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.

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.

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.

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.

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

Summary This paper aescriDes a program, called "PRUW", which writes programs. PROW accepts the specification of the program in the language of predicate calculus, decides the algorithm for the program and then produces a LISP program which is an implementation of the algorithm. Since the construction of the algorithm is obtained by formal theorem-proving techniques, the programs that PROW writes are free from logical errors and do not have to be debugged. The user of PROW can make PROW write programs in languages other than LISP by modifying the part of PROW that translates an algorithm to a LISP program. Thus PROW can be modified to write programs in any language.

COMPUTER SOLUTION OF CALCULUS WORD PROBLEMS* Eugene Charniak Massachusetts Ins:itute of Technology Cambridge, Massachusetts SUMMARY A program was written to solve calculus word problems. The program, CARPS (CAlculus Rate Problem Solver), is restricted to rate problems. The overall plan of the program is similar to Bobrow's STUDENT, the primary difference being the introduction of "structures" as the internal model in CARPS. Structures are stored internally as trees, each structure holding the information gathered about one object. It was found that the use of structures made CARPS more powerful than STUDENT in several respects. In calculus word problems it is not uncommon to have two or three sentences providing information for one equation. For example, in a problem about a filter, ALTITUDE was interpreted as ALTITUDE OF THE FILTER because CARPS knew that since the filter was a cone and cones have altitudes the filter had an altitude. The program has solved 14 calculus problems, most taken (sometimes with slight modifications) from standard calculus texts. CARPS is written in two languages. The bulk of the coding is in LISP.