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 a/b c/d e/f S0 S1 S2 g/h i/j k/l (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 Communication (Bus) Sensor information, control of all external system elements (equipment) Communication between other elements of the computer Receives sensory information ( x ), and provides control ( y ) to external world changes. Communications with external world Signals: from external sensors; to external actuators; conversion to internal representation; conversion to action signals. Communications with external world
Turing and his Detractors Category Argument Evaluation Theological Thinking is a function of man s (God-given) immortal soul. This argument is a serious restriction of the omnipotence of the Almighty. Mathematical t some theorems can neither be proved nor disproved. no such limitations apply to the human intellect. Consciousness Nervous system Extrasensory percepts Universal Computing Machine can never reproduce consciousness The nervous system is not a discrete-state machine. A machine cannot mimic nervous system behavior. Telepathy, clairvoyance, precognition, and psycho kinesis cannot be replicated by machine. This is solipsist point of view. How do you define thinking? A digital computer could be programmed to produce results indicative of a continuous organization 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 Molecules DNA Spin (quantum computing) Light Hexagonal network of carbon atoms rolled up into a seamless cylinder To switch states, change the energy level of the structure within a rotaxane molecule. Based on human biology. Trillions of DNA molecules within a test tube, each performing a given operation on differing data. Computing with the spin of electrons. Spin is a quality of electrons within an atom. Subject to laws of quantum mechanics. Laser beams perform logical and arithmetic operations. High density, high speed (1000 Gigahertz; thousand times a modern computer; logical switch size 1x10 nanometers) 10 11 bits per square inch 6.6 (10 14 ) calculations per second (cps) 660 trillion cps Mainly for memory retains information when power is removed. 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 Combine narrow AI programs into an overall framework Advanced Chatbots Emulate the brain using imaging and other neuroscientific and psychological tools. Challenge Lack ability to generalize across domains. The architecture of a chatbot does not support all the needs of an AGI and the possibility of enhancing it is remote. 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. Use math: develop a mathematical theory of intelligence Complete models of evolution have not been fully developed; the developments in artificial life as one example of an evolutionary system have been disappointing. 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
The EC Paradigm Select candidate solutions Evaluate fitness of solutions to problem Choose solutions with highest fitness Generate new offspring yes optimum end no
The conflict between EC/AGI and 18 th Century traditions Traditional EC/AGI Conscious: we know what we think Unconscious Universal Disembodied Logical Unemotional Value neutral Partly universal Embodied Emotional Emotional Empathetic Serving our own purposes and interests Serving our own purposes and interests Literal: fit an objective world precisely Metaphysical
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
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-ofthe-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 victim coordinato r Medical assistance demolition Fire fighting Fire locator Removal robot
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.