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1 path synthesis
Автоматика: синтез траектории (по опорным точкам) -
2 path synthesis
English-Russian dictionary of mechanical engineering and automation > path synthesis
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3 multiple-path synthesis
Общая лексика: многовариантный синтезУниверсальный англо-русский словарь > multiple-path synthesis
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4 synthesis
в соч.- Chebyshev synthesis of mechanism
- dynamic synthesis
- interpolative synthesis
- kinematic synthesis
- least-square synthesis
- motion synthesis
- optimization synthesis
- path synthesis
- precision-point synthesis
- speech synthesis
- structural synthesis
- structure synthesis
- synthesis of mechanism
- top-down synthesis
- type synthesisEnglish-Russian dictionary of mechanical engineering and automation > synthesis
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5 multiple-path
[͵mʌltıp(ə)lʹpɑ:θ] a спец.многовариантный, множественныйmultiple-path analysis [synthesis] - многовариантный анализ [синтез]
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6 algorithm
- ad hoc algorithm
- adaptive algorithm
- aim algorithm
- algorithm for path connections
- automatic assessment algorithm
- back-propagation algorithm
- banker's algorithm
- best-route algorithm
- bicomponent algorithm
- bipartitioning algorithm
- bisection algorithm
- branch-bound algorithm
- branching algorithm
- cascading algorithm
- chain algorithm
- channel router algorithm
- coarse-to-fine algorithm
- column sweep algorithm
- combinatorial algorithm
- computational algorithm
- computing algorithm
- conservative algorithm
- control algorithm
- convergent algorithm
- curve-fitting algorithm
- cutting-plane algorithm
- D-algorithm
- decision algorithm
- decoding algorithm
- demand-paging algorithm
- deterministic algorithm
- digit-by-digit algorithm
- divide-and-conquer algorithm
- double-sweep algorithm
- draphics algorithm
- drawing algorithm
- DSP algorithm
- dual algorithm
- durable algorithm
- earliest-deadline-first algorithm
- edge-based algorithm
- event-scheduling algorithm
- exchange algorithm
- fault-handling algorithm
- fine-to-coarse algorithm
- fixed-stealing algorithm
- fixed-step-size algorithm
- flow-synthesis algorithm
- forward-looking algorithm
- generalized algorithm
- genetic algorithm
- Goto algorithm
- graph algorithm
- graph traversal algorithm
- greedy algorithm
- grid expansion algorithm
- hardware algorithm
- heuristic algorithm
- Hightower algorithm
- incorrect algorithm
- inference-based algorithm
- inferencing algorithm
- instruction issue algorithm
- integer algorithm
- integrated query optimization algorithm
- iteration algorithm
- iterative algorithm
- layout algorithm
- layout copmaction algorithm
- leaky bucket algorithm
- learning algorithm
- least frequently used algorithm
- least recently used algorithm
- Lee-expansion algorithm
- Lee-type algorithm
- levelization algorithm
- linear expansion algorithm
- line-placing algorithm
- line-probe algorithm
- logical algorithm
- mathematically based algorithm
- mathematically intensive algorithm
- maximum matching algorithm
- mesh algorithm - minimum path-length algorithm
- modeling algorithm
- multikey algorithm
- multipass algorithm
- nested algorithm
- network algorithm
- normal algorithm
- no-wait algorithm
- operative algorithm
- optimal assignment algorithm
- optimal cutting algorithm
- ordering algorithm
- page-replacement algorithm
- paging algorithm
- parallel algorithm
- partitioning algorithm
- path-tracing algorithm
- pipeline algorithm
- pitch algorithm
- placement algorithm
- prediction algorithm
- primal-dual algorithm
- primary algorithm
- problem algorithm
- procrastination algorithm
- public-key algorithm
- quorum-based algorithm
- random search algorithm
- recognition algorithm
- recursive algorithm
- relaxation algorithm
- replicate algorithm
- robust algorithm
- round-robin algorithm
- routing algorithm
- scanline algorithm
- scheduling algorithm
- sequential algorithm
- shortest path algorithm
- shrinking algorithm
- simplex algorithm
- simulated annealing algorithm
- software algorithm
- spanning tree algorithm
- speech generation algorithm
- speed-enhancing algorithm
- square rooting algorithm
- steepest ascent algorithm
- systolic algorithm
- testing algorithm
- text-to-speech algorithm
- threshold decoding algorithm
- timetable scheduling algorithm
- trace back algorithm
- translation algorithm
- transportation algorithm
- tree-search algorithm
- two-dimensional placement algorithm
- two-list algorithm
- type-inferencing algorithm
- unconstrained minimization algorithm
- universal algorithm
- variable-stealing algorithm
- Vintr algorithm
- VLSI algorithm
- write-back algorithmEnglish-Russian dictionary of computer science and programming > algorithm
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7 algorithm
1) алгоритм2) правило; процедура; метод•- adaptive algorithm
- Agarval-Cooley algorithm
- aim algorithm
- annealing algorithm
- asymmetric encryption algorithm
- autoregressive algorithm
- backoff algorithm
- back propagation of error algorithm
- batch algorithm
- Bellman-Ford algorithm
- best cost path algorithm
- bisection algorithm
- branch and bound algorithm - CART algorithm
- channel algorithm
- channel routing algorithm
- Cholesky algorithm
- classification and regression tree algorithm
- clustering algorithm
- Cochran-Orcutt algorithm
- compression algorithm
- conjugate directions algorithm
- conjugate gradients algorithm
- constructive algorithm
- contour following algorithm
- control algorithm
- convolution algorithm
- Cooley-Tuckey algorithm
- cryptographic algorithm
- deflation compression algorithm
- differential synthesis algorithm
- Diffie-Hellman algorithm
- digital signal processing algorithm
- digital signature algorithm
- Dijkstra algorithm
- distance vector algorithm
- DSP algorithm
- dynamic programming algorithm
- edge-tracking algorithm
- evolutionary algorithm
- exact embedding algorithm
- expansion algorithm
- fast algorithm
- fast convolution Agarval-Cooley algorithm
- fixed-weight algorithm
- Ford-Fulkerson algorithm
- fundamental algorithm
- fuzzy algorithm
- general algorithm - greedy algorithm
- Gummel's algorithm
- hashing algorithm
- heuristic algorithm
- ID3 algorithm
- identification algorithm
- initialization algorithm
- integer algorithm - iterative dichotomizer 3 algorithm
- k-means algorithm
- learning algorithm
- least frequently used algorithm
- least recently used algorithm
- Lee-Moore algorithm
- Lee algorithm
- Lee-type algorithm
- Lempel-Ziv algorithm
- Lempel-Ziv-Welch algorithm
- Levenberg-Marquardt algorithm
- LFU algorithm
- line-probe algorithm
- link state algorithm
- LU decomposition algorithm
- LZ algorithm
- LZW algorithm
- MacQueen's k-means algorithm
- McCulloch-Pitts algorithm
- memetic algorithm - min-cut algorithm - nested algorithm
- numerical algorithm
- Oja iterative algorithm
- on-line algorithm
- optimization algorithm
- ordering algorithm
- painter's algorithm
- parallel algorithm
- pattern classification algorithm
- pattern recognition algorithm
- pel-recursive estimation algorithm
- placement algorithm
- planning algorithm
- polynomial algorithm
- predictive algorithm
- preemptive algorithm
- Prim algorithm
- production rule based algorithm
- pruning algorithm
- pseudo least recently used algorithm
- quantum search algorithm
- quick-union algorithm
- quick-union algorithm with path compression
- Q-R-algorithm
- radix sorting algorithm
- randomized algorithm
- rank algorithm
- Read-Solomon cyclic redundancy compression algorithm
- recognition algorithm
- recurrent algorithm
- recursive algorithm
- resilient propagation algorithm - routing algorithm
- Rprop algorithm
- RSA algorithm
- scheduling algorithm
- search algorithm
- search network algorithm - self-organizing algorithm
- semi-numerical algorithm
- sequential algorithm - shortest path algorithm
- sieving algorithm
- simulated annealing training algorithm
- simulation algorithm
- smoothing algorithm
- software algorithm
- spanning tree algorithm
- spline algorithm
- splitting algorithm
- stack algorithm
- statistical algorithm
- stochastic algorithm
- supervised training algorithm
- symmetric encryption algorithm
- time-wheel algorithm
- training algorithm
- universal algorithm
- unsupervised training algorithm
- van der Waerden algorithm
- variational algorithm
- vector distance algorithm
- Viterbi algorithm
- VLSI algorithm
- weighted quick-union algorithm
- working algorithm -
8 algorithm
1) алгоритм2) правило; процедура; метод•- Agarval-Cooley algorithm
- aim algorithm
- algorithm of doubtful convergence
- annealing algorithm
- asymmetric encryption algorithm
- autoregressive algorithm
- back propagation of error algorithm
- backoff algorithm
- batch algorithm
- Bellman-Ford algorithm
- best cost path algorithm
- bisection algorithm
- branch and bound algorithm
- British Telecom Lempel-Ziv algorithm
- BTLZ algorithm
- CART algorithm
- channel algorithm
- channel routing algorithm
- Cholesky algorithm
- classification and regression tree algorithm
- clustering algorithm
- Cochran-Orcutt algorithm
- compression algorithm
- conjugate directions algorithm
- conjugate gradients algorithm
- constructive algorithm
- contour following algorithm
- control algorithm
- convolution algorithm
- Cooley-Tuckey algorithm
- cryptographic algorithm
- deflation compression algorithm
- differential synthesis algorithm
- Diffie-Hellman algorithm
- digital signal processing algorithm
- digital signature algorithm
- Dijkstra algorithm
- distance vector algorithm
- DSP algorithm
- dynamic programming algorithm
- edge-tracking algorithm
- evolutionary algorithm
- exact embedding algorithm
- expansion algorithm
- fast algorithm
- fast convolution Agarval-Cooley algorithm
- fixed-weight algorithm
- Ford-Fulkerson algorithm
- fundamental algorithm
- fuzzy algorithm
- general algorithm
- genetic algorithm
- graph search algorithm
- greedy algorithm
- Gummel's algorithm
- hashing algorithm
- heuristic algorithm
- ID3 algorithm
- identification algorithm
- initialization algorithm
- integer algorithm
- international data encryption algorithm
- iterative algorithm
- iterative dichotomizer 3 algorithm
- k-means algorithm
- learning algorithm
- least frequently used algorithm
- least recently used algorithm
- Lee-algorithm
- Lee-Moore algorithm
- Lee-type algorithm
- Lempel-Ziv algorithm
- Lempel-Ziv-Welch algorithm
- Levenberg-Marquardt algorithm
- LFU algorithm
- line-probe algorithm
- link state algorithm
- LU decomposition algorithm
- LZ algorithm
- LZW algorithm
- MacQueen's k-means algorithm
- McCulloch-Pitts algorithm
- memetic algorithm
- message authentication algorithm
- metaheuristic algorithm
- min-cut algorithm
- modified Gram-Schmidt algorithm
- Nelder-Mead simplex algorithm
- nested algorithm
- numerical algorithm
- Oja iterative algorithm
- on-line algorithm
- optimization algorithm
- ordering algorithm
- painter's algorithm
- parallel algorithm
- pattern classification algorithm
- pattern recognition algorithm
- pel-recursive estimation algorithm
- placement algorithm
- planning algorithm
- polynomial algorithm
- predictive algorithm
- preemptive algorithm
- Prim algorithm
- production rule based algorithm
- pruning algorithm
- pseudo least recently used algorithm
- Q-R-algorithm
- quantum search algorithm
- quick-union algorithm with path compression
- quick-union algorithm
- radix sorting algorithm
- randomized algorithm
- rank algorithm
- Read-Solomon cyclic redundancy compression algorithm
- recognition algorithm
- recurrent algorithm
- recursive algorithm
- resilient propagation algorithm
- Rivest-Shamir-Adleman algorithm
- robust algorithm
- routing algorithm
- RProp algorithm
- RSA algorithm
- scheduling algorithm
- search algorithm
- search network algorithm
- secure hash algorithm
- selective-trace algorithm
- self-organizing algorithm
- semi-numerical algorithm
- sequential algorithm
- sequential leader clustering algorithm
- serial algorithm
- shortest path algorithm
- sieving algorithm
- simulated annealing training algorithm
- simulation algorithm
- smoothing algorithm
- software algorithm
- spanning tree algorithm
- spline algorithm
- splitting algorithm
- stack algorithm
- statistical algorithm
- stochastic algorithm
- supervised training algorithm
- symmetric encryption algorithm
- time-wheel algorithm
- training algorithm
- universal algorithm
- unsupervised training algorithm
- van der Waerden algorithm
- variational algorithm
- vector distance algorithm
- Viterbi algorithm
- VLSI algorithm
- weighted quick-union algorithm
- working algorithmThe New English-Russian Dictionary of Radio-electronics > algorithm
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9 Artificial Intelligence
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)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)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, EventuallyJust 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 ProgramMany 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)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)[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)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 FormThe 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)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)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)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)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 FormationIt 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)We might distinguish among four kinds of 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.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.... 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)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 ContextsEven 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)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)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 IntelligenceThe 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)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 PropositionsIn 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)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)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)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)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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10 gas
1) газ
2) бензораздаточный
3) газировать
4) газовать
5) газовый
6) газокислородный
7) газолиновый
8) бензин
9) газообразный
– adsorbed gas
– air-blast gas
– ammonia gas
– ammonia gas maser
– approved gas detector
– artificial gas
– balloon gas bag
– blast gas
– blast-furnace gas
– bottle gas
– buffer gas
– carburetted gas
– carburizing gas
– carrier gas
– casing-head gas
– chemical gas generator
– chlorine gas
– clean gas
– coal gas
– coke-oven gas
– compressed gas
– condensed gas deposit
– converter gas
– corrosive gas
– cupola gas
– cutting gas
– cyclone gas cleaning
– degenerate gas
– dehydration of gas
– densimetric gas analyzer
– diatomic gas molecule
– dilute gas
– discharge gas
– disorienting gas
– distribution of gas
– downtake gas duct
– driver gas
– dry gas cleaning
– drying gas
– dust-laden gas
– electron gas
– electronegative gas
– entrapped gas
– evolve gas
– evolved gas
– exhaust gas
– explosive gas
– flare gas
– flue gas
– flue gas analyzer
– flue gas path
– fluidized-bed gas producer
– fluidizing gas
– free gas
– free-piston gas generator
– froth gas cleaning
– fuel gas
– fume-laden gas
– gas amplification
– gas amplification factor
– gas anchor
– gas balance
– gas barrier
– gas bleeder
– gas blower
– gas calorimeter
– gas carburizing
– gas cell
– gas cleaning
– gas cleaning by filtration
– gas coal
– gas coke
– gas conduit
– gas constant
– gas content
– gas cooker
– gas cooler
– gas corrosion
– gas current
– gas cutting
– gas cylinder
– gas discharge
– gas discharge laser
– gas dynamics
– gas emission source
– gas equipment
– gas factor
– gas field
– gas flowmeter
– gas flue
– gas fuel
– gas hardener
– gas heated evaporator
– gas heating
– gas holder
– gas hole
– gas industry
– gas is adsorbed by charcoal
– gas laser
– gas law
– gas leak to atmosphere
– gas line
– gas liquor
– gas logging
– gas main
– gas meter
– gas microanalyser
– gas misalignment
– gas mixer
– gas nest
– gas oil
– gas outburst
– gas outlet
– gas phase
– gas pickling
– gas pipeline
– gas plasma display
– gas pocket
– gas pressure regulator
– gas production
– gas pump
– gas purifier
– gas purifying mass
– gas rock
– gas saturation
– gas scrubber
– gas scrubbing
– gas seal
– gas sintering
– gas space
– gas spanner
– gas supply
– gas survey
– gas synthesis
– gas tank
– gas target
– gas tongs
– gas tube
– gas turbine
– gas turbine jet engine
– gas vulcanization
– gas washer
– gas welding
– gas works
– gas yield factor
– hearth gas
– high-pressure gas burner
– high-pressure gas container
– hydraulic gas dynamics
– hypersonic gas dynamics
– ideal gas
– ideal gas law
– illuminating gas
– imperfect gas
– indoor gas line
– inert gas arc welding
– inert gas introduction
– insulating gas
– interferometric gas analyzer
– introduction of gas in metal
– kiln gas
– l.p. gas
– laughing gas
– lean gas
– lighter-than-air gas
– liquefied gas
– liquify gas
– local gas line
– magnetic gas analyzer
– magnetoionic gas
– magnetomechanical gas analyzer
– marsh gas
– mine gas
– mixed gas
– monatomic gas
– natural gas
– natural-pressure gas lift
– noble gas
– noncorrosive gas
– nondegenerate gas
– nondisorienting gas
– noxious gas
– occluded gas
– oil gas
– oil-well gas
– optical-acoustic gas analyzer
– oxygen gas
– oxygen-converter gas
– peat gas
– permanent gas
– phreatic gas
– plasma-forming gas
– poison gas
– poor gas
– power gas
– pressure gas welding
– process gas
– producer gas
– pumped gas
– rare gas
– rarefied gas
– raw gas
– raw natural gas
– real gas
– recycle gas
– reducing gas
– relaxing gas
– residual gas
– residue gas
– rich gas
– roaster gas
– RX gas
– scrub gas
– secondary gas
– separation of gas mixtures
– sewage gas
– sewer gas
– shielding gas
– solid gas
– solid-propellant gas generator
– stagnated gas
– steam and gas
– sudden gas outburst
– swamp gas
– tail gas
– thermochemical gas analyzer
– thermomagnetic gas analyzer
– to gas
– top gas pressure
– town gas
– toxic gas
– triatomic gas
– tromp gas
– tropospheric gas
– tuyere gas
– two-stage gas turbine
– valve gas
– volumetric gas analyzer
– waste gas
– waste gas flue
– waste gas heating
– water gas
– wet gas
aerodynamics of rarefied gas — аэродинамика разреженных газов
gas and steam turbine installation — <engin.> установка турбинная газо-паровая
gas plasma display element — <comput.> трубка газонаполненная
liquid petroleum gas — <energ.> газ жидкий
nondisorienting buffer gas — неразориентирующий буферный газ
Petroleum and Gas Extracting Administration — <energ.> Нефтегазодобывающее управление
radioactive noble gas — <phys.> газ благородный радиоактивный
suspension of matter in gas — <energ.> газовзвесь, газовзвеси
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11 analysis
анализ; изучение, исследование- algorithmic analysis
- analysis by synthesis
- analysis of causes
- analysis of covariance
- analysis of variance by ranks
- analysis of variance components
- analysis of variance
- approximate analysis
- automatic number analysis
- backward error analysis
- behavior pattern analysis
- behavioral analysis
- benchmark analysis
- botton-up analysis
- break-even analysis
- bus state analysis
- circuit analysis
- clickstream analysis
- cluster analysis
- competitive analysis
- computer analysis
- computerized analysis
- contour analysis
- critical-path analysis
- failure analysis
- feasibility analysis
- flow analysis
- forward error analysis
- Fourier analysis
- frequency-domain analysis
- function point analysis
- harmonic analysis
- immediate constituents analysis
- interconnect analysis
- interval analysis
- layout analysis
- lexical analysis
- linguistic analysis
- logic analysis
- mathematical analysis
- means-aids analysis
- mixed-mode analysis
- model-based analysis
- model analysis
- morphological analysis
- multiresolution analysis
- neighborhood analysis
- network analysis
- nodal analysis
- numerical analysis
- on-line analysis
- parametric analysis
- parasitic analysis
- pattern analysis
- peak hour analysis
- predictive analysis
- procedure analysis
- protocol analysis
- queueing analysis
- recursive analysis
- regression analysis
- security analysis
- sentence-by-sentence syntactic analysis
- sentiment analysis
- sequential analysis
- signature analysis
- state analysis
- statistical analysis
- statistic analysis
- stem analysis
- structural analysis
- structured analysis
- surface analysis
- symbolic analysis
- syntactic analysis
- systems analysis
- time-and-frequency analysis
- timing analysis
- top-down analysis
- topological analysis
- topological timing analysis
- trace analysis
- transient analysis
- variance analysis
- wavelet analysisEnglish-Russian dictionary of computer science and programming > analysis
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12 tools
сервис, инструментарий, инструментальные средства- best-of-breed tools
- CAD tools
- computerized tools
- cross development tools
- dialogue control tools
- dialog control tools
- dynamic debugging tools
- evolution replay tools
- eye-dropped tools
- front-end tools
- graphical tools
- humble tools
- in-house tools
- intelligent design tools
- lifecycle tools
- liquify tools
- low level tools
- lowercase tools
- machine-independent tools
- path tools
- place-and-route tools
- smart toolss
- software metric tools
- software tools
- support tools
- symbolism tools
- synthesis toolsEnglish-Russian dictionary of computer science and programming > tools
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13 motion
движение, ход, перемещение, подача, механизм, передвижение, такт
– motion channel
– motion composer
– motion computation
– motion control
– motion control loop
– motion correction
– motion correction resolution
– motion cycle
– motion database
– motion economy principle
– motion equation
– motion execution
– motion flow
– motion forecasting
– motion formation problem
– motion freedom
– motion generation program
– motion generation resolution
– motion generation subsystem
– motion generation system
– motion generator
– motion interference
– motion law
– motion level language
– motion material
– motion path
– motion pattern
– motion plan
– motion plane
– motion planning
– motion primitive
– motion program
– motion refinement
– motion repeatability
– motion repertoire
– motion restriction
– motion scheme
– motion screw
– motion sensing
– motion sequence
– motion sequence programming
– motion skeleton
– motion smoothing
– motion space
– motion span
– motion specification
– motion stability
– motion stabilization
– motion start
– motion study
– motion succession
– motion synthesis
– motion timing
– motion-average forecasting
– motion-planning computer
– motion-velocity graph
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14 speech
nACOUST palabra f -
15 algorithm
4-D algorithm4-D descent advisor algorithmaiming algorithmBayesian algorithmBeam-Warming algorithmCFD algorithmCholesky algorithmcommand generation algorithmcomputational fluid dynamics algorithmcontrol algorithmdecomposition algorithmdesign algorithmdiagnosing algorithmdiagnosis algorithmdisplay algorithmengagement algorithmestimation algorithmEuler equation algorithmexplicit-implicit algorithmfeasible directions algorithmfeedback algorithmfilter algorithmfinite-volume algorithmflight management algorithmflight path algorithmflux-split algorithmFMS algorithmfrequency domain algorithmGauss-Newton algorithmgeometry generation algorithmgradient-based algorithmgrid algorithmguidance algorithmgunnery algorithmhidden surface algorithmimage-display algorithmLanczos algorithmleast-squares algorithmLevenberg-Marquardt algorithmlinear quadratic algorithmMacCormack algorithmmaneuver algorithmmaneuvering algorithmmarching algorithmmaximum likelihood estimate algorithmminimum variance algorithmmotion drive algorithmmotion-base drive algorithmmultigrid algorithmNavier-Stokes algorithmNewton-Raphson algorithmnonlinear programming algorithmoptimization algorithmplanning algorithmpole placement algorithmPotter algorithmQR algorithmrelaxation algorithmscheduling algorithmsearch algorithmshock fitting algorithmshock-capturing algorithmspeed profile algorithmspeed selection algorithmstability algorithmstate estimation algorithmsteepest descents algorithmsynthesis algorithmthrust algorithmtime-split algorithmtime-to-go algorithmtrim algorithmtwo-stage algorithm of splittingup-date algorithmupwind algorithmupwind-differenced algorithmvariable-gain algorithmweighted least squareszonal algorithm -
16 task
1. (частная) задача,см. тж. mission,ACM taskair ambulance taskair-to-air taskair-to-air combat taskapproach taskclosed-loop taskcompensatory tracking taskcomputational taskcontrol taskdemanding taskdesign taskflight taskflight path tracking taskflying taskformation taskguidance taskhigh-gain taskhigh-stress taskhover taskHUD taskHUD command taskHUD-generated tracking taskin-flight refueling taskinstructional tasklabor-intensive tasklanding tasklateral-directional tasklateral-offset landing tasklineup tasklongitudinal tasklow-altitude tasklow-flying tasklow-stress taskmaintenance taskman-machine taskmaneuvering taskmanipulation taskmission taskmission-management taskmultichannel taskmultiloop tasknap-of-the-earth tasknavigator's tasksoffset taskon-condition taskopen-loop taskoptimization taskouter-loop taskpatrol taskpeak taskpiloting taskpitch taskpitch tracking taskroll taskscheduling taskslalom tasksoftware taskstabilization taskswitch operation tasksynthesis tasktarget-tracking taskterrain following tasktracking tasktraining taskup-and-away taskVFR taskvisual flight task
См. также в других словарях:
Path integral formulation — This article is about a formulation of quantum mechanics. For integrals along a path, also known as line or contour integrals, see line integral. The path integral formulation of quantum mechanics is a description of quantum theory which… … Wikipedia
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Компьютерный синтез — (англ. Computer Assisted Synthesis Design) область хемоинформатики, охватывающая методы, алгоритмы и реализующие их компьютерные программы, оказывающие помощь химику в планировании синтеза органических соединений, прогнозировании результатов и… … Википедия
Integral yoga — Yogaschool|name=Integral yoga color=green bgcolor=white religious origins=Hinduism, Vedanta regional origins=Sri Aurobindo Ashram, India founding guru=Sri Aurobindo, The Mother popularity= millions, both in India and abroad practice… … Wikipedia
