computational+learning

1) метод

2) методика

3) приём

4) процедура

5) способ

6) техника

7) технология

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- access technique

- adaptive prediction technique

- approximation technique

- artificial basis technique

- asymptotic technique

- bounded variable technique

- bounded variable technique

- calculation technique

- combinatorial technique

- computational technique

- conjugate gradient technique

- consensus technique

- control chart technique

- decision-making technique

- decomposition technique

- decomposition-rejection technique

- discretization technique

- distribution-free technique

- dualization technique

- enumerative technique

- equipotential technique

- evaluation technique

- evolutionary operation technique

- experimental technique

- extrapolation technique

- fixed-point technique

- folding technique

- general technique

- gradient technique

- heuristic technique

- hill-climbing technique

- hot-wire technique

- interactive technique

- interpolation technique

- invariant embedding technique

- iterative technique

- jack-knife technique

- launching technique

- learning technique

- linearization technique

- linear-programming technique

- locus technique

- mathematical technique

- matrix technique

- maximum-likelihood technique

- measuring technique

- minimization technique

- missing plot technique

- mix-and-match technique

- mode selecting technique

- model-building technique

- mode-locking technique

- moving-observer technique

- nonanalytic technique

- nonparametric technique

- nonquantitative technique

- numerical technique

- omnibus technique

- operator technique

- optimal search technique

- optimization technique

- orthogonalization technique

- perturbation technique

- post-alloy-diffusion technique

- prediction technique

- problem-solving technique

- proof technique

- pulse compression technique

- radioautograph technique

- randomization technique

- recursive technique

- regression technique

- response surface technique

- saddle-point technique

- scan-set technique

- schlieren technique

- search technique

- seeding technique

- semicontinuous technique

- sequential unconstrained minimization technique

- short-cut technique

- shuffling technique

- simplex technique

- simulation technique

- smoothing technique

- solution technique

- spline surface technique

- standardized technique

- statistical technique

- stochastic approximation technique

- technique of estimation

- technique of least squares

- technique of substitution

- test technique

- trial-and-error technique

- variance-reduction technique

- variational technique

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abstract model абстрактная модель abstract model building вчт. абстрактное моделирование allocation model модель распределения analytical model аналитическая модель associative model ассоциативная модель autonomous model автономная модель autoregressive model авторегрессионная модель backlogging model модель с задалживанием роста заказов battle model модель боя behavioral model модель поведения binomial model биномиальная модель binomial model биномиальное распределение clay-clay model жесткая модель closed model замкнутая модель coalition model модель коалиции cobweb model паутинообразная модель cognitive model когнитивная модель communication model модель общения computational model вычислительная модель computer model машинная модель conceptual model концептуальная модель cyclic queueing model вчт. циклическая модель массового обслуживания data model вчт. модель данных decision model модель принятия решений decision-theory model модель выбора решений decision-theory model модель принятия решений double-risk model модель с двойным риском dynamic model динамическая модель dynamic programming model вчт. модель динамического программирования econometric model эконометрическая модель entity-relationship model модель типа объект-отношение equilibrium model модель равновесия estimation model модель оценивания explaining model поясняющая модель finite-horizon model модель с конечным интервалом fixed-horizon model модель с постоянным интервалом fixed-service-level model модель с фиксированным уровнем обслуживания formal model формальная модель game model игровая модель game-theory model теоретико-игровая модель general duel model общая модель дуэли generalized model обобщенная модель generic model типовая модель global model глобальная модель imaging model модель изображений interindustry programming model вчт. межотраслевая модель программирования interruption model модель с возможностью прерывания обслуживания knowledge model вчт. модель знаний labyrinth model лабиринтная модель language model модель языка learning model модель обучения linear model линейная модель linear programming model модель линейного программирования linear regressive model линейный регрессионная модель linguistic model лингвистическая модель logical model логическая модель logical-linguistic model логико-лингвистическая модель macrosectoral model макроотраслевая модель many-server model вчт. многоканальная модель master-workers model модель хозяин-работники matrix model матричная модель model быть натурщиком, натурщицей, живой моделью, манекенщицей model живая модель (в магазине одежды) model макет model манекен model моделировать; лепить model модель, макет; шаблон model модель model натурщик; натурщица model образец, эталон model образец model attr. образцовый, примерный model оформлять model примерный, типовой (о конвенции, уставе и т.д.) model создавать по образцу (чего-л.; after, on); to model oneself ((up)on smb.) брать (кого-л.) за образец model тип model разг. точная копия model тех. формировать model шаблон model создавать по образцу (чего-л.; after, on); to model oneself ((up)on smb.) брать (кого-л.) за образец moving-average model модель скользящего среднего multichannel priority model вчт. многоканальная модель с приоритетами multifactor model многофакторная модель multiple model многоуровневая модель multistation queueing model вчт. многоканальная модель обслуживания network model сетевая модель no-backlog model модель без задалживания заказов no-queue model модель без образования очереди non-poisson model непуассоновская модель one-factor model однофакторная модель one-period model однопериодная модель open model открытая модель open model разомкнутая модель operations research model модель исследования операций phenomenological model феноменологическая модель pictorial model графическая модель pilot model опытный образец pilot: model plant опытный завод, опытная установка; pilot model опытная модель poisson model пуассоновская модель predicitive model прогнозирующая модель preference model модель предпочтений priority model модель с приоритетами probability model вероятностная модель probability model стохастическая модель production model производственная модель prognostic model прогностическая модель queueing model модель массового обслуживания queueing model модель очереди random model вероятностная модель random model стохастическая модель reduced model упрощенная модель regression model регрессионная модель relational model реляционная модель scaling model шкальная модель security model модель механизма защиты semi-poisson model полупуассоновская модель shortest-route model модель выбора кратчайшего пути sign model знаковая модель simplex model симплексная модель simulation model имитационная модель single-channel model одноканальная модель single-period model однопериодная модель single-phase model однофазовая модель single-server model одноканальная модель singular model одноуровневая модель software model вчт. программная модель solid model объемная модель sophisticated model усложненная модель standard model типовая модель static equilibrium model модель статического равновесия static inventory model статическая модель управления запасами static model статическая модель station-to-station model многошаговая модель stochastic model вероятностная модель teaching model учебная модель (машины, оборудования) three-dimensional model трехмерная модель transportation model транспортная задача transshipment model модель перевозок с промежуточными пунктами trend-free model модель с отсутствием тренда trial model испытательный образец trial model пробный образец two-echelon model двухступенчатая модель two-state model модель с двумя состояниями user model модель пользователя waiting line model модель очереди wire-frame model каркасная модель world decision model всеобщая модель решений world model модель мира

   1) A Comparison of the Digital Computer and the Brain

   The brain has been compared to a digital computer because the neuron, like a switch or valve, either does or does not complete a circuit. But at that point the similarity ends. The switch in the digital computer is constant in its effect, and its effect is large in proportion to the total output of the machine. The effect produced by the neuron varies with its recovery from [the] refractory phase and with its metabolic state. The number of neurons involved in any action runs into millions so that the influence of any one is negligible.... Any cell in the system can be dispensed with.... The brain is an analogical machine, not digital. Analysis of the integrative activities will probably have to be in statistical terms. (Lashley, quoted in Beach, Hebb, Morgan & Nissen, 1960, p. 539)

   2) Computers Do Not Crunch Numbers, They Manipulate Symbols

   It is essential to realize that a computer is not a mere "number cruncher," or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols.... Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines....

   The terms "computer" and "computation" are themselves unfortunate, in view of their misleading arithmetical connotations. The definition of artificial intelligence previously cited-"the study of intelligence as computation"-does not imply that intelligence is really counting. Intelligence may be defined as the ability creatively to manipulate symbols, or process information, given the requirements of the task in hand. (Boden, 1981, pp. 15, 16-17)

   3) Getting Computers to Explain Things to Themselves

   The task is to get computers to explain things to themselves, to ask questions about their experiences so as to cause those explanations to be forthcoming, and to be creative in coming up with explanations that have not been previously available. (Schank, 1986, p. 19)

   4) Some Limits of Artificial Intelligence

   In What Computers Can't Do, written in 1969 (2nd edition, 1972), the main objection to AI was the impossibility of using rules to select only those facts about the real world that were relevant in a given situation. The "Introduction" to the paperback edition of the book, published by Harper & Row in 1979, pointed out further that no one had the slightest idea how to represent the common sense understanding possessed even by a four-year-old. (Dreyfus & Dreyfus, 1986, p. 102)

   5) The Computer as a Humanizing Influence

   A popular myth says that the invention of the computer diminishes our sense of ourselves, because it shows that rational thought is not special to human beings, but can be carried on by a mere machine. It is a short stop from there to the conclusion that intelligence is mechanical, which many people find to be an affront to all that is most precious and singular about their humanness.

   In fact, the computer, early in its career, was not an instrument of the philistines, but a humanizing influence. It helped to revive an idea that had fallen into disrepute: the idea that the mind is real, that it has an inner structure and a complex organization, and can be understood in scientific terms. For some three decades, until the 1940s, American psychology had lain in the grip of the ice age of behaviorism, which was antimental through and through. During these years, extreme behaviorists banished the study of thought from their agenda. Mind and consciousness, thinking, imagining, planning, solving problems, were dismissed as worthless for anything except speculation. Only the external aspects of behavior, the surface manifestations, were grist for the scientist's mill, because only they could be observed and measured....

   It is one of the surprising gifts of the computer in the history of ideas that it played a part in giving back to psychology what it had lost, which was nothing less than the mind itself. In particular, there was a revival of interest in how the mind represents the world internally to itself, by means of knowledge structures such as ideas, symbols, images, and inner narratives, all of which had been consigned to the realm of mysticism. (Campbell, 1989, p. 10)

   6) The Intentionality of Computers Is Essentially Borrowed, Hence Derivative

   [Our artifacts] only have meaning because we give it to them; their intentionality, like that of smoke signals and writing, is essentially borrowed, hence derivative. To put it bluntly: computers themselves don't mean anything by their tokens (any more than books do)-they only mean what we say they do. Genuine understanding, on the other hand, is intentional "in its own right" and not derivatively from something else. (Haugeland, 1981a, pp. 32-33)

   7) The Possibility of Computer Thought

   he debate over the possibility of computer thought will never be won or lost; it will simply cease to be of interest, like the previous debate over man as a clockwork mechanism. (Bolter, 1984, p. 190)

   8) We Are Getting Better at Building Even Better Computers, an Ever- Escalating Upward Spiral

   t takes us a long time to emotionally digest a new idea. The computer is too big a step, and too recently made, for us to quickly recover our balance and gauge its potential. It's an enormous accelerator, perhaps the greatest one since the plow, twelve thousand years ago. As an intelligence amplifier, it speeds up everything-including itself-and it continually improves because its heart is information or, more plainly, ideas. We can no more calculate its consequences than Babbage could have foreseen antibiotics, the Pill, or space stations.

   According to weak AI, the principle value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion than before. But according to strong AI the computer is not merely a tool in the study of the mind; rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states. And according to strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations. (Searle, 1981b, p. 353)

    10) Why People Are Smarter Than Computers

   What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations, at understanding language and retrieving contextually appropriate information from memory, at making plans and carrying out contextually appropriate actions, and at a wide range of other natural cognitive tasks. People are also far better at learning to do these things more accurately and fluently through processing experience.

   What is the basis for these differences? One answer, perhaps the classic one we might expect from artificial intelligence, is "software." If we only had the right computer program, the argument goes, we might be able to capture the fluidity and adaptability of human information processing. Certainly this answer is partially correct. There have been great breakthroughs in our understanding of cognition as a result of the development of expressive high-level computer languages and powerful algorithms. However, we do not think that software is the whole story.

   In our view, people are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at.... hese tasks generally require the simultaneous consideration of many pieces of information or constraints. Each constraint may be imperfectly specified and ambiguous, yet each can play a potentially decisive role in determining the outcome of processing. (McClelland, Rumelhart & Hinton, 1986, pp. 3-4)