This paper echoes, from a philosophical standpoint, the claim of McCarthy and Hayes that Philosophy and Artificial Intelligence have important relations. Philosophical problems about the use of "intuition" in reasoning are related, via a concept of analogical representation, to problems in the simulation of perception, problem-solving and the generation of useful sets of possibilities in considering how to act. The requirements for intelligent decision-making proposed by McCarthy and Hayes are criticised as too narrow, and more general requirements are suggested instead. Introduction The aim of this paper is to illustrate the way in which interaction between Philosophy and A.I. may be useful for both disciplines. It starts with a discussion of some philosophical issues which interested me long before I knew anything about A.I., and which I believe are considerably enriched and clarified by relating them to problems in A.I., which, they, in turn, help to clarify. This discussion is followed by some general speculations about the conceptual and perceptual equipment required by an animal or machine able to cope with our spatiotemporal environment. Finally, there are further vague, general and programmatic remarks about the relations between Philosophy and A.I. The paper was inspired mainly by discussions with Max Clowes, but also to some extent by the attempts made by McCarthy and Hayes (12), and Hayes (8) to relate philosophical issues to problems in the design of intelligent robots.

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).

The problem we consider in this paper is that of discovering formal rules which will enable us to decide when a question posed in English can be answered on the basis of one or more declarative English sentences. To illustrate how this may be done in very simple cases we give rules which translate certain declarative sentences and questions involving the quantifiers'some', 'every', 'any', and'no' into a modified first-order predicate calculus, and answer the questions by comparing their translated forms with those of the declaratives. We suggest that in order to capture the meanings of more complex sentences it will be necessary to go beyond the first-order predicate calculus, to a notation in which the scope of words other than quantifiers and negations is clearly indicated. We conclude by describing a notational form for connected sentences, which seems to be a natural extension of Chomsky's'deep structures'.

One example of this will suffice.

A general game-playing program must know the rules of the particular playing game. These rules are:(1) an algorithm indicating the winning state;(2) an algorithm enumerating legal moves. A move gives a set of changes from the present situation.There are two means of giving these rules:(1) We can write a subroutine which recognizes if we have won and another which enumerates legal moves. Such a subroutine is a black box giving to the calling program the answer: 'you win' or 'you do not win', or the list of legal moves. But it cannot know what is in that subroutine.(2) We can also define a language in which we describe the rules of a game. The program investigates the rules written with this language and finds some indications to improve its play. Artificial Intelligence and Heuristic Programming Edinburgh University Press

Amsterdam: North-Holland.

JOINDER OF CLAIMS, COUNTERCLAIMS, AND CROSS-COMPLAINTS: SUGGESTED REVISION OF THE CALIFORNIA PROVISIONS. Research in artificial intelligence, a branch of computer science, has illuminated our capacity to use computers to model human thought processes. In this Article we will argue that the time has come for serious interdisciplinary work between lawyers and computer scientists to explore the computer's potential in law. Interdisciplinary work between the lawyer and the computer scientist has floundered on the misconceptions that each has of the other's discipline. As a result, no one has yet attempted computer programs incorporating complex techniques of legal reasoning. Even efforts in legal information retrieval have been hampered by these misconceptions. In retrieval, lawyers have viewed the computer as, at most, a storehouse from which cases and statutes might be retrieved by skillfully designed indexing systems. But the lawyer rarely looks for, or even expects, clear answers. So far, the efforts in legal retrieval have given little consideration to the possibility that computers might operate on the legal data base the way a lawyer does. Yet the work in both fields law and computer science -,suggests that the computer modeling of legal reasoning would be a fruitful area for research. In this Article we speculate about the dimensions and possible directions of this research. Under the most promising of outcomes, interdisciplinary research could lead both to a greater understanding of the legal reasoning process and to the design of machine methods for performing parts of it. The prospect of using computers to model legal reasoning processes is likely to prompt a typically lawyer-like response: So what if we understand legal reasoning or legal argument formation better? Knowing more about the ways in which lawyers search and manipulate the legal data base might lead to improving the lawyer's skill at his work. We recognize the possibility that the work of many lawyers might actually involve little use of the legal data base for argument construction or dispute resolution.

Ph.D. dissertation "Bi-directional and heuristic search in path problems" (Stanford, Computer Science, 1970) summarized in this article in Machine Intelligence 6 (1971).In the uni-directional algorithms, the search proceeds from an initial nodeforward until the goal node is encountered. Problems for which the goal nodeis explicitly known can be searched backward from the goal node. Analgorithm combining both search directions is bi-directional.This method has not seen much use because book-keeping problems werethought to outweigh the possible search reduction. The use of hashingfunctions to partition the search space provides a solution to some of theseimplementation problems. However, a more serious difficulty is involved.To realize significant savings in bi-directional search, the forward andbackward search trees must meet in the 'middle' of the space. The potentialbenefits from this technique motivates this paper's examination of thetheoretical and practical problems in using bi-directional search.

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.

The fintech industry is growing at an accelerated pace, driven by new technological innovations and evolving needs. In many cases, the modern enhancements across many IT sectors have had secondary effects across industries – and particularly on fintech products and services. For example, artificial intelligence (AI) now drives a large number of applications and major predictive market models/systems. Of particular note are big data analytics and product analytics. Both industries get a lot of news coverage, though normally in relation to social media or marketing.