Ch_12 - Computer Science @ Millersville University

imminentpoppedAI and Robotics

Feb 23, 2014 (3 years and 5 months ago)

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CHAPTER TWELVE

The Artificial Intelligence (AI) Approach I: The Mind
As Machine

What is AI?


Intelligent Agent (IA)



complete machine
implementation of human thinking, feeling, speaking,
symbolic processing, remembering, learning, knowing,
problem solving, consciousness, planning, and decision
-
making.


AI


the computational elements of IAs

Historical Precursors


Mechanical
: Calculating machines (Pascal, Leibnitz,
Newton Babbage)


Intellectual/Philosophical
: Logic (Aristotle);
mathematical calculus (Leibnitz, Newton); Knowedge
-
based agent: (Craik); computation (Turing).


Electronic and computer
: computer (Zuse, Eckart, IBM,
Intel); integrated circuit (Shockley, Kilby)

Turing’s Finite State Machine

S0

S1

S2

g/h i/j

k/l



a/b c/d e/f

(A simple example)

Finite State Explanations

Sn

=

State

(condition)

definition

of

the

system

with

a

number

(n)

indicating

the

specific

state
.

x/y

=


“x”

indicates

what

stimulus

(from

the

external

world)

is

detected
;


“y”

what

action

is

to

be

taken

when

“x”

occurs
.

The

action

“y”

will

move

the

state

of

the

system

to

a

new

state

(or

possibly

retain

the

original

state)
.


Cognitive/Behavioral Model after
Kenneth Craik


Convert to
internal
representations


Manipulation by
cognitive
processes.


Translate into
action


External stimuli


Modification of
the external
world

Computer/Cognitive Corollaries

Element

Digital computer

Turing’s Finite State
Descriptor

Craik

Behavioral
Model

Central Processor
Unit (CPU)

Calculations, Logical
decisions, program
sequence control

Determines State
Transitions.

Makes cognitive
decisions (Cognitive
manipulation.)

Memory

Stores: programs,
results, temporary
results, data

Stores: state definitions
(S0,…), external
information
(“x”),Transition (IF
-
THEN) Rules (“x/y”)

Memory: Facts,
Cognitive Rules,
Cognitive Methods

Input/Output

Sensor information,
control of all external
system elements
(equipment)

Receives sensory
information (“x”), and
provides control (“y”) to
ex瑥r湡l world
c桡湧es.

Signals: from external
sensors; to external
actuators; conversion to
internal representation;
conversion to action
signals.

Communication
(Bus)

Communication
between other elements
of the computer

Communications with
external world

Communications with
external world

Turing and his Detractors

Category

Argument

Evaluation

Theological

Thinking is a function of man’s
⡇Ed
-
give温⁩浭 rtal 獯畬u

周i猠慲s畭敮琠i猠愠獥物o畳u
牥獴si捴io渠of⁴桥 o浮楰o瑥湣e
of⁴桥 䅬浩m桴h.

Mathematical

t
some theorems can neither
be proved nor disproved.

no
such limitations apply to
the human intellect.

Consciousness

Universal

Computing Machine
can never reproduce
consciousness



This

is

solipsist point of view.
How do you define thinking?

Nervous system

The nervous system is not a
discrete
-
state machine. A
machine cannot mimic nervous
system behavior.

A digital computer could be
programmed to produce
results indicative of a
continuous
organization

Extrasensory percepts

Telepathy, clairvoyance,
precognition, and psycho
kinesis cannot be replicated
by machine.

Statistical evidence for such
phenomena is, at the very
least, not convincing.


Predictive Architectures




Craik’s

“predictive” has been reinterpreted by
Hawkins




Hawkins proposes an architecture based on the
neocortex
. Our brains compare perceptual inputs to
expectations.

The Hawkins IA Model

Modality
-

Independent

Representation


Perceptual

Objects


Partial

Object

Representation


Perceptual

Features




Perception

Memory


Vision Audition

Emerging Technologies to Address
Capacity Challenges of “Strong AI”

Technology

Description

Potential Capacity

Nanotubes

Hexagonal network of carbon atoms
rolled up into a seamless cylinder

High density, high speed (1000
Gigahertz; thousand times a
modern computer; logical
switch size 1x10 nanometers
)

Molecules

To switch states, change the energy
level of the structure within a

rotaxane
” molecule.

10
11

bits per square inch

DNA

Based on human biology. Trillions of
DNA molecules within a test tube,
each performing a given operation on
differing data.

6.6 (10
14
) calculations per
second (cps)


660 trillion cps

Spin (quantum computing)

Computing with the spin of electrons.
Spin is a quality of electrons within
an atom. Subject to laws of quantum
mechanics.

Mainly for memory


retains
information when power is
removed.

Light

Laser beams perform logical and
arithmetic operations.

8 trillion cps

Artificial General Intelligence (AGI)



A model envisioned by
Minsky
, McCarthy and
others .




A “thinking machine” with human
-
like “general
intelligence”.




To include: self
-
awareness, will, attention,
creativity as well as human qualities we take for
granted. To date, only formative thinking
characterizes AGI.

The Singularity Institute for IA



Redirects AI research and development towards
theory of AGI.
Kurzweil

calls its goal the “Singularity.”



Narrow AI is a context specific approach to machine
intelligence.



Goal of AGI is an intelligence that is
beyond the
human level.

Approaches to AGI and its Challenges

Method

Challenge

Combine narrow AI programs into an
overall framework

Lack ability to generalize across domains.

Advanced
Chatbots

The architecture of a
chatbot

does not support all the needs of an AGI and the
possibility of enhancing it is remote.

Emulate the brain using imaging and
other neuroscientific and psychological
tools.

We really don’t know how the brain works


software for interpretation is very
limited; the result will be a ‘human
-
like’ brain and the goal of AGI is to surpass
human intelligence.

Evolve an AGI; run an evolutionary
process within the computer and wait for
the AGI to evolve.

Complete models of evolution have not been fully developed; the developments
in “artificial life” as one example of an evolutionary system have been
摩獡灰潩d瑩tg.

Use math: develop a mathematical
theory of intelligence

Current mathematical theories require unrealistic amounts of memory or
processing power.

Integrative Cognitive Architectures: a
software system with components that
carry out cognitive functions and connect
in such a way as to achieve the desired
goal.

We have experience from computer science and neuroscience but this is
currently very complex and a need for extensive creative invention.

Evolutionary Computing (EC)



Some similarity to AGI but modeled on the
principles of biological evolution.




Aims to solve real world problems: finance;
software design; robotic learning




Model and understand natural evolutionary
systems existing in: economics, immunology, ecology




A metaphor for the operation of human thought
processes


singularly germane to achieving an IA

The EC Paradigm

Select

“candidate solutions”

Evaluate fitness of solutions to
problem

Choose solutions with highest
fitness

Generate new offspring

end

optimum

no

yes

Traditional

EC/AGI

Conscious: we know what we think

Unconscious

Universal

Partly universal

Disembodied

Embodied

Logical

Emotional

Unemotional

Emotional

Value neutral

Empathetic

Serving our own purposes and interests

Serving our own purposes and interests

Literal: fit an objective world precisely

Metaphysical

The conflict between EC/AGI and
18
th

Century traditions

Agent
-
based Architectures



“every aspect of learning or other feature of
intelligence can be so precisely described that a
machine can be made to simulate it”.

IA Classifications



Acting humanly: knowledge representation,
reasoning, learning.



Thinking humanly: subsumes psychological elements
(introspection, neurological actions of brain using brain
imaging)




Thinking rationally: solve any problem described in
logical notation


exemplified by Aristotelian principles.





Acting rationally: achieve the best outcome; act best
when uncertainty exists; produce the best expected
outcomes.

Russell/Norvig Generic IAs


Simple Reflex: actions based on existing precepts (survival)




Model
-
based: keep track of changing precepts; maintains an
internal state that it uses to develop responses.



Goal
-
based: actions depend on goals; retain goal information with
desirable situations.



Utility
-
based: enhanced goal
-
based agents


add a quality factor.




Learning agents: outgrowth of Turing (universal computation); build
a learning machine and then “teach it.” (This has become a
preferred method for building state
-
of
-
the
-
art
Ias
.


Sensors and Actuators for IAs

Agent

Representative Sensor

Representative Actuators

Human

Eyes, ears, tactile, hands,
legs, mouth, nose

Hands, legs, mouth, arms

Robotic

Cameras, infrared range
finders, tactile sensors, odor
detectors

Motors and other actuators.

Cognitive (software)

Keystrokes, file contents,
network packets

Display devices (optical,
audio), file outputs, packet
transmission.

Multiagent IAs



A cooperative (or noncooperative) group of IAs
capable of sophisticated information processing
activity.




Based on mechanisms that specify the kinds of
information they can exchange and their method for
doing so.

A Simple Multiagent Example: Firefighting

Medical

assistance

Fire

fighting

Fire

locator

demolition

Removal

robot

coordinator

victim

Overall Challenges to an IA



Considerable criticism of “computational” AI has come from the neuroscientific
community (Edelman and
Reeke
)



coding

of models: programmer must find a suitable representation of the
information; what symbolic manipulations may be required; what antecedent
requirements on the representation; human cognition may not even rely on
symbolic computation at all.



categorization

requirement (facts, rules): the programmer must specify a
sufficient set of rules to define all the categories that the program must support.



procedure

(algorithmic processes): the programmer must specify in advance
the actions to be taken by the system for all combinations of inputs that may
occur. The number of such combinations is enormous and becomes even larger
when the relevant aspects of context are taken into account.

Crossroads



AI is emerging as a central element of cognitive
science.; methodologies lend themselves to study in :
biological modeling ; principles of intelligent behavior ;
robotics.



Numerous practical examples
of IAs provide
encouraging evidence that the disciplines of
psychology, biology, computer science, and
engineering
may
eventually lead to a machine that
“exceeds human intelligence.”