BBN Rep. No. 2378, Bolt Beranek and Newman, Inc., Cambridge, Mass.

Nowadays, practically everything around us that comes from the realm of technology appears to have some aspect of artificial intelligence (AI). Artificial intelligence, in computer terminology, is the programming and development of computers and systems capable of utilising and processing information in a way analogous to human activity. In other terms, it is a technology that allows robots to accomplish jobs that would ordinarily need human-like reasoning. Artificial intelligence offers a wide range of potential applications, including transportation, healthcare, education, agriculture, cybersecurity, and so on. It has the potential to increase worker productivity, stimulate economic growth, and improve the lives of millions of people.

Like it or not – artificial intelligence is here, and it is going to stay. Researchers predict that by 2020, artificial intelligence technologies will be implemented in the majority of new software products and services, which will inevitably change the way we live, work, and do business. The machine learning technology is only in its infant stage, but it has already proven its efficiency in performing routine tasks in a broad array of industries, from retail, manufacturing, and healthcare to education and cybersecurity. However, while AI can be a huge help in detecting and fighting the latest cyber threats, experts are worried that artificial intelligence techniques could also bring more risks and even fuel cybercrime. "As AI capabilities become more powerful and widespread, we expect the growing use of AI systems to lead to the expansion of existing threats, the introduction of new threats and a change to the typical character of threats," a report warns. Researchers strongly suggest that before completely trusting the benefits of deep machine learning, it's crucial to take into consideration potential misuse of the artificial intelligence technology.

In Meltzer, Bernard and Donald Michie (Eds.), Machine Intelligence 5. Edinburgh University Press, pp.203-15.

In Meltzer, Bernard and Donald Michie (Eds.), Machine Intelligence 5. Edinburgh University Press.

In Meltzer, Bernard and Donald Michie (Eds.), Machine Intelligence 5. Edinburgh University Press.

In Meltzer, Bernard and Donald Michie (Eds.), Machine Intelligence 5. Edinburgh University Press.

In Meltzer, Bernard and Donald Michie (Eds.), Machine Intelligence 5. Edinburgh University Press.

Note: PDF of full volume downloadable by clicking on title above (32.8 MB). Selected individual chapters available from the links below.CONTENTSINTRODUCTORY MATERIALMATHEMATICAL FOUNDATIONS1 Program scheme equivalences and second-order logic. D. C. COOPER 32 Programs and their proofs: an algebraic approach.R. M. BURSTALL and P. J. LANDIN 173 Towards the unique decomposition of graphs. C. R. SNOW andH. I. SCOINS 45THEOREM PROVING4 Advances and problems in mechanical proof procedures. D. PRAWITZ 595 Theorem-provers combining model elimination and Tesolution.D. W. LOVELAND 736 Semantic trees in automatic theorem-proving. R. KOWALSKI andP. J. HAYES 877 A machine-oriented logic incorporating the equality relation.E. E. SIBERT 1038 Paramodulation and theorem-proving in first-order theories withequality. G. ROBINSON and L. Wos 1359 Mechanizing higher-order logic. J. A. ROBINSON 151DEDUCTIVE INFORMATION RETRIEVAL10 Theorem proving and information retrieval. J. L. DARLINGTON 17311 Theorem-proving by resolution as a basis for question-answeringsystems. C. CORDELL GREEN 183MACHINE LEARNING AND HEURISTIC PROGRAMMING12 Heuristic dendral: a program for generating explanatory hypothesesin organic chemistry. B. BUCHANAN, G. SUTHERLAND andE. A. FEIGENBAUM 20913 A chess-playing program. J. J. SCOTT 25514 Analysis of the machine chess game. I. J. GOOD 26715 PROSE—Parsing Recogniser Outputting Sentences in English.D. B. VIGOR, D. URQUHART and A. WILKINSON 27116 The organization of interaction in collectives of automata. 285V. I. VARSHAVSKY COGNITIVE PROCESSES: METHODS AND MODELS17 Steps towards a model of word selection. G. R. Kiss 31518 The game of hare and hounds and the statistical study of literaryvocabulary. S. H. STOREY and M. A. MAYBREY 33719 The holophone —recent developments. D. J. WILLSHAW andH. C. LONGUET-HIGGINS 349PATTERN RECOGNITION20 Pictorial relationships — a syntactic approach. M. B. CLOWES 36121 On the construction of an efficient feature space for optical characterrecognition. A. W. M. COOMBS 38522 Linear skeletons from square cupboards. C. J. HILDITCH 403PROBLEM-ORIENTED LANGUAGES23 Absys 1: an incremental compiler for assertions; an introduction.J. M. FOSTER and E. W. ELCOCK 423PRINCIPLES FOR DESIGNING INTELLIGENT ROBOTS24 Planning and generalisation in an automaton/environment system.J. E. DORAN 43325 Freddy in toyland. R. J. POPPLESTONE 45526 Some philosophical problems from the standpoint of artificialintelligence. J. MCCARTHY and P. J. HAYES 463INDEX 505 Machine Intelligence Workshop

"The purpose of this paper is to clarify some basic issues of choice of representation for problems of reasoning about actions. The general problem of re- Presentation is concerned with the relationship between different ways of formulating a problem to a problem solving system and the efficiency with which the system can be expected to find a solution to the problem. An understanding of the relationship between problem formulation and problem solving efficiency is a prerequisite for the design of procedures that can automatically choose the most `appropriate' representation of a problem ( they can find a `point of view' of the problem that maximally simplifies the process of finding a solution).Many problems of practical importance are problems of reasoning about actions. In these problems, a course of action has to be found that satisfies a number of specified conditions. A formal definition of this class of problems is given in the next section, in the context of a general conceptual framework for formulating these problems for computers. Everyday examples of reasoning about actions include planning an airplane trip, organizing a dinner party, etc. There are many examples of industrial and military problems in this category, such as scheduling assembly and transportation processes, designing a program for a computer, planning a military operation, etc."In D.Michie (Ed.), Machine intelligence 3. New York: American Elsevier,131-171