What happens when we teach a computer how to learn? Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. Get caught up on a field that will change the way the computers around you behave ... sooner than you probably think.

Ask Apple's Siri digital assistant if she's evil, and she'll respond curtly, "Not really." Repeat a famous line from the movie "2001: A Space Odyssey," in which a computer on a spaceship kills nearly all the human crew, and Siri groans. And who can blame her? We humans have a morbid fascination with machines rising up to wipe us out or to enslave us as cocooned, flesh-and-blood battery packs. You can see that vision of the future streaming over Netflix whenever you want.

WASHINGTON/SAN FRANCISCO – Tesla Motors Inc. says the self-driving feature suspected of being involved in a fatal crash on May 7 is experimental, yet it's been installed on all 70,000 of its cars since October 2014. For groups that have lobbied for stronger safety rules, that's precisely what's wrong with U.S. regulators' increasingly anything-goes approach. "Allowing automakers to do their own testing, with no specific guidelines, means consumers are going to be the guinea pigs in this experiment," said Jackie Gillan, president for Advocates for Highway and Auto Safety, a longtime Washington consumer lobbyist who has helped shape numerous auto-technology mandates. "This is going to happen again and again and again." Tesla's use of a technology still in development, while common in its Silicon Valley home, contrasts with the cautious method of General Motors Co. and other automakers that have restricted their semi-autonomous cars to test tracks and professional drivers.

Finding insight in oceans of data is one of enterprises' most pressing challenges, and increasingly artificial intelligence is being brought in to help. Now, a new tool for Apache Spark aims to put machine learning within closer reach. Announced Friday, Sparkling Water 2.0 is a major new update from H2O.ai that's designed to make it easier for companies using Spark to bring machine-learning algorithms into their analyses. It's essentially an API (application programming interface) that lets Spark users tap H2O's open-source artificial-intelligence platform instead of -- or alongside -- the algorithms included in Spark's own MLlib machine-learning library. Among the highlights of the new software is the ability to run Spark and Scala through H2O's Flow user interface.

In recent times deep learning techniques have become more and more prevalent in NLP tasks; just take a look at the list of accepted papers at this year's NAACL conference, and you can't miss it. We've now completely moved away from traditional NLP approaches to focus on deep learning and how it can be leveraged in language problems, as successfully as it has in both image and audio recognition tasks. One of these approaches that has seen great success and is backed by a wave of research papers and funding is the concept of word embeddings. For those of you who aren't familiar with them, word embeddings are essentially dense vector representations of words. Similar to the way a painting might be a representation of a person, a word embedding is a representation of a word, using real-valued numbers.

In 1995, Bill Gates ( Trades, Portfolio) wrote the AAAInternet Tidal WaveAAA memo outlining how the Internet would affect Microsoft ( MSFT). Following GatesAAA example, current Microsoft CEO Satya Nadella published an article outlining artificial intelligenceAAAs significance for society on Tuesday. He discussed the opportunities and challenges ahead emphasizing that his vision is for A.I. and humans to work collaboratively and not in opposition to one another. GuruFocus has detected 9 Warning Signs with GLPI. Click here to check it out.

Score one for the combat geeks, after the news broke this week that a computer defeated a human fighter pilot in a virtual dogfight. For years, there has been a debate between those who sees drones as the inevitable future of air combat, and those who insist that a human must always be in the cockpit. But the drone crowd received a boost after researchers devised an artificial intelligence, or AI, that bested a human pilot in aerial combat. Sound too good to be true? The problem with designing a combat AI is that are so many variables to consider in combat, and those variables are so broad that the computer becomes overloaded. So scientists at software firm Psibernetix, the University of Cincinnati and the Air Force Research Laboratory devised ALPHA, an AI that uses "fuzzy logic" to calculate optimum tactics for questions such as which maneuvers to employ and which targets to engage.

The problem of how to defend a country changes when your attacker isn't acting rationally. Terrorists put their causes above their home country and don't necessarily fear death or retaliation. So shortly after 9/11, Milind Tambe, a professor of computer science and engineering at USC, proposed a radical new style of protection: Why not use artificial intelligence to make your own targets harder to attack? By matching predictive algorithms with machine learning and some massive processing power, you could create a computer program capable of figuring out how to deploy limited security forces around sensitive places most effectively. The trick would be for those schedules or formations to remain unpredictable.

MIT machine-learning system analyzed television shows including "The Office" to predict human interactions.

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