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ARTIFICIAL INTELLIGENCE—I 45Early in the field of AI Art Samuel, then at IBM, wrote a checker playing program, checkers beingthought to be easier than chess which had proved to be a real stumbling block. The formula he wrote forplaying checkers had a large number of rather arbitrary parameters in the weighting functions for makingdecisions, such as for control of the center, passed pieces, kings, mobility, pinned pieces, etc. Samuel madea copy of the program and then slightly altered one (or more) of these parameters. Then he made oneformula play, say, ten games against the other, and the formula which won the most games was clearly(actually only probably) the better program. The machine went on perturbing the same parameters until itcame to a local optimum, where upon it shifted to other parameters. Thus it went around and around,repeatedly using the same parameters, gradually emerging with a significantly better checker playingprogram—certainly much better than was Samuel himself. The program even beat a Connecticut Statechecker champion!Is it not fair to say, “The program learned from experience”? Your immediate objection is there was aprogram telling the machine how to learn. But when you take a course in Euclidean geometry is not theteacher putting a similar learning program into you? Poorly, to be sure, but is that not, in a real sense, what acourse in geometry is all about? You enter the course and cannot do problems; the teacher puts into you aprogram and at the end of the course you can solve such problems. Think it over carefully. If you deny themachine learns from experience because you claim the program was told (by the human programmer) howto do improve its performance, then is not the situation much the same with you, except you are born with asomewhat larger initial program compared to the machine when it leaves the manufacturer’s hands? Areyou sure you are not merely “programmed” in life by what by chance events happen to you?We are beginning to find not only is intelligence not adequately defined so arguments can be settledscientifically, but a lot of other associated words like, computer, learning, information, ideas, decisions(hardly a mere branching of a program, though branch points are often called decision points to make theprogrammers feel more important), expert behavior—all are a bit fuzzy in our minds when we get down tothe level of testing them via a program in a computer. Science has traditionally appealed to experimentalevidence and not idle words, and so far science seem to have been more effective than philosophy inimproving our way of life. The future can, of course, be different.In this chapter we have “set the stage” for a further discussion of AI. We have also claimed it is not atopic you can afford to ignore. Although there seems to be no hard, factual results, and perhaps there cannever be since the very words are ill-defined and are open to modification and various interpretations, stillyou must come to grips with it. In particular, when a program is written which does meet some earlierspecification for a reasonable test of computer learning, originality, creativity, or intelligence, then it ispromptly seen by many people the test had a mechanical solution. This is true even if random numbers areinvolved, and given the same test twice the machine will get a solution which differs slightly from the earlierone, much as humans seldom play exactly the same game of chess twice in a row. What is a reasonable,practical test of machine learning? Or are you going to claim, as the earlier cited Jesuit trained engineer did,by definition learning, creativity, originality, and intelligence are what machines cannot do? Or are yougoing to try to hide this blatant statement and conceal it in some devious fashion which does not really alterthe situation?In a sense you will never really grasp the whole problem of AI until you get inside and try your hand atfinding what you mean and what machines can do. Before the checker playing program which learned wasexposed in simple detail, you probably thought machines could not learn from experience—now you mayfeel what was done was not learning but clever cheating, though clearly the program modified its behaviordepending on its experiences. You must struggle with your own beliefs if you are to make any progress inunderstanding the possibilities and limitations of computers in the intellectual area. To do this adequately
46 CHAPTER 6you must formalize your beliefs and then criticize them severely, arguing one side against the other, untilyou have a fair idea of the strengths and weakness of both sides. Most students start out anti-AI; some areproAI; and if you are either one of these then you must try to undo your biases in this important matter. Inthe next chapter we will supply more surprising data on what machines have done, but you must make upyour own mind on this important topic. False beliefs will mean you will not participate significantly in theinevitable and extensive computerization of your organization and society generally. In many senses thecomputer revolution has only begun!
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The Art of Doing Science and Engine
Page 4 and 5: This edition published in the Taylo
Page 6 and 7: 23 Mathematics 16324 Quantum Mechan
Page 8 and 9: The year 2020 seems a convenient da
Page 10 and 11: 1OrientationThe purpose of this cou
Page 12 and 13: ORIENTATION 3Telephone Laboratories
Page 14 and 15: they arise so you will not be left
Page 16 and 17: ORIENTATION 7One of the main tasks
Page 18 and 19: 2Foundations of the Digital (Discre
Page 20 and 21: FOUNDATIONS OF THE DIGITAL (DISCRET
Page 26 and 27: 3History of Computers— HarwareThe
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Page 42 and 43: 5History of Computer ApplicationAs
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Page 52 and 53: ARTIFICIAL INTELLIGENCE—I 43chang
Page 56 and 57: 7Artificial Intelligence—IIIn thi
Page 58 and 59: ARTIFICIAL INTELLIGENCE—II 49mome
Page 60 and 61: ARTIFICIAL INTELLIGENCE—II 51have
Page 62 and 63: ARTIFICIAL INTELLIGENCE—II 53can
Page 64 and 65: 8Artificial Intelligenc—IIII sugg
Page 66 and 67: 9n-Dimensional spaceWhen I became a
Page 68 and 69: N-DIMENSIONAL SPACE 59Figure 9. IIl
Page 70 and 71: N-DIMENSIONAL SPACE 61and hence the
Page 72 and 73: N-DIMENSIONAL SPACE 63where X and Y
Page 74 and 75: N-DIMENSIONAL SPACE 653. D(x,y)=D(y
Page 76 and 77: 10Coding Theory—IHaving looked at
Page 78 and 79: CODING THEORY—I 69is uniquely dec
Page 80 and 81: CODING THEORY—I 71Figure 10.IVFig
Page 82 and 83: CODING THEORY—I 73I feel it neces
Page 84 and 85: CODING THEORY—II 75Figure 11.IFig
Page 86 and 87: CODING THEORY—II 77Figure 11.VThi
Page 88 and 89: CODING THEORY—II 79in this scheme
Page 90 and 91: 12Error Correcting CodesThere are t
Page 92 and 93: ERROR CORRECTING CODES 83Figure 12.
Page 94 and 95: ERROR CORRECTING CODES 85old syndro
Page 96 and 97: ERROR CORRECTING CODES 87mention it
Page 98 and 99: 13Information TheoryInformation The
Page 100 and 101: INFORMATION THEORY 91Figure 13.IThi
Page 102 and 103: INFORMATION THEORY 93Figure 13.IIIc
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INFORMATION THEORY 95Let us critiqu
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14Digital Filters—INow that we ha
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DIGITAL FILTERS—I 99for any other
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DIGITAL FILTERS—I 101Figure 14.IT
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DIGITAL FILTERS—I 103Figure 14.II
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DIGITAL FILTERS—I 105Figure 14.IV
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15Digital Filters— IIWhen digital
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DIGITAL FILTERS—II 109Let us run
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DIGITAL FILTERS—II 111Figure 15.I
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DIGITAL FILTERS—II 113At x=0 this
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16Digital Filters—IIIWe are now r
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DIGITAL FILTERS—III 117Figure 16.
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DIGITAL FILTERS—III 119We next tu
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DIGITAL FILTERS—III 121Figure 16.
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17Digital Filters—IVWe now turn t
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DIGITAL FILTERS—IV 125These recur
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DIGITAL FILTERS—IV 127Figure 17.I
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SIMULATION—I 129Figure 18.IThe fi
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SIMULATION—I 131(though they had
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SIMULATION—I 133Figure 18.IIIinte
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19Simulation—IIWe now take up the
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SIMULATION—II 137The major part o
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SIMULATION—II 139energy or angula
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20Simulation—IIII will continue t
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SIMULATION—III 143Figure 20.IVFig
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SIMULATION—III 145Figure 20.VIdow
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SIMULATION—III 147Figure 20.VIIIW
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SIMULATION—III 149Now to the next
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21Fiber OpticsOne of the reasons fo
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FIBER OPTICS 153Figure 21.IIII did
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FIBER OPTICS 155Figure 21.IVFigure
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22Computer Aided Instruction—CAIB
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COMPUTER AIDED INSTRUCTION—CAI 15
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and they all knew immediately it wa
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23MathematicsAs you live your life
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MATHEMATICS 165Figure 23.IFigure 23
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MATHEMATICS 167illustrates my attit
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MATHEMATICS 169can use the same for
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24Quantum MechanicsMost physicists
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taught QM, no one has found a widel
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QUANTUM MECHANICS 175I do not prete
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CREATIVITY 177I hope the above has
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CREATIVITY 179“memorizing the mat
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26ExpertsAs remarked in an earlier
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EXPERTS 183Figure 26.ILaboratories
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EXPERTS 185Figure 26.IIthe final an
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27Unreliable DataIt has been my exp
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UNRELIABLE DATA 189But how about ba
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UNRELIABLE DATA 191country might ge
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UNRELIABLE DATA 193collection of qu
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28Systems EngineeringParables are o
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SYSTEMS ENGINEERING 197Cramming is
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of traffic performance degrades imm
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SYSTEMS ENGINEERING 201of the natur
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YOU GET WHAT YOU MEASURE 203Figure
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YOU GET WHAT YOU MEASURE 205still o
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YOU GET WHAT YOU MEASURE 207whole o
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30You and Your ResearchI have given
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YOU AND YOUR RESEARCH 211Confidence
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YOU AND YOUR RESEARCH 213know where
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YOU AND YOUR RESEARCH 215believe, i
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INDEX 217Godel’s theorem, 280grow
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