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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
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.”