Chapter Twelve

1. Chapter Twelve The Artificial Intelligence (AI) Approach I: The Mind As Machine

2. 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

3. 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)

4. Turing’s Finite State Machine a/b c/d e/f S2 S0 S1 g/h i/j k/l (A simple example)

5. 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).

6. Convert to internal representations Manipulation by cognitive processes. Translate into action External stimuli Modification of the external world Cognitive/Behavioral Model after Kenneth Craik

7. Computer/Cognitive Corollaries

8. Turing and his Detractors

9. 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.

10. Modality- Independent Representation Perceptual Objects Partial Object Representation Perceptual Features Perception Memory Vision Audition The Hawkins IA Model

11. Emerging Technologies to Address Capacity Challenges of “Strong AI”

12. 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.

13. 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.

14. Approaches to AGI and its Challenges

15. 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

16. Select “candidate solutions” Evaluate fitness of solutions to problem Choose solutions with highest fitness Generate new offspring optimum no yes end The EC Paradigm

17. The conflict between EC/AGI and 18th Century traditions

18. 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”.

19. 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.

20. 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.

21. Sensors and Actuators for IAs

22. 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.

23. A Simple Multiagent Example: Firefighting victim coordinator Medical assistance demolition Removal robot Fire fighting Fire locator

24. 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.

25. 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.”