Monkey Before the
Skeleton (Ecce
simia
),
Gabriel von Max, Prague
painter (1840
-
1915)
Towards C
omputational
Models
of Artificial Cognitive Systems
That Can, in Principle,
Pass the Turing test
Jiri Wiedermann
Institute of Computer Science, Prague
Academy of Sciences of the Czech Republic
Partially
su
pp
orted
GA CR
grant
No. P202/10/1333
SOFSEM 2012 January 21
-
27, 2012
Spindleruv
Mlyn
``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.
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 constructing
Outline
1.
Current state: Watson the Computer vs. humanoid robotic
systems
2.
Winds of Change
–
Escaping the Turing test
–
Escaping
Biologism
–
Internal World Models
–
Mirror neurons
–
Global Workspace Theory
–
(
Dis
)solving the Hard Problem of Consciousness
–
Episodic Memories
–
Real Time Massive Data Processing
–
Comprehensive and Up
-
To
-
Date Models of Cognitive Systems
3.
HUGO: A Non
-
Biological Model of a Conscious Agent
System
4.
Conclusions
–
lessons from what we
have seen
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 (ad
d
res
s 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 Blogs
Clue:
Secretary Chase just submitted this to me for
the third
time; guess what, pal. This time I’m
accepting it.
Answer
: his resignation
Category:
Head North
Clue:
They’re the two states you could be reentering
if you’re
crossing Florida’s northern border.
Answer:
Georgia and Alabama
Category:
Rhyme Time
Clue:
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
2010
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 memory
Technological progress:
•
maintenance of supercritical volumes of data, and
•
searching and retrieval of data by supercritical speed
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.
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?
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
minds
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 think
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)
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 explain
emergence of a conscious process from large sets of unconscious
processes 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 GWT
A new trend: evolutionary approach to phenomenal
consciousness (Inman Harvey)
A naive “incremental”
approach to create
phenomenal consciousness:
1.
Create a “zombie” with
functional consciousness
(the easy problem)
2.
Add the extra
ingredient to give it a
phenomenal
consciousness
(the hard problem)
“evolutionary approach allows emulation without comprehension”
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 world
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
)
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 min
Single 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 code
5 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
).
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
-
1653
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
memory
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 models
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 world
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 units
G
r
o
u
n
d
i
n
g
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
agent
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)
As
s
ociativ
e 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
situation
The task of the semantic world model:
Learning (mining) and maintaining the knowledge
from the data
-
stream of
multimod
al
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;
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.
What knowledge is mined and maintained in a dynamic
world model:
•
often occurring concepts
•
resemblance of concepts
•
contiguity in time or place
•
cause and effect
An
algebra of thoughts…
David Hume 1711
-
1766
Cognitive tasks:
1.
Simple conditioning
2.
Learning of sequences
3.
Operand
conditioning
(by rewards and punishment)
4.
Imitation learning
5.
Abstraction
forming
6.
Habits formation
, etc.
“Hume’s test” for intelligence
Previously
activated
concepts
Pa
ssive
concepts
N
ewly
a
ctivated
c
oncept
s
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 concepts
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
agent
A
m
echani
s
m
of
empat
hy
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
world
Understanding
: 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.
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
•
Forma
tion
of the
self
concept
•
Possibility
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
mechanisms
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
instruc
tions
by
missing
perception
learned by
experience
Wiedermann 2004
Beginning of
thinking as a
habit
of speaking to oneself
An agent operates similarly
as before
,
albeit it
processes “virtual”
data
.
It works in an
„off
-
line“
mode
,
it is
virtually
situated
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
agents
Realize
a certain activity given its verbal high
-
level
description
Explain
the meaning of notions
Learn
new notions and new languages
Consciousness is a
big suitcase
M.
Minsky
A sketch of the evolutionary development of
cognitive abilities, consciousness included
P
h
e
n
o
m
e
n
a
l
c
o
n
s
c
.
F
u
n
c
t
.
c
o
n
s
c
.
From: J. Wiedermann: A High Level Model of an Embodied Conscious Agent, IJSSCI, 2, 2010
A thinking machine: a de
-
embodied robot
c
ogitoid
Mirror
neuro
ns
A brain in a vat
A robot’s thinking mechanism
in a computer
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
.
Caspar David Friedrich,
Giant Mountains, cca 1830
Enter the password to open this PDF file:
File name:
-
File size:
-
Title:
-
Author:
-
Subject:
-
Keywords:
-
Creation Date:
-
Modification Date:
-
Creator:
-
PDF Producer:
-
PDF Version:
-
Page Count:
-
Preparing document for printing…
0%
Σχόλια 0
Συνδεθείτε για να κοινοποιήσετε σχόλιο