Machine as Mind

1. Machine as Mind Herbert A. Simon 인지과학 협동과정 99132-801 심소영

2. Contents 1. Introduction 2. Nearly-Decomposable Systems 3. The Two Faces of AI 4. The View from Psychology 5. The Matter of Semantics 6. “Ill-Structured” Phenomena 7. The Processing of Language 8. Affect, Motivation, and Awareness 9. Conclusion: Computers Think -- and Often Think like People

3. 1. Introduction • I will proceed from what psychological research has learned about human mind, to the characteristics we must bestow upon computer programs when we wish those programs to think. (Section 4 ~ 8) • By “mind”, I means a system that produces thought, viewed at a relatively high level of aggregation. (Section 2)

4. 1. Introduction • The level of aggregation at which we model phenomena • The primitive of mind are symbols, complex structure of symbols, and processes that operate on symbols (requiring at least tens of milliseconds). At this level, the same software can be implemented with different kinds of hardware.

5. 1. Introduction • Central thesis • At this level of aggregation, conventional computer can be, and have been, programmed to represent symbol structures and carry out processes on those structures in a manner that parallels the way the human brain does it. • Principal evidence • Programs that do just that

6. 1. Introduction • Computer simulation of thinking is no more thinking than a simulation of digestion is digestion. • The analogy is false. The materials of digestion are chemical substances, which are not replicated in computer simulation., but the materials of thought are symbols, which can be replicated in a great variety of materials (including neurons and chips).

7. 2. Nearly-Decomposable Systems • Most complex systems are hierarchical and nearly decomposable. • E.g. Building - Rooms - Cubicles • Nearly-Decomposable Systems • can be analyzed at a particular level of aggregation without detailed knowledge of the structures at the levels below. Only aggregate properties of the more microscopic systems affect behavior at the higher level.

8. 2. Nearly-Decomposable Systems • Because mind behaves as a nearly-decomposable system, we can model thinking at the symbolic level, without concern for details of implementation at the hardware level.

9. 3. The Two Faces of AI • AI can be approached in two ways. • First, we can write programs without any commitment to imitating the processes of human intelligence. • E.g. DEEPTHOUGHT • Alternatively, we can write programs that imitate closely the human processes. • E.g. MATER (Baylor and Simon 1966)

10. 3. The Two Faces of AI • Chess-playing programs illustrate the two approaches. • DEEPTHOUGHT does not play in a humanoid way, typically exploring 107 of branches of the game tree before it makes its choice of move. DEEPTHOUGHT rests on a combination of brute force, unattainable by human players, and extensive, mediocre chess knowledge.

11. 3. The Two Faces of AI • Human grandmasters seldom look at more than 100 branches. By searching the relevant branches, they make up with chess knowledge for their inability to carry out massive searches. • MATER uses heuristics, so it looks at fewer than 100 branches. • Because my aim here is to consider machine as mind, the remainder of my remarks are concerned with programs that are intelligent in more or less humanoid ways.

12. 4. The View from Psychology • How does intelligence look to contemporary cognitive psychology? 4.1 Selective Heuristic Search • Human problem solvers do not carry out extensive searches. • People use knowledge about the structure of the problem space to form heuristics that allow them to search extremely selectively.

13. 4. The View from Psychology 4.2 Recognition: The Indexed Memory • The grandmaster’s memory is like a large indexed encyclopedia. • The perceptually noticeable features of the chessboard (the cues) trigger the appropriate index entries and give access to the corresponding information.

14. 4. The View from Psychology • Solving problems by responding to cues that are visible only to experts is called solving them by “intuition”. (solving by recognition) • In computers, recognition processes are implemented by productions: the condition sides serve as tests for the presence of cues, the action sides hold the information that is accessed when the cues are noticed.

15. 4. The View from Psychology • Items that serve to index semantic memory are called “chunks”. An expert in any domain must acquire some 50,000 chunks. • It takes at least 10 years of intensive training for a person to acquire the information required for world-class performance in any domain of expertise.

16. 4. The View from Psychology 4.3 Seriality: The Limits of Attention • Problems that cannot be solved by recognition require the application of sustained attention. Attention is closely associated with human short-term memory. • The need for all inputs and outputs of attention-demanding tasks to pass through short-term memory essentially serializes the thinking process. We can only think of one thing at a time.

17. 4. The View from Psychology • Hence, whatever parallel processes may be going on at lower (neural) levels, at the symbolic level the human mind is fundamentally a serial machine.

18. 4. The View from Psychology 4.4 The Architecture of Expert Systems • Human Experts • Search is highly selective, the selectivity is based on heuristics stored in memory. • The information accessed can be processed further by a serial symbol-processing system.

19. 4. The View from Psychology • The AI experts systems • have fewer chunks than the human experts and make up for the deficiency by doing more computing than people do. The difference is quantitative, not qualitative: Both depend heavily upon recognition, supplemented by a little capacity for reasoning (i.e., search)

20. 5. The Matter of Semantics • It is claimed that the thinking of computers is purely syntactical, that is, computers do not have intentions, and their symbols do not have semantic referents. • The argument is refuted by concrete examples of computer programs that have goals and that demonstrably understand the meanings of their symbols.

21. 5. The Matter of Semantics • Computer-driven van program has the intention of driving along the road and creates internal symbols that denote landscape features, interprets them, and uses the symbols to guide its steering and speed-control mechanisms • Chess-playing program forms internal representation that denotes the chess position and intends to beat its opponent.

22. 5. The Matter of Semantics • There is no mystery about semantics and human intentions. • “Semantic” means that there is a correspondence, a relation of denotation, between symbols inside the head and objects outside and the two programs have goals. • It may be objected that computer does not “understand” the meaning of its symbols or the semantic operations on them, or the goals it adopts.

23. 5. The Matter of Semantics • The word “understand” has something to do with consciousness of meanings and intentions. But my evidence that you are conscious is no better than my evidence that the road-driving computers are conscious.. • Semantic meaning • a correspondence between the symbol and the thing it denotes. • Intention • a correspondence between the goal symbol and behavior appropriate to achieving the goal.

24. 5. The Matter of Semantics • Searl’s Chinese Room parable • proves not that computer programs cannot understand Chinese, but only that the particular program Searl described does not understand Chinese. • Had he described a program that could receive inputs from a sensory system and emit the symbol “cha” in the presence of tea, we would have to admit that it understood a little chinese.

25. 6. “Ill-Structured” Phenomena • “Ill-structured” means • that the task has ill-defined or multi-dimensional goals, • that its frame of reference or representation is not clear or obvious, • that there are no clear-cut procedures for generating search paths or evaluating them. • Use of NL, learning, scientific discovery • When a problem is ill-structured, • a first step is to impose some kind of structure that allows it to be represented at least approximately.

26. 6. “Ill-Structured” Phenomena • What does psychology tell us about problem representations? 6.1 Forms of Representation • Propositional Representations • Situations may be represented in word or in logical or mathematical notations • The processing will resemble logical reasoning or proof.

27. 6. “Ill-Structured” Phenomena • Pictorial Representations • Situations may be represented in diagrams or pictures. • With processes to move them through time or to search through a succession of their states. • Most psychological research on representations assumes one of the representations mentioned.

28. 6. “Ill-Structured” Phenomena 6.2 Equivalence of Representations • What consequences does the form of representation have for cognition? • Informational & Computational Equivalence • Two representations are informationally equivalent if either one is logically derivable from the other. If all the information available in the one is available in the other. • Two representations are computationally equivalent if all the information easily available in the one is easily available in the other.

29. 6. “Ill-Structured” Phenomena • Information is easily available if it can be obtained from the explicit information with a small amount of computation. (small relative to the capacities of the processor) • E.g. Arabic and Roman numerals are informationally equivalent, but not computationally equivalent. • E.g. Representation of the same problem as a set of declarative propositions in PROLOG, as a node-link diagram in LISP.

30. 6. “Ill-Structured” Phenomena 6.3 Representations Used by People • There is much evidence that people use mental pictures to represent problems, but there is little evidence that people use propositions in predicate calculus. • Even in problems with mathematical formalisms, the processes resemble heuristic search more than logical reasoning.

31. 6. “Ill-Structured” Phenomena • In algebra and physics, subjects typically convert a problem from natural language into diagrams and then into equations. • Experiment with presentation(* and +) and a sentence, “The star is above/below the plus” • Whatever the form of representation, the processing of information resembles heuristic search rather than theorem proving

32. 6. “Ill-Structured” Phenomena 6.4 Insight Problems (“Aha!” experiences) • Problems that tend to be solved suddenly, after a long period of fruitless struggle. • “Insight” that lead to change in representation and solution of the mutilated checkerboard problem can be explained by mechanisms of attention focusing.

33. 6. “Ill-Structured” Phenomena The representations people use (both propositional and pictorial) can be simulated by computers.

34. 7. The Processing of Language • Whatever the role it plays in thought, natural language is the principal medium of communication between people. • Far more has been learned about the relation between natural language and thinking from computer programs that use language inputs or outputs to perform concrete tasks.

35. 7. The Processing of Language 7.1 Some Programs that Understand Language • Novak’s ISMC program (1977) • extracts the information from natural-language descriptions of physics problems, and transforms it into an internal “semantic” representation suitable for a problem-solving system.

36. 7. The Processing of Language • Hayes and Simon’s UNDERSTAND program (1974) • reads natural-language instructions for puzzles and creates internal representations(“pictures”) of the problem situations and interpretations of the puzzle rules for operating on them. • These programs give us specific models of how people extract meaning from discourse with semantic knowledge in memory.

37. 7. The Processing of Language 7.2 Acquiring Language • Siklossy’s program ZBIE (1972) • was given (internal representations of) a simple picture (a dog chasing a cat) and a sentence describing the scene. • With the aid of a carefully designed sequence of such examples, it gradually learned to associate nouns with the objects in the pictures and other words with their properties and the relations.

38. 7. The Processing of Language 7.3 Will Our Knowledge of Language Scale? • These illustrations involve relatively simple language with a limited vocabulary. • To demonstrate an understanding of human thinking, we do not need to model thinking in the most complex situations we can imagine. Our theory explain the phenomena in range of situations that would call for genuine thinking in human.

39. 7. The Processing of Language 7.4 Discovery and Creativity • Making scientific discoveries is both ill-structured and creative. These activities have been simulated by computer. • BACON program (Simon et al. 1987) • When given the data available to the scientists in historically important situations, it has discovered Kepler’s Third Law, etc..

40. 7. The Processing of Language • KEKADA program (Simon et al. 1988) • plans experimental strategies, responding to the information gained from each experiment to plan the next one. • is able to track Faraday’s strategy. • Programs like BACON and KEKADA show that scientists use essentially the same kinds of processes as those identified in more prosaic kinds of problem solving.

41. 8. Affect, Motivation, and Awareness • Motivation selects particular tasks for attention and diverts attention from others. • If affect and cognition interact largely through the mechanisms of attention, then it is reasonable to pursue our research on these two components of mental behavior independently. • Many of the symbolic processes are in conscious awareness, and awareness has implications for the easy of testing.

42. 9. Conclusion: Computers Think and Often Think like People • Computers can be programmed, and have been programmed, to simulate at a symbolic level the processes that are used in human thinking. • The human mind does not reach its goals mysteriously or miraculously. Even its sudden insights are explainable in terms of recognition processes, well-informed search, and changes in representation motivated by shifts in attention.