The problem we consider in this paper is that of discovering formal ruleswhich will enable us to decide when a question posed in English can beanswered on the basis of one or more declarative English sentences. Toillustrate how this may be done in very simple cases we give rules whichtranslate 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 ofthe declaratives. We suggest that in order to capture the meanings of morecomplex sentences it will be necessary to go beyond the first-order predicatecalculus, to a notation in which the scope of words other than quantifiersand negations is clearly indicated.Machine Intelligence 6

We describe the analysis of visual scenes consisting of black on white drawings formed with curved lines, depicting familiar objects and forms: houses, trees, persons, and so on; for instance, drawings found in coloring books. The goal of such analysis is to recognize (by computer) such forms and shapes when present in the input scene; that is, to name (correctly) as many parts of the scene as possible: finger, hand, girl, dance, and so on. Complications occur because each input scene contains several such objects, partially occluding each other and in varying degrees of orientation, size, and so on. The analysis of these line drawings is an instance of'the context problem', which can be stated as'given that a set (a scene) is formed by components that locally (by their shape) are ambiguous, because each shape allows a component to have one of several possible values (a circle can be sun, ball, eye, hole) or meanings, can we make use of context information stated in the form of models, in order to single out for each component a value in such manner that the whole set (scene) is consistent or makes global sense?' Thus, shape drastically limits the values that a component could have, and further disambiguation is possible only by using global information (derived from several components and their interrelations or interconnections) under the assumption that the scene as a whole is meaningful. This paper proposes a way to solve'the context problem' in the paradigm of coloring book drawings. We have not implemented this approach; indeed, a purpose of this paper is to collect criticisms and suggestions.

In N. V. Findler and B. Meltzer (Eds.), Artificial intelligence and heuristic programming. New York: American Elsevier, 81-100.

Arguably the first article discussing the uses of AI in the law beyond straightforward information retrieval.Although the computer has worked its way out of the laboratory and into common experience, lawyers have made slim progress towards finding useful computer applications. Research in artificial intelligence, a branch of computer science, has illuminated our capacity to use computers to model human thought processes. This research suggests that computer science may assist lawyers in both the study and performance of their reasoning 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.Stanford Law Review vol.23, no.1, November, 1970

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.

This paper explores new approaches to the symmetric traveling-salesman problem in which 1-trees, which are a slight variant of spanning trees, play an essential role. A 1-tree is a tree together with an additional vertex connected to the tree by two edges. We observe that (i) a tour is precisely a 1-tree in which each vertex has degree 2, (ii) a minimum 1-tree is easy to compute, and (iii) the transformation on “intercity distances” cij → Cij + πi + πj leaves the traveling-salesman problem invariant but changes the minimum 1-tree. Operations Research, 18, 1138–1162.

See also: ACM Digital Library.CACM 13 (1):15-30

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.

AI or artificial intelligence is a field of computer science that's been relevant ever since the first computer was created back in the 1930s. Over the years, we have seen countless AI applications in both the social and technological aspects of our lives, but there is a still a significant part of this rapidly growing field that is unknown to most people. We will go over 7 things you didn't know about artificial intelligence that could be affecting your life right now. Do you ever wonder why you have to figure out all the CAPTCHAs to identify yourself? Well, back in 1950, Sir Alan Turing devised a test to determine if the user taking the test is either a computer or a human.