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.

Note: PDF of full volume downloadable by clicking on title above (32.8 MB). Selected individual chapters available from the links below.CONTENTSINTRODUCTORY MATERIALMATHEMATICAL FOUNDATIONS1 Program scheme equivalences and second-order logic. D. C. COOPER 32 Programs and their proofs: an algebraic approach.R. M. BURSTALL and P. J. LANDIN 173 Towards the unique decomposition of graphs. C. R. SNOW andH. I. SCOINS 45THEOREM PROVING4 Advances and problems in mechanical proof procedures. D. PRAWITZ 595 Theorem-provers combining model elimination and Tesolution.D. W. LOVELAND 736 Semantic trees in automatic theorem-proving. R. KOWALSKI andP. J. HAYES 877 A machine-oriented logic incorporating the equality relation.E. E. SIBERT 1038 Paramodulation and theorem-proving in first-order theories withequality. G. ROBINSON and L. Wos 1359 Mechanizing higher-order logic. J. A. ROBINSON 151DEDUCTIVE INFORMATION RETRIEVAL10 Theorem proving and information retrieval. J. L. DARLINGTON 17311 Theorem-proving by resolution as a basis for question-answeringsystems. C. CORDELL GREEN 183MACHINE LEARNING AND HEURISTIC PROGRAMMING12 Heuristic dendral: a program for generating explanatory hypothesesin organic chemistry. B. BUCHANAN, G. SUTHERLAND andE. A. FEIGENBAUM 20913 A chess-playing program. J. J. SCOTT 25514 Analysis of the machine chess game. I. J. GOOD 26715 PROSE—Parsing Recogniser Outputting Sentences in English.D. B. VIGOR, D. URQUHART and A. WILKINSON 27116 The organization of interaction in collectives of automata. 285V. I. VARSHAVSKY COGNITIVE PROCESSES: METHODS AND MODELS17 Steps towards a model of word selection. G. R. Kiss 31518 The game of hare and hounds and the statistical study of literaryvocabulary. S. H. STOREY and M. A. MAYBREY 33719 The holophone —recent developments. D. J. WILLSHAW andH. C. LONGUET-HIGGINS 349PATTERN RECOGNITION20 Pictorial relationships — a syntactic approach. M. B. CLOWES 36121 On the construction of an efficient feature space for optical characterrecognition. A. W. M. COOMBS 38522 Linear skeletons from square cupboards. C. J. HILDITCH 403PROBLEM-ORIENTED LANGUAGES23 Absys 1: an incremental compiler for assertions; an introduction.J. M. FOSTER and E. W. ELCOCK 423PRINCIPLES FOR DESIGNING INTELLIGENT ROBOTS24 Planning and generalisation in an automaton/environment system.J. E. DORAN 43325 Freddy in toyland. R. J. POPPLESTONE 45526 Some philosophical problems from the standpoint of artificialintelligence. J. MCCARTHY and P. J. HAYES 463INDEX 505 Machine Intelligence Workshop

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.

AN AUGMENTED STATE TRANSITION NETWORK ANALYSIS PROCEDURE Daniel G. Bobrow Bolt, Beranek and Newman, Inc. Cambridge, Massachusetts Bruce Eraser Language Research Foundation Cambridge, Massachusetts Summary A syntactic analysis procedure is described which obtains directly the deep structure information associated with an input sentence. The implementation utilizes a state transition network characterizing those linguistic facts representable in a context free form, and a number of techniques to code and derive additional linguistic information and to permit the compression of the network size, thereby allowing more efficient operation of the system. By recognizing identical constituent predictions stemming from two different analysis paths, the system determines the structure of this constituent only once. When two alternative paths through the state transition network converge to a single state at some point In the analysis, subsequent analyses are carried out only once despite the ...

Stanford AI Memo 83 or Computer Science Technical Note 130, Computer Science Department, Stanford University, Stanford, CA, 1969

Reading, Mass.: Addison-Wesley.

In IEEE Conference Record of the 1969 Tenth Annual Symposium on Switching and Automata Theory, pp. 74–81.

This term is meant to suggest not only the usual specifics of programming system and language but also such more elusive and subjective considerations as ease and level of interaction, "forgivefulness" of errors, human engineering, and system "Initiative." In normal usage, the word "environment" refers to the "aggregate of social and cultural conditions that influence the life of an individual." The programmer's enivronment influences, to a large extent determines, what sort of problems he can (and will want to) tackle, how far he can go, and how fast. If the environment is "cooperative" and "helpful" -- the anthropomorphism is deliberate -- then the programmer can be more ambitious and oroductive. If not, he will spend most of his time and energy "fighting" the system, which at times seems bent on frustrating his best efforts.

PLANNER: A LANGUAGE FOR PROVING THEOREMS IN ROBOTS Summary Carl Project MAC - Massachuse PLANNER is a language for proving theorems and manipulating models in a robot. The language is built out of a number of problem solving primitives together with a hierarchical control structure. Statements can be asserted and perhaps later withdrawn as the state of the world changes. Conclusions can be drawn from these various changes in state. Goals can be established and dismissed when they are satisfied. The deductive system of PLANNER is subordinate to the hierarchical control structure in order to make the language efficient. The use of a general purpose matching language makes the deductive system more powerful. Preface PLANNER is a language for proving theorems and manipulating models in a robot.