Great effort from team BDMS and Crayon Data to put up a portal like this. Big Data Made Simple is one of the best big data content portals that I know.

Digital transformation will create trillions of dollars of value. While estimates vary, the World Economic Forum in 2016 estimated an increase in $100 trillion in global business and social value by 2030. Due to AI, PwC has estimated an increase of $15.7 trillion and McKinsey has estimated an increase of $13 trillion in annual global GDP by 2030. We are currently in the middle of an AI renaissance, driven by big data and breakthroughs in machine learning and deep learning. These breakthroughs offer opportunities and challenges to companies depending on the speed at which they adapt to these changes.

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.

The problem of producing sentences of a transformational grammar by using a random generator to create phrase structure trees for input to the lexical insertion and transformational phases is discussed. A purely random generator will produce base trees which will be blocked by the transformations, and which are frequently too long to be of practical interest. A solution is offered in the form of a computer program which allows the user to constrain and direct the generation by the simple but powerful device of restricted subtrees. The program is a directed random generator which accepts as input a subtree with restrictions and produces around it a tree which satisfies the restrictions and is ready for the next phase of the grammar. The underlying linguistic model is that of Noam Chomsky, as presented in Aspects of the Theory of Syntax.

A question-answering system. Tech. Note 15, AI Group, Stanford Research Institute, Menlo Park, Calif.

Science 166:178-192

This is a reprint of a chapter that first appeared in 1968 in a collection of papers on operations research [1]. The chapter was written to survey the progress in heuristic processes since an earlier 1968 paper on the same topic [2]. In the current collection, it serves as something of a historical introduction to the SOAR system, which occupied Newell and his students for many years. Even though SOAR did not exist at the time this chapter was written, one finds some of the threads that are later to be drawn together into the construction of SOAR. Newell explores so-called weak methods, which trade power for general applicability.

In Walker, D. E. & Norton, L. N. (eds. ), IJCAI 1969: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, May 7-9, 1969 Washington, D. C., pp. 241-252

A program was writte n to solve calculus word problems. The program, CARPS (CAlculus Rate Problem Solver), is restricte d to rate problems. The overall plan of the program is simila r to Bobrow's STUDENT, the primary difference being the introductio n of "structures " as the internal model in CARPS. Structures are stored internally as trees, each structure holding the information gathered about one object.In Walker, D. E. & Norton, L. N. (eds. ), IJCAI 1969: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, May 7-9, 1969 Washington, D. C., pp. 241-252