Machine as Mind

1
Machine as Mind

• Herbert A. Simon
• ???? ????
• 99132-801
• ???

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 grandmasters 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

• Searls 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
• Novaks 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 Simons 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
• Siklossys 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 Keplers 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 Faradays 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.