In 1950, Alan Turing, already famous for helping to crack the German Enigma code during World War II, devised the Turing test to define intelligence in machines. Could a computer, Turing asked, fool a human into thinking he was interacting with another person, or imitate human responses so well that it would be impossible for a person to tell the difference? If the machine could, Turing proposed, it could be considered intelligent. Turing's thought experiment spawned scores of science-fiction tales, such as the 2015 hit movie Ex Machina. Now, artificial intelligence (AI) and autonomous algorithms are not only passing the Turing test every day but, more importantly, are making and saving money for the businesses that deploy them.

The cheetah can accelerate from zero to 96.6 kph in under three seconds. These cases show where the Google approach gives inferior answers. If you type the first question into Google, you get a "Popular on the web" snippet with photos of several candidates. Google just reads this, even omitting any kind of pause after "web" and before "cheetah." To top it off, the correct answer isn't even in the list it reads, and appears 10th in the list of animals. In the second question, you also don't get the correct answer from Google.

Identity theft is often a multi-layered process. Once a thief gets one bit of your information, they try to use it to get more. The hackers behind the 2015 data breach of the US Internal Revenue Service (IRS), for example, used personal information they'd previously stolen from thousands of Americans to answer security questions on the IRS website, and in turn get access to their tax returns. The security questions asked about personal details, like, "On which of the following streets have you lived?" and, "What is your total scheduled monthly mortgage payment?" The hackers in the IRS case successfully got through that security measure, but what if the agency had a system in place that could detect whether the person answering the questions really was who they claimed to be?

If Robert Cardillo has his way, robots will perform 75 percent of the tasks currently done by American intelligence analysts who collect, analyze, and interpret images beamed from drones, satellites, and other feeds around the globe. Cardillo, the director of the National Geospatial-Intelligence Agency, known by the acronym NGA, announced his push toward "automation" and artificial intelligence at a conference this week in San Antonio. The annual conference, hosted by the United States Geospatial Intelligence Foundation, brings together technologists, soldiers, and intelligence professionals to discuss national security threats, changes in technology, and data collection and processing. Artificial intelligence is on the rise; former President Barack Obama's White House released a white paper on its potential future impacts in the final months of the administration. Police officers are using preliminary programs to predict the likelihood someone will commit a crime in a specific neighborhood based on crime statistics data.

He is the star of the Daily Telegraph's splash, which sees his unexpected comeback as Theresa May's attempt to avoid a leadership challenge. The newspaper thinks the prime minister is addressing complaints that she is too autocratic and unwilling to work with her critics. It also expects Mr Gove to become a key adviser on Brexit and a powerful ally in getting any deal through the Commons. The Sun says he is the biggest winner in the "post-election disaster shake-up". The Times interprets the limited reshuffle as a signal that Mrs May will go for a so-called soft Brexit. The newspaper says she faces overwhelming pressure to go for looser controls on immigration, a re-think on leaving the customs union, and a more flexible approach to the European Court of Justice.

One of the advantages that decision trees have over many other models is their ability to natively handle categorical predictors without having to first transform them (e.g., by using one-hot encoding). However, in this paper, we show how this capability can also lead to an inherent "absent levels" problem for decision tree based algorithms that, to the best of our knowledge, has never been thoroughly discussed, and whose consequences have never been carefully explored. This predicament occurs whenever there is indeterminacy in how to handle an observation that has reached a categorical split which was determined when the observation's level was absent during training. Although these incidents may appear to be innocuous, by using Leo Breiman and Adele Cutler's random forests FORTRAN code and the randomForest R package as motivating case studies, we show how overlooking the absent levels problem can systematically bias a model. Afterwards, we discuss some heuristics that can possibly be used to help mitigate the absent levels problem and, using three real data examples taken from public repositories, we demonstrate the superior performance and reliability of these heuristics over some of the existing approaches that are currently being employed in practice due to oversights in the software implementations of decision tree based algorithms. Given how extensively these algorithms have been used, it is conceivable that a sizable number of these models have been unknowingly and seriously affected by this issue---further emphasizing the need for the development of both theory and software that accounts for the absent levels problem.

Virtually every processor you see is based on the same basic (Von Neumann) computing model: they're designed to access large chunks of sequential data and fill their caches as often as possible. This isn't the quickest way to accomplish every task, however, and the American military wants to explore an entirely different kind of chip. DARPA is spending $80 million to fund the development of the world's first graph analytic processor. That's a much faster approach for handling large data, which frequently involves many relationships between info sets. It's also extremely scalable, so you can use as many HIVE chips as you need to accomplish your goals.

It's hard to talk about EA's multiplayer Star Wars shooter without accidentally stumbling over your words and mentioning the company's other large-scale war series: Battlefield. Long before Disney gave Electronic Arts the exclusive rights to create Star Wars video games, the Battlefront series was taking notes from DICE's own shooter -- draping science fiction trappings over the WWII game's vehicle combat, large battlefields and even its name. When EA took over the franchise its own game inspired, however, the resulting game was accused of being gorgeous, but shallow. Fortunately, the company seems to have heard player complaints. According to Star Wars Battlefront II executive producer Matt Webster, the next game in the series could play like a more accessible, but still sufficiently deep Battlefield title. Hints of this were all over the game's E3 reveal: a revamped multiplayer mode with character classes, a point-based progression system and, perhaps most importantly, the promise that all online DLC would be free.

I recently spent a week in Toronto and Montreal working with new startups and can't believe the passion and energy I felt there. The DNA of Canadian entrepreneurs is charged with technical competency, commitment to creating value and a drive to make a difference. Unfortunately the Canadian startup scene has been plagued with a lack of investment dollars and experience, resulting in few mergers and acquisitions and major IPOs. The conditions to create the next unicorn have never been met. Canada, a place where education in subjects like math has always been strong, hasn't seen the startup success of other countries.

When it comes to AI, however, there's a certain "black box" behind decisions that makes it so that even AI developers themselves don't quite understand or anticipate the decisions an AI is making. We do know that neural networks are taught to make these choices by exposing them to a huge data set. From there, AIs train themselves into applying what they learn. It's rather difficult to trust what one doesn't understand. The U.S. Defense Advanced Research Projects Agency (DARPA) wants to break this black box, and the first step is to fund eight computer science professors from Oregon State University (OSU) with a $6.5 million research grant.