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.

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.