The X Prize Foundation and IBM have officially launched a global artificial intelligence competition that provides participants with the chance to compete for 5 million. The contest, dubbed "IBM Watson A.I. XPRIZE: A Cognitive Computing Competition," was announced this morning at TED2016. The competition invites teams from around the world to harness the power of artificial intelligence (AI) in order to brainstorm a solution to some of the planet's most pressing problems. "X Prize believes that artificial intelligence is the best tool in our toolkit to address the world's grand challenges and biggest problems," said Stephanie Wander, X Prize development associate and lead prize designer, to IFLScience. Which grand problem will the developers have to tackle?

It uses artificial intelligence (AI) to automatically convert data into semantic knowledge. Diffbot uses deep-learning technology to categorize webpage data according to its meaning. Essentially, it provides users with structured knowledge sorted in human-like categories. The web-scrapping company can suck up content from homepages, articles, products, and social network profiles, which can aid both, app developers as well as enterprises, to do competitive analysis and gain insights into consumers. With the use of AI, Diffbot has already surpassed the data bank of Google's Knowledge Graph.

Artificial intelligence (AI) developed by a University of Cincinnati doctoral graduate was recently assessed by subject-matter expert and retired United States Air Force Colonel Gene Lee - who holds extensive aerial combat experience as an instructor and Air Battle Manager with considerable fighter aircraft expertise - in a high-fidelity air combat simulator. The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is "the most aggressive, responsive, dynamic and credible AI I've seen to date." Details on ALPHA - a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

When Tesla Motors CEO Elon Musk proclaims that artificial intelligence is "our biggest existential threat," it makes headlines worldwide. But what goes unreported is that the very search engines people used to find Musk's comments are themselves an example of how AI has subtly but forcefully become a part of everyday, real-world life. When it comes to a discussion of AI, it helps to have a sense of history--as well as a sense of humor. Thanks to premonitory proclamations by Musk, Microsoft's Bill Gates, Cambridge's Stephen Hawking and other prominent technologists, AI has become a popular topic again, after a 20-year cooling-off period. It's tempting to assume that the "dire warnings" about AI being a threat to mankind were mostly tongue-in-cheek, but the end result is that just as it happened in the 1980s and '90s, the hype over AI is again outpacing the reality (virtual and otherwise). The first question that needs to be answered though is: Whatever happened to AI and why did it go underground for so many years?

Not in that gadget-y way, like when a laptop screen turns on, though. The robot slowly raises its head and opens one eye, then the other, as if the light of the world is just too much. Sofman, CEO of robotics company Anki, chuckles as the it shakes off the rust of sleep and ambles off its charging cradle. After circling the table a moment, it drives quickly to the edge. It only pauses once it's driven halfway off.

This structural design can support calculations being made upon thousands of layers, and it was this aspect of the architecture that gave rise to the name'deep learning'. Marchand-Maillet explains: "Each artificial neurone is assigned an input value, which it computes using a mathematical function, only firing if the output exceeds a pre-defined threshold." In this way, it reproduces the behaviour of real neurones, which only fire and transmit information when the input signal (the potential difference across the entire neural circuit) reaches a certain level. In the artificial model, the results of a single layer are weighted, added up and then sent as the input signal to the following layer, which processes that input using different functions, and so on and so forth. For example, if a system is trained with great quantities of photos of apples and watermelons, it will progressively learn to distinguish them on the basis of diameter, says Marchand-Maillet. If it cannot decide (e.g., when processing a picture of a tiny watermelon), the subsequent layers take over by analysing the colours or textures of the fruit in the photo, and so on.

This repository contains Python code for a selection of tables, figures and LAB sections from the book'An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani (2013). This great book gives a thorough introduction to the field of Statistical/Machine Learning. The book is available for download (see link below), but I think this is one of those books that is definitely worth buying. The book contains sections with applications in R based on public datasets available for download or which are part of the R-package ISLR. Furthermore, there is a Stanford University online course based on this book and taught by the authors (See course catalogue for current schedule).

To recap the last tutorial: we got comfortable with Python for re-implementing the models we originally imagined in Excel. By using a programming language, we were able to (1) use more powerful constructs and methods, like arrays to store and retrieve variables, and (2) to write scripted steps that can be repeated in the future without us performing the work by hand. However, you may be thinking that you found it easier to understand what's in the data back when you were using Excel. Well, in this third tutorial we will take a slight detour from our modeling work in order to bridge that gap. Python has another great package called Pandas, which makes data exploration and data cleaning much easier to do than manipulating arrays.

The future of legal research assistance lawyers is zero. The job will soon vanish for all practical purposes. Futurism reports "Ross, the world's first artificially intelligent attorney, has its first official law firm. Baker & Hostetler announced that they will be employing Ross for its bankruptcy practice, currently comprised of almost 50 lawyers." Please consider Artificially Intelligent Lawyer "Ross" Has Been Hired By Its First Official Law Firm.

Not in that gadget-y way, like when a laptop screen turns on, though. The robot slowly raises its head and opens one eye, then the other, as if the light of the world is just too much. Sofman, CEO of robotics company Anki, chuckles as the it shakes off the rust of sleep and ambles off its charging cradle. After circling the table a moment, it drives quickly to the edge. It only pauses once it's driven halfway off.