engineering design machine

design for manufacturability, design for assembly or design for production

the process of designing a product for best-fit with the manufacturing system of an organization in order to reduce the problems of bringing a product to market. Design for manufacturability is a team approach to manufacturing that pairs those responsible for the design of a product with those who build it. The manufacturing issues that need to be taken into account in the design process may include using the minimum number of parts, selecting appropriate materials, ease of assembly, and minimizing the number of machine set-ups. Design for manufacturability is one of the elements of concurrent engineering and is sometimes used as a synonym for it.

Also known as engineering for excellence, manufacturing for excellence, producibility engineering

design for manufacturability, design for assembly or design for production

the process of designing a product for best-fit with the manufacturing system of an organization in order to reduce the problems of bringing a product to market. Design for manufacturability is a team approach to manufacturing that pairs those responsible for the design of a product with those who build it. The manufacturing issues that need to be taken into account in the design process may include using the minimum number of parts, selecting appropriate materials, ease of assembly, and minimizing the number of machine set-ups. Design for manufacturability is one of the elements of concurrent engineering and is sometimes used as a synonym for it.

Also known as engineering for excellence, manufacturing for excellence, producibility engineering

design for manufacturability, design for assembly or design for production

the process of designing a product for best-fit with the manufacturing system of an organization in order to reduce the problems of bringing a product to market. Design for manufacturability is a team approach to manufacturing that pairs those responsible for the design of a product with those who build it. The manufacturing issues that need to be taken into account in the design process may include using the minimum number of parts, selecting appropriate materials, ease of assembly, and minimizing the number of machine set-ups. Design for manufacturability is one of the elements of concurrent engineering and is sometimes used as a synonym for it.

Also known as engineering for excellence, manufacturing for excellence, producibility engineering

machine, engine, machinery (attr.); machine-made

машинна обработка machining

машинен бод lock-stitch

машинно отделение engine-room

машинен инженер a mechanical engineer

машинно масло machine/lubricating oil

машинна част piece

машинно чертане engineering drawing

* * *

машѝнен,

прил., -на, -но, -ни machine, machinery (attr.); machine-made; \машиненен бод lock-stitch; \машиненен инженер mechanical engineer; \машиненна обработка machining; \машиненна част piece; \машиненно отделение engine-room; \машиненно проектиране киб. computer-aided design; \машиненно чертане engineering drawing.

* * *

machine: машинен oil - машинно масло; machine-made; mechanical

* * *

1) General subject: Computer-Assisted Design (CAD)

2) Engineering: automated engineering design, computer-aided design, computer-assisted engineering

3) Mathematics: automated, automatic, computer-aided, computer-based, computer-based design, design, or computer-assisted

4) Economy: automated designing

5) Architecture: computer-aided designing (CAD)

6) Telecommunications: computer aided design

7) Electronics: computer-assisted design

8) Information technology: automatized design, computer design, computer-aided design engineering, computer-aided drawing, man-machine design

9) Oil: computer-aided design (cad), design augmented by computer

10) Mechanics: cad-to-part approach, computer-integrated design

11) Business: CAD (computer-aided design), computer-aided design (CAD)

12) Household appliances: automated design

13) Sakhalin energy glossary: CAD computer-aided design, computer-assisted drafting

14) Microelectronics: computer-aided engineering

15) Automation: automated (design) engineering, automated construction, data-driven design

16) Makarov: machine design

m; nur Sg. apparatus engineering, design and manufacture of apparatus

* * *

m no pl

* * *

m kein pl machine-building no pl, instrument-making no pl

* * *

Apparatebau m; nur sg apparatus engineering, design and manufacture of apparatus

1) Engineering: product-oriented machine, purpose-designed machine, single-purpose machine, single-purpose machine tool, special-purpose machine, specialized machine tool, tailored machine

2) Mechanics: machine-tool specialty

3) Automation: custom-build machine, customized machine, dedicated special machine, nonstock machine, process-specialized machine, special design machine, specialist machine, specialized machine, specialty machine, targeted machine, typical machine, process-specific machine

1) Engineering: customized machine tool, special

2) Mechanics: single-purpose machine tool

3) Automation: job-dedicated machine, nonstock machine, process-specialized machine, purpose-built machine, purpose-designed machine, single-purpose machine, special design machine, special-purpose machine, specialist machine, specialized machine, specially outfitted machine tool, specialty machine, typical machine, process-specific machine

4) Makarov: specially outlined machine tool

1) Engineering: batch-produced machine

2) Automation: commercial machine, standard configuration machine, standard design machine

1) Engineering: monotype machine

2) Polygraphy: monotype

3) Yachting: one design

SUBJECT AREA: Electricity, Electronics and information technology, Public utilities

[br]

[br]

[br]

After attending Owens College, Manchester, Hopkinson was admitted to Trinity College, Cambridge, in 1867 to read for the Mathematical Tripos. An appointment in 1872 with the lighthouse department of the Chance Optical Works in Birmingham directed his attention to electrical engineering. His most noteworthy contribution to lighthouse engineering was an optical system to produce flashing lights that distinguished between individual beacons. His extensive researches on the dielectric properties of glass were recognized when he was elected to a Fellowship of the Royal Society at the age of 29. Moving to London in 1877 he became established as a consulting engineer at a time when electricity supply was about to begin on a commercial scale. During the remainder of his life, Hopkinson's researches resulted in fundamental contributions to electrical engineering practice, dynamo design and alternating current machine theory. In making a critical study of the Edison dynamo he developed the principle of the magnetic circuit, a concept also arrived at by Gisbert Kapp around the same time. Hopkinson's improvement of the Edison dynamo by reducing the length of the field magnets almost doubled its output. In 1890, in addition to-his consulting practice, Hopkinson accepted a post as the first Professor of Electrical Engineering and Head of the Siemens laboratory recently established at King's College, London. Although he was not involved in lecturing, the position gave him the necessary facilities and staff and student assistance to continue his researches. Hopkinson was consulted on many proposals for electric traction and electricity supply, including schemes in London, Manchester, Liverpool and Leeds. He also advised Mather and Platt when they were acting as contractors for the locomotives and generating plant for the City and South London tube railway. As early as 1882 he considered that an ideal method of charging for the supply of electricity should be based on a two-part tariff, with a charge related to maximum demand together with a charge for energy supplied. Hopkinson was one the foremost expert witnesses of his day in patent actions and was himself the patentee of over forty inventions, of which the three-wire system of distribution and the series-parallel connection of traction motors were his most successful. Jointly with his brother Edward, John Hopkinson communicated the outcome of his investigations to the Royal Society in a paper entitled "Dynamo Electric Machinery" in 1886. In this he also described the later widely used "back to back" test for determining the characteristics of two identical machines. His interest in electrical machines led him to more fundamental research on magnetic materials, including the phenomenon of recalescence and the disappearance of magnetism at a well-defined temperature. For his work on the magnetic properties of iron, in 1890 he was awarded the Royal Society Royal Medal. He was a member of the Alpine Club and a pioneer of rock climbing in Britain; he died, together with three of his children, in a climbing accident.

[br]

SUBJECT AREA: Mechanical, pneumatic and hydraulic engineering, Metallurgy, Weapons and armour

[br]

[br]

[br]

Joseph Whitworth received his early education in a school kept by his father, but from the age of 12 he attended a school near Leeds. At 14 he joined his uncle's mill near Ambergate, Derbyshire, to learn the business of cotton spinning. In the four years he spent there he realized that he was more interested in the machinery than in managing a cotton mill. In 1821 he obtained employment as a mechanic with Crighton \& Co., Manchester. In 1825 he moved to London and worked for Henry Maudslay and later for the Holtzapffels and Joseph Clement. After these years spent gaining experience, he returned to Manchester in 1833 and set up in a small workshop under a sign "Joseph Whitworth, Tool Maker, from London".

The business expanded steadily and the firm made machine tools of all types and other engineering products including steam engines. From 1834 Whitworth obtained many patents in the fields of machine tools, textile and knitting machinery and road-sweeping machines. By 1851 the company was generally regarded as the leading manufacturer of machine tools in the country. Whitworth was a pioneer of precise measurement and demonstrated the fundamental mode of producing a true plane by making surface plates in sets of three. He advocated the use of the decimal system and made use of limit gauges, and he established a standard screw thread which was adopted as the national standard. In 1853 Whitworth visited America as a member of a Royal Commission and reported on American industry. At the time of the Crimean War in 1854 he was asked to provide machinery for manufacturing rifles and this led him to design an improved rifle of his own. Although tests in 1857 showed this to be much superior to all others, it was not adopted by the War Office. Whitworth's experiments with small arms led on to the construction of big guns and projectiles. To improve the quality of the steel used for these guns, he subjected the molten metal to pressure during its solidification, this fluid-compressed steel being then known as "Whitworth steel".

In 1868 Whitworth established thirty annual scholarships for engineering students. After his death his executors permanently endowed the Whitworth Scholarships and distributed his estate of nearly half a million pounds to various educational and charitable institutions. Whitworth was elected an Associate of the Institution of Civil Engineers in 1841 and a Member in 1848 and served on its Council for many years. He was elected a Member of the Institution of Mechanical Engineers in 1847, the year of its foundation.

[br]

SUBJECT AREA: Electricity, Electronics and information technology, Photography, film and optics

[br]

[br]

[br]

Paul was educated at the City of London School and Finsbury Technical College. He worked first for a short time in the Bell Telephone Works in Antwerp, Belgium, and then in the electrical instrument shop of Elliott Brothers in the Strand until 1891, when he opened an instrument-making business at 44 Hatton Garden, London. He specialized in the design and manufacture of electrical instruments, including the Ayrton Mather galvanometer. In 1902, with a purpose-built factory, he began large batch production of his instruments. He also opened a factory in New York, where uncalibrated instruments from England were calibrated for American customers. In 1903 Paul introduced the Unipivot galvanometer, in which the coil was supported at the centre of gravity of the moving system on a single pivot. The pivotal friction was less than in a conventional instrument and could be used without accurate levelling, the sensitivity being far beyond that of any pivoted galvanometer then in existence.

In 1894 Paul was asked by two entrepreneurs to make copies of Edison's kinetoscope, the pioneering peep-show moving-picture viewer, which had just arrived in London. Discovering that Edison had omitted to patent the machine in England, and observing that there was considerable demand for the machine from show-people, he began production, making six before the end of the year. Altogether, he made about sixty-six units, some of which were exported. Although Edison's machine was not patented, his films were certainly copyrighted, so Paul now needed a cinematographic camera to make new subjects for his customers. Early in 1895 he came into contact with Birt Acres, who was also working on the design of a movie camera. Acres's design was somewhat impractical, but Paul constructed a working model with which Acres filmed the Oxford and Cambridge Boat Race on 30 March, and the Derby at Epsom on 29 May. Paul was unhappy with the inefficient design, and developed a new intermittent mechanism based on the principle of the Maltese cross. Despite having signed a ten-year agreement with Paul, Acres split with him on 12 July 1895, after having unilaterally patented their original camera design on 27 May. By the early weeks of 1896, Paul had developed a projector mechanism that also used the Maltese cross and which he demonstrated at the Finsbury Technical College on 20 February 1896. His Theatrograph was intended for sale, and was shown in a number of venues in London during March, notably at the Alhambra Theatre in Leicester Square. There the renamed Animatographe was used to show, among other subjects, the Derby of 1896, which was won by the Prince of Wales's horse "Persimmon" and the film of which was shown the next day to enthusiastic crowds. The production of films turned out to be quite profitable: in the first year of the business, from March 1896, Paul made a net profit of £12,838 on a capital outlay of about £1,000. By the end of the year there were at least five shows running in London that were using Paul's projectors and screening films made by him or his staff.

Paul played a major part in establishing the film business in England through his readiness to sell apparatus at a time when most of his rivals reserved their equipment for sole exploitation. He went on to become a leading producer of films, specializing in trick effects, many of which he pioneered. He was affectionately known in the trade as "Daddy Paul", truly considered to be the "father" of the British film industry. He continued to appreciate fully the possibilities of cinematography for scientific work, and in collaboration with Professor Silvanus P.Thompson films were made to illustrate various phenomena to students.

Paul ended his involvement with film making in 1910 to concentrate on his instrument business; on his retirement in 1920, this was amalgamated with the Cambridge Instrument Company. In his will he left shares valued at over £100,000 to form the R.W.Paul Instrument Fund, to be administered by the Institution of Electrical Engineers, of which he had been a member since 1887. The fund was to provide instruments of an unusual nature to assist physical research.

[br]

BC / GW

   1) Programs and the Complexity of Human Mental Processes

   In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)

   2) Artificial Intelligence Is an Engineering Discipline

   Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)

   3) A Sceptical View of Artificial Intelligence

   Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....

   When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)

   4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, Eventually

   Just as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   Many problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)

   6) The Meaning of a Symbolic Description

   What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)

   7) The Principle of Artificial Intelligence

   [AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)

   9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract Form

   The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)

    10) There Are Many Types of Reasoning

   There are many different kinds of reasoning one might imagine:

    Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."

    Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)

    12) The Synthesis of Man and Machine

   Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)

    14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory Formation

   It is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological AI

   Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.

   ♦ Weak Psychological AI

   Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.

   ♦ Strong Psychological AI

... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)

   ♦ Suprapsychological AI

   Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)

    16) Determination of Relevance of Rules in Particular Contexts

   Even if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)

    17) Form and Content Are Not Fundamentally Different

   Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)

    18) The Assumption That the Mind Is a Formal System

   Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)

    19) A Statement of the Primary and Secondary Purposes of Artificial Intelligence

   The primary goal of Artificial Intelligence is to make machines smarter.

   The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)

    20) Mathematical Logic Provides the Basis for Theory in AI

   The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....

   AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)

    21) Perceptual Structures Can Be Represented as Lists of Elementary Propositions

   In artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)

    22) Definitions of Artificial Intelligence

   Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)

    23) The Computer Can Not Be a Model of the Mind

   Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)

    24) Computers Will Not Always Be Inferior to Human Brains

   The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)