Russian Mathematical Surveys, 25, 221-262

Russian Mathematical Surveys, 25, 221-262

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

In Linguaggi nella Societ`a e nella Tecnica, pp. 189– 224. Edizioni di Comunit`a.

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

Some of the world's most advanced artificial intelligence (AI) systems, at least the ones the public hear about, are famous for beating human players at chess or poker. Other algorithms are known for their ability to learn how to recognize cats or their inability to recognize people with darker skin. But are current AI systems anything more than toys? Sure, their ability to play games or identify animals is impressive, but does this help toward creating useful AI systems? To answer this, we need to take a step back and question what the goals of AI are.

Artificial Intelligence(AI), a very old subject in computer science, explores how to make machines intelligent. Machine learning(ML) is a subset of AI, which focuses on teaching machines how to learn patterns, based on given a set of data. In short, try to replicate, how humans learn new skills or make decisions. Good news is, AL/ML is not restricted to Sci-Fi movies OR big corporations. Due to explosive growth in hardware capabilities, open source libraries and ability to pay per use model to access this computing power, AI/ML is now within reach of small businesses.

Time is a critical context for understanding how things function. It serves as the digital history for businesses. When you think about institutional knowledge, that's not just bound up in people. Data is part of that knowledge base as well. So, when companies can capture, store and analyze that data in an effective way, it produces better results.