We were led to this comparison by the observation that the computer model is weaker in three important ways: search depth is not unbounded, structures matching variables cannot be compared, and structures matching variables cannot be moved. Thus, every recursively enumerable language is generated by a transformational grammar with limited search depth, without equality comparisons of variables, and without moving structures corresponding to variables. On the other hand, both mathematical models allow unbounded depth of analysis; both allow equality comparisons of variables, although the Ginsburg-Partee model.compares
This paper describes a computer system for understanding English. It is based on the belief that in modeling language understanding, we must deal in an integrated way with all of the aspects of language--syntax, semantics, and inference. It enters into a dialog with a person, responding to English sentences with actions and English replies, asking for clarification when its heuristic programs cannot understand a sentence through the use of syntactic, semantic, contextual, and physical knowledge. By developing special procedural representations for syntax, semantics, and inference, we gain flexibility and power.
In forming such a view the Council has available to it a great deal of specialist information through its structure of Boards and Committees-- particularly from the Engineering Board and its Computing Science Committee and from the Science Board and its Biological Sciences Committee. To supplement the important mass of specialist and detailed information available to the Science Research Council, its Chairman decided to commission an independent report by someone outside the Al eld but with substantial general experience of research work in multidisciplinary elds including elds with mathematical, engineering and biological aspects. Such a personal view of the subject might be helpful to other lay persons such as Council members in the process of preparing to study specialist reports and recommendations and working towards detailed policy formation and decision taking. In scientic applications, there is a similar look beyond conventional data processing to the problems involved in large-scale data banking and retrieval, The vast eld of chemical compounds is one which has lent itself to ingenious and eective programs for data storage and retrieval and for the inference of chemical structure from mass-spec- trometry and other data.
Sociologists are concerned to predict the effect of changes on future society.But is prediction in principle possible when intelligence is involved? Ifintelligence is the production of novelty, accurate prediction might seem to bestrictly impossible. However this may be, it seems that the present troubleabout social prediction is simply that there are no adequate theoreticalmodels of societies. This means that politicians are almost powerless topredict, plan, or control, except with incredible errors. We find ourselves injust this position in trying to assess the implications of future intelligence.Machine Intelligence 6
Language was considered just a "bunch of words" and the primary task for early machine translation (MT) was to build machines large enough to hold all the words necessary in the translation process. These means included the printing out of the several possible solutions of ambiguous text segments to let the reader decide for himself the correct meaning, printing out the ambiguous source language text, and other temporary expedients. Particularly one must understand the rules under which such a complex system as human language operates and how the mechanism of this operation can be simulated by automatic means, i.e., without any human intervention at all. The second problem, the simulation of human language behavior by automatic means, is almost impossible to achieve, since language is an open and dynamic system in constant change and because the operation of the system is not yet completely understood.
Abstract: The thesis describes a computer program which performs a complex picture processing task. The task is to choose, from a collection of pictures of people taken by a TV camera, those pictures that depict the same person. The primary purpose of this research has been directed toward the development of new techniques for picture processing.
In the meantime, Chomsky (1965) devised a paradigm for linguistic analysis that includes syntactic, semantic, and phonological components to account for the generation of natural language statements. This theory can be interpreted to imply that the meaning of a sentence can be represented as a semantically interpreted deep structure--i.e, From computer science's preoccupation with formal programming languages and compilers, there emerged another paradigm. The adoption and combination of these two new paradigms have resulted in a vigorous new generation of language processing systems characterized by sophisticated linguistic and logical processing of well-defined formal data structures. These included a social-conversation machine, systems that translated from English into limited logical calculi, and programs that attempted to answer questions from English text.
Abstract: The paper describes techniques and methodology which are useful in achieving close to real-time recognition of speech by a computer. To analyze connected speech utterances, any speech recognition system must perform the following processes: preprocessing, segmentation, segment classification, recognition of words, recognition of sentences. The paper presents implemented solutions to each of these problems which achieved accurate recognition in all the trial cases.
Another substantial body of work on general problem-solving is that associated with the Graph Traverser program (Doran and Michie 1966, Doran 1967, Michie 1967, Doran 1968, Michie, Fleming and Oldfield 1968, Michie and Ross 1970). In this section and the next we shall consider the transition from heuristic problem-solving as exemplified by the Graph Traverser, to planning by a robot as exemplified by my own work and that of Marsh (Doran 1967, 1967a, 1968a, 1969; Marsh 1970; Michie 1967, 1968a; Popplestone 1967). In order to do this efficiently the program uses, in general, a heuristic state evaluation function and heuristic operator selection techniques to grow the search tree in the most promising direction. The following types of learning occurred in the system: (a) learning of the relationship between acts and perceptions by noting the effects of individual acts, by making generalizations about the effects of acts, and by noting that certain complicated transitions from one perceived state to another can always be achieved, (b) learning which acts to employ in particular situations and the benefits to be expected -- a kind of habit formation.
We may regard the subject of artificial intelligence as beginning with Turing's article'Computing Machinery and Intelligence' (Turing 1950) and with Shannon's (1950) discussion of how a machine might be programmed to play chess. In this case we have to say that a machine is intelligent if it solves certain classes of problems requiring intelligence in humans, or survives in an intellectually demanding environment. However, we regard the construction of intelligent machines as fact manipulators as being the best bet both for constructing artificial intelligence and understanding natural intelligence. Given this notion of intelligence the following kinds of problems arise in constructing the epistemological part of an artificial intelligence: I.