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.

Oracle are poised to help customers make the leap from on premise, up into the cloud. At Infosecurity Europe 2017, you couldn't escape the buzz of what will may soon become the next great battle on the cybersecurity frontier – automation. However, the belief that automation is a cyber security silver bullet is not one that is well believed it seems, with Oracle's Rohit Gupta telling CBR that not all situations can be solely monitored by technology. "There are certain conditions where automation will never be accepted. As an example, let's say the system discovers something suspicious going on in an executive's credentials, the CFO's credentials. You don't want to turn off the access between the CFO and his or her system, that could be career suicide, you never want that to happen. "In that scenario you have policy in place that says for these specific types of roles, or these specific types of entitlements, I want to have human intervention – somebody to look over this detected issue ...

Facts matter: but is that statement a fact? Suppose it could be proven or refuted – what then? What's the best way to tell someone they've made an error? How do you nudge the world in the direction of truth? These are the questions professional factcheckers wrestle with on a daily basis.

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.

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If you have ever been on a ship, you might have realized navigation is the most technical and the most important part of sailing -- a slight error in calculation might literally sink you. And such errors, in the past, have been caused mostly by humans. Japan has found a unique solution to the problem -- self-navigating ships. These "smart ships" will find the safest, shortest and most fuel-efficient routes using artificial intelligence (AI) and could be put into service by 2025. The AI tech is expected to make shipping safer by not just potting the course, but also detecting machinery malfunctions and other problems way in advance.