first-order design

1) Математика: план первого порядка

2) Контроль качества: план (эксперимента) первого порядка

1) дизайн; конструкция

2) замысел, план; проект, проектное решение

3) конструирование; проектирование || конструировать; проектировать

4) конструкция

5) рисунок, эскиз, набросок || делать рисунок, эскиз, набросок

6) составлять план

7) чертёж

8) расчёт || рассчитывать

•

- absolutely minimal design

- accurate design

- admissible design

- affine design

- affine resovable design

- alternate design

- asymmetric design

- asymptotic sequential design

- asymptotically locally optimal design

- balanced incomplete block design

- balanced orthogonal design

- biased design

- binary design

- breadboard design

- central composite design

- changeover design

- civil-engineering design

- combinatorial design

- complementary design

- complete design

- complete factorial design

- completely confounded design

- completely crossed design

- completely hierarchical design

- completely randomized design

- confounded design

- connected designs

- crossed design

- cube-plus-octahedron rotatable design

- cubic lattice design

- cuboidal design

- cut-and-try design

- cyclic design

- cylindrically rotatable design

- design of statistical inquiry

- design of type face

- disconnected design

- discriminating design

- double-buttion design

- doubly balanced design

- doubly overlap design

- elementary design

- equilateral triangle design

- equireplicated designs

- first-order design

- fractional factorial design

- fractional nested design

- fractional weighing design

- fractionally replicated design

- generalized chain block design

- generally balanced design

- geometrical design

- group divisible design

- half-factorial design

- half-replicated design

- hexagonal design

- hierarchical design

- hypercubic design

- ice-gouging design

- icosahedral design

- incomplete split plot design

- intragroup balanced design

- isomorphic block designs

- landscape design

- lattice design

- linked-block design

- magic design

- minimal design

- minimax design

- mixed design

- multiply balanced design

- multiresponse design

- multivariate design

- noncentral composite design

- nonfactorial design

- nonorthogonal design

- nonrotatable design

- nonsaturated design

- nonsymmetrical design

- one-factor-at-a-time design

- one-stage design

- orthogonal composite design

- paired-comparison design

- pairwise-balanced designs

- partially balanced design

- partially confounded design

- partially duplicated design

- pentagonal design

- permutable design

- pseudotriangular design

- purely hierarchical design

- quasifactorial design

- quasioptimal design

- quasiorthogonal designs

- quasiresidual design

- quasisymmetrical design

- radial centroid design

- radial lattice design

- randomized design

- regular design

- replicated nested design

- response surface design

- right-angular design

- roll pass design

- row-and-column design

- screening design

- second-order design

- self-corrective design

- semibalanced design

- semiregular design

- sequential design

- simplex design

- simplex-centroid design

- simplex-lattice design

- simplex-symmetric design

- simplified design

- singly-linked block design

- singular design

- six-point design

- smooth design

- spherical design

- split-plot design

- split-split-plot design

- square design

- staircase design

- standard design

- steepest ascent design

- strongly regular incomplete multiresponse design

- supersaturated design

- survey design

- switch-back design

- system design

- tetrahedral design

- three-factor design

- totally confounded design

- tournamental design

- transversal design

- tri-factorial design

- truncated design

- two-associate design

- two-stage design

- typographic design

- ultimate load design

- unbalanced design

- unbiased design

- unconventional design

- unit-type design

- unreduced design

- unsymmetrical design

- variance-balanced design

- wallpaper design

- weighed design

- weighing design

1. проект; конструкция; схема <ЛА>/ проектный

2. проектирование; разработка; конструирование; синтез/ проектировать; разрабатывать; конструировать; синтезировать

3. расчет/ рассчитывать/ расчетный

см. тж. design

"clean-sheet-of-paper" design

design for low vibration

design for supportability

design to constraints

design to cost

aerodynamic design

aerodynamically unstable design

aircraft design

airfoil design

all-altitude design

all-wing design

amphibious design

analytical design

arrow-wing design

attached-flow design

augmentation design

autopilot design

avionics design

balanced design

baseline design

bearingless design

canard design

cockpit design

compensator design

computerized design

conceptual design

configuration design

constrained design

control design

control augmentation design

control law design

controller design

crew station design

CTOL design

damage tolerant design

decoupled design

delta-wing design

delta-winged design

detail design

detailed design

display design

double-scoop design

durability design

elastic design

fail-safe design

fallback design

fatigue design

feedback design

feedback loop design

fighter design

filter design

final production design

fixed gain design

fixed-sweep design

flutter design

flying knife design

flying-wing design

fracture mechanics design

frequency domain design

full-state design

full-scale design

fully-stressed design

handling-qualities design

high-tail design

high-wing design

hydrodynamic design

in-house design

inelastic design

inlet design

input design

iterative design

joined-wing design

laminate design

land based design

landing gear design

layout design

least-mass design

Lyapunov design

lift-plus-cruise design

linear regulator design

logic design

logistics design

long-nosed design

longitudinal design

low-boom design

low-cost design

low-wing design

LQG/LTR design

LQR design

macro-structural design

micro-structural design

minimum weight design

multiple-input-multiple-output design

nonrobust design

oblique-wing design

observer design

observer-based design

optimum design

outboard-pivot design

pitch design

podded design

point design

powered-lift design

preliminary design

propeller design

RALS design

reduced order design

reliability-conscious design

robust design

roll-yaw design

rotorcraft design

sensitivity-reduction design

shape optimal design

single-finned design

single-seat design

stealth design

STOVL design

strengthened design

structural design

support-conscious design

swing-wing design

system design

T-tail design

tail-aft design

tail-first design

tandem-wing design

thermoelastic design

three-shaft design

three-surface design

to design in

torsional design

twin-boom design

twin-pod design

two-shaft design

two-spool design

unconstrained design

undercarriage design

vortex-flow design

VTOL design

washin design

washout design

wheel design

wide-body design

Wiener-Hopf design

wing-winglet design

соц. характер [структура, постановка, формулировка\] вопроса

SUBJECT AREA: Electricity, Ports and shipping

[br]

[br]

[br]

The youngest son of the Earl of Rosse, he came from a family well known in scientific circles, the six boys growing up in an intellectual atmosphere at Birr Castle, the ancestral home in Ireland, where a forge and large workshop were available to them. Charles, like his brothers, did not go to school but was educated by private tutors of the character of Sir Robert Ball, this type of education being interspersed with overseas holiday trips to France, Holland, Belgium and Spain in the family yacht. In 1871, at the age of 17, he went to Trinity College, Dublin, and after two years he went on to St John's College, Cambridge. This was before the Engineering School had opened, and Parsons studied mechanics and mathematics.

In 1877 he was apprenticed to W.G.Armstrong \& Co. of Elswick, where he stayed for four years, developing an epicycloidal engine that he had designed while at Cambridge. He then moved to Kitson \& Co. of Leeds, where he went half shares in a small experimental shop working on rocket propulsion for torpedoes.

In 1887 he married Katherine Bethell, who contracted rheumatic fever from early-morning outdoor vigils with her husband to watch his torpedo experiments while on their honeymoon! He then moved to a partnership in Clarke, Chapman \& Co. at Gateshead. There he joined the electrical department, initially working on the development of a small, steam-driven marine lighting set. This involved the development of either a low-speed dynamo, for direct coupling to a reciprocating engine, or a high-speed engine, and it was this requirement that started Parsons on the track of the steam turbine. This entailed many problems such as the running of shafts at speeds of up to 40,000 rpm and the design of a DC generator for 18,000 rpm. He took out patents for both the turbine and the generator on 23 April 1884. In 1888 he dissolved his partnership with Clarke, Chapman \& Co. to set up his own firm in Newcastle, leaving his patents with the company's owners. This denied him the use of the axial-flow turbine, so Parsons then designed a radial-flow layout; he later bought back his patents from Clarke, Chapman \& Co. His original patent had included the use of the steam turbine as a means of marine propulsion, and Parsons now set about realizing this possibility. He experimented with 2 ft (61 cm) and 6 ft (183 cm) long models, towed with a fishing line or, later, driven by a twisted rubber cord, through a single-reduction set of spiral gearing.

The first trials of the Turbinia took place in 1894 but were disappointing due to cavitation, a little-understood phenomenon at the time. He used an axial-flow turbine of 2,000 shp running at 2,000 rpm. His work resulted in a far greater understanding of the phenomenon of cavitation than had hitherto existed. Land turbines of up to 350 kW (470 hp) had meanwhile been built. Experiments with the Turbinia culminated in a demonstration which took place at the great Naval Review of 1897 at Spithead, held to celebrate Queen Victoria's Diamond Jubilee. Here, the little Turbinia darted in and out of the lines of heavy warships and destroyers, attaining the unheard of speed of 34.5 knots. The following year the Admiralty placed their first order for a turbine-driven ship, and passenger vessels started operation soon after, the first in 1901. By 1906 the Admiralty had moved over to use turbines exclusively. These early turbines had almost all been direct-coupled to the ship's propeller shaft. For optimum performance of both turbine and propeller, Parsons realized that some form of reduction gearing was necessary, which would have to be extremely accurate because of the speeds involved. Parsons's Creep Mechanism of 1912 ensured that any errors in the master wheel would be distributed evenly around the wheel being cut.

Parsons was also involved in optical work and had a controlling interest in the firm of Ross Ltd of London and, later, in Sir Howard Grubb \& Sons. He he was an enlightened employer, originating share schemes and other benefits for his employees.

[br]

1) изменение; смена; замена || изменять(ся); сменять(ся); заменять(ся)

2) переход; превращение; трансформация; модификация || переходить; превращать(ся); трансформировать(ся); модифицировать(ся)

3) обмен; перестановка || обменивать(ся); переставлять(ся)

•

- change of state

1) изменение; смена; замена || изменять(ся); сменять(ся); заменять(ся)

2) переход; превращение; трансформация; модификация || переходить; превращать(ся); трансформировать(ся); модифицировать(ся)

3) обмен; перестановка || обменивать(ся); переставлять(ся)

•

- address change

1) фаза

- in condition

- in working condition

- out of condition

- improve the working conditions

- under production conditions

- condition of entry

- conditions of payment

- conditions of supply

- abnormal operating conditions

- actual operating conditions

- as-received condition

- average operating conditions

- average service conditions

- best-profit condition

- boom conditions

- boundary condition

- business conditions

- cleared conditions

- credit condition

- crowded conditions

- design conditions

- emergency condition

- end-service condition

- end-use condition

- equilibrium conditions

- extreme conditions

- fair condition

- fat condition

- faulty condition

- feasibility conditions

- first-order conditions

- flock condition

- general policy conditions

- high condition

- housing conditions

- humble conditions of life

- intensive conditions

- Kuhn-Tucker conditions

- lean condition

- license conditions

- limiting condition

- low-duty operating conditions

- maintenance conditions

- marital condition

- market condition

- market-clearing condition

- medium operating conditions

- mild-duty operating conditions

- minimax condition

- minimum condition

- moderate operating conditions

- nominal conditions

- operable condition

- operating condition

- operation condition

- operative condition

- optimality condition

- out-of-stock condition

- peak condition

- plant conditions

- profit maximizing condition

- profit-maximizing employment condition

- queue condition

- quiet conditions

- range conditions

- rated conditions

- readiness condition

- reference conditions

- regular service conditions

- running conditions

- saturation condition

- second-order conditions

- septic condition

- service conditions

- serviceable condition

- severe operation conditions

- shop conditions

- show condition

- side condition

- sociodemographic conditions

- special policy conditions

- specified operating conditions

- standard policy conditions

- standby condition

- start conditions

- storage conditions

- surplus conditions

- top condition

- usable condition

- use conditions

- waterlogged conditions

- working conditions

- workshop conditions

- worst-on-worst operating conditions

1. n

1) состояние, положение

2) pl обстоятельства, условия, обстановка; режим

3) условие, оговорка

4) общественное положение

2. v

1) обусловливать

2) приводить в надлежащее состояние

1) уравнение

2) равенство

•

- algebraic equation

1. решение

2. раствор

aeroelastic solution

approximate solution

boundary element solution

closed-form solution

closed-loop solution

compressible solution

cone solution

conforming solution

control solution

crack solution

design solution

eigenproblem solution

energy management solution

Euler solution

fast solution

finite difference solution

finite-element solution

firing solution

first-order solution

flutter solution

free-vortex sheet solution

full-potential solution

game solution

gradient solution

guidance solution

hybrid-element solution

implicit solution

least-squares solution

level-flight solution

lifting-line solution

low-speed solution

minimum flight-time solution

minimum time solution

multigrid solution

Navier-Stokes solution

Newton-Raphson solution

nonconforming solution

nonunique solution

numerical solution

optimal control solution

overlaid solution

perturbation solution

relaxation solution

rigid body solution

saddle-point solution

semisimilar solution

shock layer solution

similarity solution

strip solution

traveling wave solution

trial solution

trim solution

upwind solution

V-g flutter solution

vortex solution

weight solution

SUBJECT AREA: Automotive engineering, Civil engineering, Land transport, Railways and locomotives

[br]

[br]

[br]

Little is known of Shaw's youth, but in the 1930s he was running a comparatively successful business repairing roads. One evening in 1933, he was driving to his home in Halifax, West Yorkshire; it was late, dark and foggy and only the reflection of his headlights from the tram-tracks guided him and kept him on the road. He decided to find or make an alternative to tramlines, which were not universal and by that time were being taken up as trams were being replaced with diesel buses.

Shaw needed a place to work and bought the old Boothtown Mansion, a cloth-merchant's house built in the mid-eighteenth century. There he devoted himself to the production of a prototype of the reflecting roadstud, inspired by the reflective nature of a cat's eyes. Shaw's design consisted of a prism backed by an aluminium mirror, set in pairs in a rubber casing; when traffic passed over the stud, the prisms would be wiped clean as the casing was depressed. In 1934, Shaw obtained permission from the county surveyor to lay, at his own expense, a short stretch of catseyes on a main highway near his home: fifty were laid at Brightlington cross-roads, an accident blackspot near Bradford. This was inspected by a number of surveyors in 1936. The first order for catseyes had already been placed in 1935, for a pedestrian crossing in Baldon, Yorkshire. There were alternative designs in existence, particularly in France, and in 1937 the Ministry of Transport laid an 8 km (5 mile) stretch in Oxfordshire with sample lengths of different types of studs. After two years, most of them had fractured, become displaced or ceased to reflect; only the product of Shaw's company, Reflecting Roadstuds Ltd, was still in perfect condition. The outbreak of the Second World War brought blackout regulations, which caused a great boost to sales of reflecting roadstuds; orders reached some 40,000 per week. Production was limited, however, due to the shortage of rubber supplies after the Japanese overran South-East Asia; until the end of the war, only about 12,000 catseyes were produced a year.

Over fifty million catseyes have been installed in Britain, where on average there are about two hundred and fifty catseyes in each kilometre of road, if laid in a single line. The success of Shaw's invention brought him great wealth, although he continued to live in the same house, without curtains—which obstructed his view—or carpets—which harboured odours and germs. He had three Rolls-Royce cars, and four television sets which were permanently switched on while he was at home, each tuned to a different channel.

[br]

   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)