Monkey Before the Skeleton (Ecce - PowerPoint Presentation

Slide1

Monkey Before the Skeleton (Ecce

simia

),

Gabriel von Max, Prague painter (1840-1915)Slide2

Towards Computational Models

of Artificial Cognitive Systems

That Can, in Principle, Pass the Turing test

Jiri WiedermannInstitute of Computer Science, Prague Academy of Sciences of the Czech Republic

Partially

supported GA CR grant No. P202/10/1333

SOFSEM 2012 January 21-27, 2012

Spindleruv

MlynSlide3

``I believe that in about fifty years' time it will be possible, to program computers, with a storage capacity of about 100

kB

, to make them play

the imitation game so well that an average interrogator will not have more than 70 % chance

of making the right identification after five minutes of questioning.

The original question, "Can machines think?" I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that

one will be able to speak of machines thinking

without expecting to be contradicted."

From the discussion between Turing and one of his colleagues (M. H. A. Newman, professor of

mathematics at the Manchester University):

Newman

: I should like to be there when your match between a man and a machine takes place,

and perhaps to try my hand at making up some of the questions. But that will be a long time from

now, if the machine is to stand any chance with no questions barred?

Turing

: Oh yes, at least 100 years, I should say.Slide4

Three heretic ideas:

We already have a sufficient knowledge to

understand the working of interesting minds achieving a high-level cognition

Achieving a higher-level AI is not a matter of a fundamental scientific breakthrough but rather a matter of exploiting our best theories of artificial minds, and a matter of scale, speed and technological achievements

It is unlikely that thinking machines will be developed by purely academic research

since it is beyond its power to concentrate the necessary amount of man power and technology. Approaches to mind understanding:Understanding by philosophying Understanding by designing (specifying)Understanding by constructingSlide5

Outline

Current state: Watson the Computer vs. humanoid robotic systems

Winds of Change

Escaping the Turing testEscaping BiologismInternal World Models

Mirror neuronsGlobal Workspace Theory(

Dis)solving the Hard Problem of ConsciousnessEpisodic MemoriesReal Time Massive Data ProcessingComprehensive and Up-To-Date Models of Cognitive SystemsHUGO: A Non-Biological Model of a Conscious Agent System

Conclusions – lessons from what we

have seenSlide6

Watson

-

an AI system capable to answer the questions stated in natural language

Jeopardy

!

(in the CR – the TV game „Riskuj!“) – given an answer one has to guess the question.

E.g.:

5280 (

how many

feets

has a mile), or 79 Wistful Vista (address of

Fibber

a

nd

Molly

McGee

)

Category:

General Science

Clue:

When hit by electrons, a phosphor gives off

electromagnetic energy in this form.Answer: Light (or Photons)Category: Lincoln BlogsClue: Secretary Chase just submitted this to me for the third time; guess what, pal. This time I’m accepting it.Answer: his resignationCategory: Head NorthClue: They’re the two states you could be reentering if you’re crossing Florida’s northern border.Answer: Georgia and Alabama

Category: Rhyme TimeClue: It’s where Pele stores his ball.Subclue 1: Pele ball (soccer)Subclue 2: where store (cabinet, drawer, locker, and so on)Answer: soccer locker

Source

: AI

Magazine

,

Fall

2010Slide7

Winds of Change

New trends in theory:

escaping biologism

escaping the Turing Test strengthening the position of embodiment: a common sensorimotor

basis for phenomenal and functional consciousness evolutionary priority of phenomenal consciousness over functional one

internal world models, mirror neurons global workspace theory episodic memoryTechnological progress: maintenance of supercritical volumes of data, and

searching and retrieval of data by supercritical speed Slide8

A shift in popular thinking about artificial minds - people generally accept that computers can think (albeit in a different sense than some philosophers of mind would like to see)

John Searle

: “

Watson Doesn't Know It Won on 'Jeopardy!' IBM invented an ingenious program—not a computer that can think.”

Noam Chomsky: “Watson understands nothing. It’s a bigger steamroller. Actually, I work in AI, and a lot of what is done impresses me, but not these devices to sell computers.”

What these gentlemen failed to see

is the giant leap

from the formal rules of the chess playing to informality of Jeopardy! rules…

J.R. Lipton

: Big insight – a program can be immensely powerful even if it is

imperfect. Slide9

A new trend: escaping biologism

Rodolfo

Llinas (a prominent neuroscientist):“I must tell you one of the most alarming experiences I've had in pondering brain function.... that the octopus is capable of truly extraordinary feats of intelligence… most remarkable is the report that octopi may learn from observing other octopi at work. The alarming fact here is that the organization of

the nervous system of this animal is totally different from the organization we have learned is capable of supporting this type of activity in the vertebrate brain....

there may well be a large number of possible architectures that could provide the basis of what we consider necessary for cognition and

qualia.... Many possible architectures for cognition

Why should we only think about human brain when

designing artificial minds?Slide10

Turing test is explicitly anthropomorphic.Russell and Norvig: "aeronautical engineering texts do not define the goal of their field as 'making machines that fly so exactly like pigeons that they can fool other pigeons’”.

A new trend: escaping the Turing test

All minds

Human

mind

Alien

minds

Animal

minds

Artificial

mindsSlide11

A new trend: Internal World Models

IWMs capture a “description” of that (finite) part of the world and that part of the self which has been “learned” by agent’s

sensori

-motor activities. An IWM is fully determined by the agent’s embodiment and is automatically built during agent’s interaction with the real world.

Mechanisms situating an agent in its environment ; they determine the syntax and the semantic of agent behavior and perception in its environment

Finite

control

Sensory-motor

units

World model

The body

(Infinite) stream of inputs generated by sensory-motor interaction

A virtual inner world in which an agent can thinkSlide12

A new trend: Mirror neurons – a mechanism for “mind reading” of other subjects

“the discovery of mirror neurons in

the frontal lobes of monkeys, and

their potential relevance to human brain evolution is the single most important ``unreported“ (or at least,

unpublicized) story of the decade. I predict that mirror neurons will do for psychology what DNA did for

biology: they will provide a unifying framework and help explain a host of mental abilities that have hitherto remained mysterious and inaccessible to experiments“V.S. Ramachandran

Mirror neurons

: are active when a subject performs a specific action as well as when the subject observes an other or a similar subject performing a similar action (

Rizzolatti

, 199x)

Slide13

A new trend: Global Workspace Theory

a simplistic, very high-level cognitive architecture that has been developed by

B. J. Baars

by the end of the last century to explainemergence of a conscious process from large sets of unconsciousprocesses in the human brain.

The GWT can successfully model a number of

characteristics of consciousness, such as its role in handling novel situations, its limited capacity, its sequential nature, and its ability to trigger a vast range of unconscious brain processes.Interesting:

Watson the Computer works according to the GWTSlide14

A new trend: evolutionary approach to phenomenal consciousness (Inman Harvey)

A naive “incremental” approach to create phenomenal consciousness:

Create a “zombie” with functional consciousness (the easy problem)

Add the extra ingredient to give it a phenomenal consciousness

(the hard problem)

“evolutionary approach allows emulation without comprehension” Slide15

A new trend: a common sensorimotor basis for phenomenal and functional consciousness

Source: How to build a robot that feels.

J.Kevin

O'Regan

,Talk given at

CogSys

2010 at ETH Zurich

A

sensorimotor

interaction with the environment involving corporality, alerting capacity, richness,

insubordinateness

, and the self

Instead of thinking of the brain as the generator of feel, feel is considered as a way of interacting with the worldSlide16

A new trend: Episodic Memory

An agent without episodic memory is like

a person with amnesia

Episodic memory systems allow

“mental time travel”

and can support a vast number of cognitive capabilities based on inspecting memories from the past that are ``similar" to the present situation, such as noticing novel situations, detecting repetitions,

virtual sensing (reminded by some recall),

future action modeling,

planning ahead,

environment modeling,

predicting success/failure, managing long term goals, etc. is what people ``remember", i.e., the contextualized information about autobiographical events (times, places, associated emotions), and other contextual knowledge that can be explicitly stated.

Efficient management and retrieval from episodic memories is a case for real-time massive data processing technologies.

(Drawing by Ruth

Tulving

)Slide17

A new trend: intelligence might be a matter of scale and speed: maintaining supercritical volumes of data and their searching and retrieval by supercritical speed (cf. episodic memories).

Element

Number

of cores

Time to answer one Jeopardy! question

Single core

1

2 hours

Single IBM Power 750 server

32

<4 minSingle rack (10 servers)

320

<30 seconds

IBM Watson (90 servers)

2 880

<3 seconds

Memory:

20 TB

200 million pages

(~1

000

000 books)

~1 000 000 million lines of code5 years development(20 men) A lesson from Watson the Computer: intelligence might not only be a matter of suitable algorithms, but also, and mainly so, of the ability to accumulate (e.g., via learning and episodic memories storing), organize, and exploit large data volumes representing knowledge at a speed matching the timescale of the environmental requirements (real time data processing).Slide18

A new trend: Comprehensive and up-to-date models of cognitive systems

An urgent need of

situatedness

via embodiment

(from J. A. Comenius,

Orbis

pictus

, 1658)

An embodied cognitive agent

is a robot i.e., an

embodied computer

, which is a computer equipped by

sensors

by which it “perceives” its environment and by

effectors

by which it interacts with its environment

Nuremberg funnel,

Harsdörffer, Georg Philipp:

Poetischer Trichter,

Nuremberg

1648-1653Slide19

HUGO: a Non-Biological Model of an Embodied Conscious Agent

From: J. Wiedermann: A High Level Model of an Embodied Conscious Agent, IJSSCI, 2, 2010

Semantic world model

Syntactic world model

Global workspace

Mirror net

Episodic

memorySlide20

A high-level schema of a robot:

Finite control (a computer)

Sensory-motor units

(Infinite) stream of inputs generated by sensory-motor interaction

World model

Real world

The body

Mechanisms

situating the agent in its environment

must be considered: internal world modelsSlide21

The central idea: Educating and Teaching a Robot

The purpose of educating and teaching an agent is to build its internal world model

The internal world model gives a “description” of that (finite) part of the world (inclusively of agent’s (it)self) which has been “learned” by agent’s S-M activities.

The model is fully determined by the agent’s embodiment and is automatically built during agent’s interaction with the real worldSlide22

The idea of two cooperating world models in cognitive systems

“action”

Dynamic world model:

sequences of

sensorimotor

information

Controls the agent’s behavior

Static world model:

Elements of a coupled

sensory-motor

information; responsible for situating the agent

Real world

“cognition”

Motor instructions

perception

Sensory-motor unitsSlide23

Grounding

Abstract

concepts

U

nits of S-M

information

(World’s

“syntax”)

Embodied

concepts

S-M

units

Motor instructions

Multimodal

information

Perception

Motor instructions

Symbolic

level

Sub-symbolic level

Control unit

Body

Environment

Mirror net

An architecture of an

embodied cognitive

agentSlide24

The task of the syntactic world model:

Coupling

the motor instructions with the perception information into so-called multimodal information;

Learning frequently occurring multimodal information from the coupled input streams (one coming from the dynamic model and one from the S-M units)

Associative retrieval: a partial, or “damaged”, or previously “unseen” incoming multimodal information gets completed so that it

corresponds to the “most similar” previously learned information; the result

captures the instantaneous agent’s

situationThe task of the semantic world model:Learning (mining) and maintaining the knowledge from the data-stream of multimodal

informa

tion

delivered by static (syntactic) world model

Realizing the intentionality

:

with each unit of multimodal information a sequence of actions (motor commands) –

habits

- gets associated which can be realized in the given context; Slide25

Mirror neurons: are active when a subject performs a specific action as well as when the subject observes an other or a similar subject performing a similar action (

Rizzolatti

, 199x)A generalization

: … a set of neurons which are active when a subject performs any frequent action as well as when only partial information related to that action is available to the subject at hand

Implementing the syntactic world model:

Visual inf. Aural inf. Haptic Propriocept.

Multimodal

information

Learns frequently occurring conjunctions of related input information

It gets activated when only partially excited (by one or several of its inputs)

Works as

associative memory,

completing the missing input information

Mirror net forms and stores (pointers to)

episodic memories

The basis for understanding imitation learning, language acquisition,

thinking, consciousness.Slide26

What knowledge is mined and maintained in a dynamic world model:

often occurring concepts

resemblance of concepts contiguity in time or place

cause and effectAn algebra of thoughts…

David Hume 1711-1766

Cognitive tasks:

Simple conditioning

Learning of sequences

Operand

conditioning

(by rewards and punishment)Imitation learningAbstraction formingHabits formation, etc.

“Hume’s test” for intelligenceSlide27

Previously

activated

concepts

Passive

concepts

Newly activated concepts

Multimodal information

Currently

activated

concepts

A

cogitoid

: an algorithm

building a neural net for

knowledge-mining

from

the

flow

of multi-modal information

Emotions

Excitatory and inhibitory links

aaaa

affect

Wiedermann 1999

Implementing the dynamic world model

Habits: often followed

chains of conceptsSlide28

What both world models jointly do for an agent:

A mechanism enabling

imitation of activities of other agents (without

understanding)A

germ of awareness – a mechanism for distinguishing between one’s own action, and that of an observed agentA mechanism of empathy

A

substrate for a mechanism for

predic

ting the results

of an agent’s own or observed actions via their “simulation” in the virtual model of the known part of the real worldUnderstanding: an agent “understands” its actions in terms of their embodiment in terms of habits (and thus: of S-M actions plus associated emotions)

Phenomenal consciousness

(according to

O’Regan

) as a habit of conscious awareness of

performing one’s own skills

Humanoid Robot

Mahru

Mimics a Person's Movements in Real Time

A person wears the motion tracking suit while performing various tasks. The movements are recorded and the robot is then programmed to reproduce the tasks while adapting to changes in the space, such as a displaced objects.Slide29

The birth of

communication and speaking

By indicating a certain action an agent

broadcasts a visual information

which is completed by the empathy and prediction mechanism of an observing agent into the intended action

Formation of the self conceptPossibility for emotions

to enter the

game

The birth of the

body language

Adding of articulation (vocalization) and gesticulation tempering

The verbal component of the language gets associated with the motor of speech organs and prevails over gesticulation

Development of

episodic memory

management and retrieval mechanismsSlide30

The birth of thinking

c

ogitoid

Mirror

neuro

ns

Motor

instructions

Multimod

al

informa

tion

Subsequent

decay of whatever motor activity

(

of vocal organs)

Perception suppressing

Switching-off

motor

instruction

realization

Mirror neurons complete motor instructions by missing perception learned by experienceWiedermann 2004Beginning of

thinking as a habit of speaking to oneselfAn agent operates similarly as before, albeit it processes “virtual” data. It works in an „off-line“ mode, it is virtually situatedSlide31

The birth of

functional consciousness

The agents are said to possess

artificial

functional consciousness

iff their communication abilities reach such a level that the agents are able to fable on a given theme.More precisely, the conscious agents can

Communicate in a

high-level language

Verbally

describe

past and present experience, and expected consequences of future actions, of self or of other agentsRealize a certain activity given its verbal high-level descriptionExplain the meaning of notions

Learn

new notions and new languages

Consciousness is a

big suitcase

M.

MinskySlide32

A sketch of the evolutionary development of

cognitive abilities, consciousness included

Phenomena

l

consc

.

Funct

.

consc

.

From: J. Wiedermann: A High Level Model of an Embodied Conscious Agent, IJSSCI, 2, 2010

Slide33

A thinking machine: a de-embodied robot

c

ogitoid

Mirror

neuro

ns

A brain in a vat

A robot’s thinking mechanism

in a computerSlide34

Lessons from what we have seen

Achieving a higher-level artificial intelligence no longer seems to be a matter of a fundamental scientific breakthrough

but rather a matter of exploiting our best algorithmic theories of thinking machines supported by our most advanced robotic and real time data processing technologies.

An artificial cognitive system is quite a complex system with only a few components none of which could work alone and none of them could be developed separately;

It is unlikely that thinking machines will be developed by purely academic research since it is beyond its power to concentrate the necessary amount of man power and technology. This cannot be accomplished by large international research programs

either since a dedicated long-term open-ended effort of many researchers concentrated on a single practically non-decomposable task is needed.

It seems to be a unique strategic opportunity for giant IT corporations

.

The road towards thinking machines glimpses ahead of us and it only is a matter of money whether we set off for a journey along this road.Slide35

The EndSlide36

Caspar David Friedrich,

Giant Mountains, cca 1830