Big data, or the data processed on a large scale by analytical tools, is crossing industries to generate new and improved technological processes. In fleet management, this means benefits to maintenance, driver safety, and overall profits. For a bottom line capable of surviving any economic conditions of the present and near-future, big data-powered tech can help fleet management companies thrive. The integration of these data tools have already proved their worth in the industry, and the future only looks brighter. Here, we'll explore just how big data is being utilized by the fleet management industry to produce better solutions in maintenance, safety, and financial outcomes.

The global autonomous vehicle market is already valued at an estimated 54 billion, and is projected to increase over the coming years. Self-driving cars are one of the more popularized and futuristic endeavors that people are looking forward to, and it's all made possible in part by big data. However, in addition to autonomous vehicles, big data is transforming the transportation industry in a number of other ways as well. From improving traffic efficiency to crash maps made with predictive analysis, here's what you need to know. City traffic can cause a number of issues for commuters -- in fact, it's estimated that Americans lose $160 billion in productivity each year, simply by sitting in traffic.

How often do you hear people use the terms "artificial intelligence" and "machine learning" interchangeably? The two are definitely related, and machine learning is actually a subset of artificial intelligence. However, as a greater number of businesses begin offering "intelligent" solutions, it becomes more vital than ever before to differentiate between these two concepts. After all, you may find yourself giving a presentation or speaking with someone who specializes in one of these fields, and you want to know what you're talking about. From cancer screenings to climate change, there are numerous applications for artificial intelligence.

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.

Machine learning has ties to statistics. It allows you to detect patterns in the sometimes chaotic mathematical models that exist. Online machine learning courses teach you how to make use of machine learning algorithms in a programming language. The disruptions caused by (and anticipated disruptions of) artificial intelligence is a reality we have come to live with. You do not need to be a techie to appreciate the many inroads artificial intelligence has made into everyday life.

Many problems in artificial intelligence involve the searching of large trees of alternative possibilities--for example, game-playing and theorem-proving. The problem of efficiently searching large trees is discussed. A new method called "dynamic ordering" is described, and the older minimax and Alpha-Beta procedures are described for comparison purposes. Performance figures are given for six variations of the game of kalah. A quantity called "depth ratio" is derived which is a measure of the efficiency of a search procedure.

"A computer program has been written which can formulate hypotheses from a given set of scientific data. The data consist of the mass spectrum and the empirical formula of an organic chemical compound. The hypotheses which are produced describe molecular structures which are plausible explanations of the data. The hypotheses are generated systematically within the program's theory of chemical stability and within limiting constraints which are inferred from the data by heuristic rules. The program excludes hypotheses inconsistent with the data and lists its candidate explanatory hypotheses in order of decreasing plausibility. The computer program is heuristic in that it searches for plausible hypotheses in a small subset of the total hypothesis space according to heuristic rules learned from chemists."In Meltzer, B., Michie, D., and Swann, M. (Eds.), Machine Intelligence 4, pp. 209-254. Edinburgh University Press

See also: Education Resources Information CenterSupplement to Proceedings of the IFIP 68 International Congress, Edinburgh, August 1968. Published in A. J. H. Morrell (ed.), Information Processing 68, Vol. II, pp. 1008-1022, Amsterdam: North-Holland, 1969.

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

We study some aspects of a general game-playing program. Such a program receives as data the rules of a game: an algorithm enumerating the moves and an algorithm indicating how to win. The program associates to each move the conditions necessary for this move to occur. It must find how to avoid a dangerous move. We describe the part of the program playing the combinatorial game in order to win: how it can find the moves which lead to victory and what are the only opponent's moves with which he does not lose. This program has been tried with various games: chess, tic-tac-too, etc.INFORMATION PROCESSING 68 - NORTH-HOLLAND PUBLISHING COMPANY - AMSTERDAM