A versatile assembly system, using TV cameras and oomputer-controlled arm and moving table, is described. It makes almple assemblies such aa a peg and rings and a toy car. It separates parts from a heap, recognising them with an overhead camera, then assembles them by feel. It can be instructed to perform a new task with different parte by spending an hour showing it the parts and a day or two programming the assembly manipulations. A hierarchical description of parts, views, outlines etc. is used to construct models, and a structure matching algorithm is used in recognition.Later version appearing in Artificial Intelligence, Vol 6, pp. 129(1975) (available for a fee).In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

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

Early programs were written to demonstrate that a particular task could be accomplished and could not periorm other tasks, even if quite similar, without being extensively rewritten. Generality unnecessary for the task at hand was sacrificed to keep the programs as *Currently on leave to The University of Jerusalem **Now at Computer Science Department, Rutgers University ***Is now at NIH, Bethesda, Maryland ****With Lockheed Palo Alto Research Labs //This research was supported by the Advanced research Projects Agency of the Department of Defense under Contract No. SD-183. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the Advanced Research Projects Agency of the U.S. Government. Bmall as possible so they would fit the core limitations of our computer. The main result of this research was the development of programs which could find and stack cubes, either sorting them by size (1), or ordering them by voice command (2).

This edge follower could easily find the outlines of white cubes on a black table, but was prone to error in less carefully controlled environments. Our studies of its inadequacies have stimulated the development of a more powerful edge follower, which overcomes most of the limitations of the old one. This program is currently the initial stage of visual processing in the Stanford hand-eye system (2). It has demonstrated an ability to track weak edges under adverse lighting conditions 2. HARDWARE The edge follower uses a standard vidicon television camera, modified to provide computer control of orientation (a pan-tilt head), focal length (a lens turret), color filter, focus, and target voltage. The lens iris is set manually. The pan-tilt head, lens turret, and focus motor *This research was supported by the Advanced research Projects Agency of the Department of Defense under Contract No. SD-183. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the Advanced Research Projects Agency of the U.S. Government.

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.

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

COMPUTER SOLUTION OF CALCULUS WORD PROBLEMS* Eugene Charniak Massachusetts Ins:itute of Technology Cambridge, Massachusetts SUMMARY A program was written to solve calculus word problems. The program, CARPS (CAlculus Rate Problem Solver), is restricted to rate problems. The overall plan of the program is similar to Bobrow's STUDENT, the primary difference being the introduction of "structures" as the internal model in CARPS. Structures are stored internally as trees, each structure holding the information gathered about one object. It was found that the use of structures made CARPS more powerful than STUDENT in several respects. In calculus word problems it is not uncommon to have two or three sentences providing information for one equation. For example, in a problem about a filter, ALTITUDE was interpreted as ALTITUDE OF THE FILTER because CARPS knew that since the filter was a cone and cones have altitudes the filter had an altitude. The program has solved 14 calculus problems, most taken (sometimes with slight modifications) from standard calculus texts. CARPS is written in two languages. The bulk of the coding is in LISP.

The problem discussed in this paper, namely that of finding a function to satisfy a given argument-value table, is by no means new to computing science, or to mathematics. Thus, for example, the problem of fitting a curve to a set of points is a part of numerical analysis. However, I am concerned with finding a function over a non-metric space, and so my work is closer to that of Feldman et al. (1969) in what they call, 'grammatical inference' or to the automaton-synthesizing programs described by Fogel, Owens and Walsh (1966).

A robot, in order to act intelligently, must be able to reason from facts which its sensors detect to conclusions which govern its actions. This reasoning process is so central to human intelligence that it seems immediately relevant to the problems of robot design to consider its properties, how it might be analysed and imitated.