– Cellular Automata : simple rules can yield both chaos and order

– Distill the Network for patterns : eg: Amazon

• Philosophy of Flux :

– The end is born. Vibrating at punctuated equilibrium.

– Perceive the gestalt to adapt.

– To create and adapt to (consume) simultaneously

Flux Topics

A SNEAK PREVIEW

Stephen Wolfram’s RULE NUMBER 30yields complexity

NIS Topics : Mind

• If Mind is what the Brain does :

– How does it do it?

– Why does it do it?

– What are the outcomes?

Mind Topics• Intelligence

– The definitions, gender differences, feature detection, memory consolidation, [beliefs/values, attitude, behaviour, satisfaction, effectiveness]

• Cognitive, Computational and Physiological Neuroscience– Scanning experiments and neurotic syndromes shed light on

work principles

• Theories of Mind, The Science of Consciousness– They debate and the winner is....

• Mind over Matter - Mind over Body– The duality of body/mind or whole/part simply evaporated when

holistic behavior lawfully emerged from the limited behavior of the parts (Kevin Kelly, Out of Control)

NIS Topics : Learning

• Learning is a relatively permanent adaptation in mental representations or associations as a result of experience. As such it is our internalization of observations

Learning Topics

• Learning and the Brain

– Complex learning

– Brain compatible teaching : Transfer, Problem Solving, Memory

– Neurotransmitters, Hormones

• Understanding, Hemisphericity, Emotions

– To control is to understand, orchestration of knowledge

– Gestalt Psychology

• Social Cognition, Gender Differences, Early Learning

Learning : Sneak PreviewE

N V

I R

O N

M E

N T

Sight

Hearing

Touch

Smell

Taste

ImmediateMemory

WorkingMemory

Long-TermMemory

* Convergence Zones

Sensory Filter whosestate is recorded at

MeaningEmotionsIntentions

SensoryRegister

* K-Lines

How the brain deals with information (inspired from Stahl 1985)

NervousSystem

BO

DY

EXPERIENCES

WORLD(SELF)-CONCEPT

Learning Lateral Thinking

NIS Topics : Bionomics

• Bionomics (Kevin Kelly, Out of Control) : Making sense out of collective human behaviour towards cooperation vs selfishness.

• Behavioral Economics : – Definite wins/losses bias decisions;

(e.g. sunk cost fallacy)

– Irrationality ~ innumeracy (predictable)

~ bounded rationality (drunk)

• Explorations can be made using special surveys as well as computer simulations using modelling and artificial worlds

Bionomics Topics• Game Theory : a mathematical framework designed for

analyzing the interaction between several agents whose decisions affect each other

• Decision Theory : a theory of one person games, or a game of a single player against nature. “Behavioral Economics”

• Information Theory : Resort to Information Theory instead of epistemology when dealing with mathematical frameworks related to knowledge

• Networks : Its a social affair. Social structures are scale-free networks.

• Modelling : Necessary for constructing mathematical models for computer simulations

• Artificial Life : Computer agents and interaction rules to simulate real life situations over generations.

NIS Topics : Symbiotic Intelligence

• A major source of order in nature is symbiotic relations. Half of all species are parasitic.

• A good spot to observe symbiotic relationship is co-evolution of brain and language

• Can there be such a relation between man and machine intelligence, given that these two can not mimic each other so far?

• Man-Machine Interface : What color is a chameleon placed on the mirror?

• Can the machine be a natural extension of the mind?

Symbiotic Intelligence Topics

• Co-evolution of– Brain and Language

– Man and Machine

• Semiology

• Artificial Intelligence– Is there a way to escape from smart imitators? (Eliza

effect)

• Man-Machine Interfaces

• Visual Language Theory– The language as the carrier of knowledge and

streamer of thoughts

NIS Topics : Evolutionary Computation

Paradigms of evolutionary computation

– genetic algorithms,

– evolution strategies,

– evolutionary programming,

– classifier systems, and

– combinations or hybrids

are used to tackle problems that are either intractable or unrealistically time consuming to solve through traditional computational strategies

Evolutionary Computation Topics

• Darwinian Processes

• Evolutionary Game Theory

• Evolution of Complexity

• Genetics

• Heuristics

NIS Topics : Complex Adaptive Systems

• A CAS is an “open” collection of interacting autonomous agents without central control whose behaviour is affected by feedback (memory) so that they adapt their strategies (evolve).

• CAS appears alive (Kelly coins the term vivisystem), resilient, trails a punctuated equilibrium and exhibits emergent phenomena

• CAS cannot be fully optimized, controlled, predicted, booted up quickly, and cannot be understood through linear models of causation

• Chaos author, James Gleick, calls it mapping "the morphology of the amorphous"

Complex Adaptive Systems Topics

• Properties of Complex Adaptive Systems

• Non-Linear Systems

• Self Organization

How to make something out of nothing

• Distribute being (Hive Mind)

• Control from the bottom up

• Cultivate increasing returns

• Grow by chunking

• Maximize the fringes

• Honor your errors

• Pursue no optima; have multiple goals

• Seek persistent disequilibrium

• Change changes itself.

Resources

• Edge Foundation

– Established in 1988 as an extention to “The Reality Club”. The Club of the Digerati.

– www.edge.org

• Stanford Encyclopedia of Philosophy

– plato.stanford.edu

• MIT Encyclopedia of the Cognitive Sciences

– cognet.mit.edu

• Principia Cybernetica

– pespmc1.vub.ac.be

• Complexity Digest

– www.comdig.org

Extelligence:

1. Changing environment to change frame of mind OR changing frame of mind to change environment

2. Using ontic dumpings (epistemologically accurate and reliable) to get “street smart”

3. Using memetics to influence others

4. Constructing a self-model as perceived by others

5. Self-administring inoculation against indoctrination

Flux:

6. Projecting to the end of a story/episode and being mindful about the flow of events in regard of the embracing story

7. Observing Punctuated Equilibrium as the prime indicator of life

8. Seeking flow in pursuit of optimal task performance

9. Keeping implicospheres large enough for holist thinking, syncretization

Mind:

10. Emergence of consciousness

11. The relation between intelligence and hemisphericity & gender

12. Emotional regulation on intentions for easier internalization

13. Use of brain scans in cognitive neuroscience and related products

14. The relation between logic and computation, and why they can’t fully explain human thought process

Learning:

15. Choosing the right (relevant and achievable) challanges on the way to a final goal

16. Balancing the demands of challenges with the scarcity of knowledge/competencies available

17. Physiology, structure, working and types of memory

18. Using differentiated patterns that are tagged by emotions for logical+intuitive prediction with probabilistic truth value

19. Learning and neuroplasticity

20. Brain compatible educational design principles

Bionomics:

1.The difference between order and pattern, disorder and entropy, knowledge and information.

2. Using utilities and prospects for making rational decisions

3. How we are rational in intentions, yet irrational in behaviour due to individual beliefs

4. Constructing games to frame interactive decision making reaching an equilibrium

5. Analysing interactions in games, transactions in dialogue and exchanges in signalling

6. Properties of social networks as of natural scale-free economical networks

7. Artificial life of interacting adaptive autonomous agents for modelling real life

Symbiotic Intelligence:

8. Seeking the right rival/challenge to enagage in a co-evolutionary arms race

9. Semiology for designing interfaces for coupled fitness

10. Using a visual language for interfacing man and machine towards coupled fitness

Evolutionary Computation:

11. Using population dynamics to solve complex optimisation problems

12. Neo-Darwinian evolutionary process and its flexibility (e.g horizontal gene transfer)

13. 2nd law of thermodynamics both driving and constraining evolution and confining life to a pocket of order

14. Evolution as a learning process of how to balance exploitation vs exploration

15. Machine learning paradigms providing situation-tuned yet punctuated solutions

Complex Adaptive Systems:

16. Positioning complex adaptive systems (CAS) as a band between order and chaos

17. Emergence after a phase-transition due to self-organisation to criticality

18. The role of feedback and repetition to ensemble and collapse probabilities

19. Life as a dissipative economical society of hierarchical autocatalytic loops where critical chance events pin the fate to the destiny of the life-story

20. Designing plans to be a learning process with feedback from the path and room for recursion so that the likelihood of milestones would collapse