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

ElementDigital

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 external world

changes.

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

TheologicalThinking is a function of man’s (God-given) immortal soul.

This argument is a serious restriction of the omnipotence of the Almighty.

Mathematicalt 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

PerceptualObjects

PartialObject

Representation

PerceptualFeatures

Perception

Mem

ory

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)

MoleculesTo switch states, change the energy

level of the structure within a “rotaxane” molecule.

1011 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 (1014) 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 ChatbotsThe 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 disappointing.

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

optimumno

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 18th 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

AgentRepresentative

SensorRepresentative

Actuators

HumanEyes, 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

Medicalassistanc

e

Medicalassistanc

e

Firefighting

Firefighting Fire

locatorFire

locator

demolition

demolition

Removalrobot

Removalrobot

coordinator

coordinatorvictimvictim

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.”