OpenAI, the billion-dollar San Francisco artificial intelligence lab backed by Tesla CEO Elon Musk, just unveiled a new virtual world. It's called Universe, and it's a virtual world like no other. It's a place where AI can learn to do just about anything. Other AI labs have built similar worlds where AI agents can learn on their own. Researchers at the University of Alberta offer the Atari Learning Environment, where agents can learn to play old Atari games like Breakout and Space Invaders. Microsoft offers Malmo, based on the game Minecraft.

"AI," "big data," and "machine learning" are all trending buzzwords, and you might be curious about how they apply to your domain. You might even have startups beating down your door, pitching you their new "AI-powered" product. So how can you know which problems in your business are amenable to machine learning? To decide, you need to think about the problem to be solved and the available data, and ask questions about feasibility, intuition, and expectations. Machine learning can help automate your processes, but not all automation problems require learning. Automation without learning is appropriate when the problem is relatively straightforward.

I spent the last three months learning about every artificial intelligence, machine learning, or data related startup I could find -- my current list has 2,529 of them to be exact. A few years ago, investors and startups were chasing "big data" (I helped put together a landscape on that industry). Now we're seeing a similar explosion of companies calling themselves artificial intelligence, machine learning, or somesuch -- collectively I call these "machine intelligence" (I'll get into the definitions in a second). Our fund, Bloomberg Beta, which is focused on the future of work, has been investing in these approaches. I created this landscape to start to put startups into context.

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This revision has corrected derivations, new R/JAGS code, and new diagrams.] Overview "Captain, the prior probability of this character dying and leaving the show is infinitesimal." A primary example of Bayes' rule is for disease diagnosis (or illicit drug screening). The example is invoked routinely to explain the importance of prior probabilities. Here's one version of it: Suppose a diagnostic test has a 97% detection rate and a 5% false alarm rate.

Don't worry about the robots, worry about the humans. There's a huge difference between the modest aims of the artificial intelligence (AI) and machine learning being used today, and the grand ideas of creating an artificial general intelligence that could match -- and then rapidly exceed - the capabilities of a human mind As they develop, AI and machine learning will be able to take on even more complicated tasks, but it could still be half a century or more before AI capable of human-level intelligence is built. And, then, even longer before the sort of super-intelligence emerges that excites some, terrifies others and has provided plot lines for science fiction for decades. One may (eventually) lead to the other, but conflating today's AI and machine learning with tomorrow's Skynet is not helpful. Indeed, that confusion has encouraged many to exaggerate the short-term potential of existing (and often somewhat mundane) AI and machine learning technologies.

Smoking cigarettes is the leading cause of lung cancer, the most common cause of cancer death in the world. But nearly 20 percent of lung-cancer diagnoses are made in people who are non-smokers. That means in addition to smoking, geographic, demographic and genetic factors play a role in the devastating disease. A project from Microsoft's research labs is exploring the feasibility of using anonymized web search data to learn more about lung-cancer risk factors and provide early warning to people who are candidates for disease screening. The findings, published Thursday in JAMA Oncology, extend research that team members published last June on the feasibility of using the text of questions people ask search engines to predict diagnoses of pancreatic cancer.

Artificial intelligence software can now help cops spot new or previously unknown child sexual abuse media and prosecute offenders. The toolkit, described in a paper published in Digital Investigation, automatically detects new child sexual abuse photos and videos in online peer-to-peer networks. The new approach combines automatic filename and media analysis techniques in an intelligent filtering module, which can identify new criminal media and distinguish it from other media being shared, such as adult pornography. Spotting newly produced media online can give law enforcement agencies the fresh evidence they need to find and prosecute offenders. "Identifying new child sexual abuse media is critical because it can indicate recent or ongoing child abuse," said lead study author Claudia Peersman from Lancaster University.

NEW DELHI: In a first for Indian carriers, Air India is rationing the number of alcoholic drinks being served to its premium international passengers at airport lounges across India. The AI move follows a recent plea by several Indian airlines to aviation authorities to restrict passengers' access to liquor at departure terminals to check increasing incidents of unruly behaviour by tipsy flyers. Tagged In Artificial Intelligence India Airport Beer Kolkata Chhatrapati Shivaji International Airport Air India Indira Gandhi International Airport Wine Gin Rum Chennai International Airport Indian (airline) Go Air Indi Go Airport Lounge Rajiv Gandhi International Airport Recently actually had the time to visit a very controversial exhibition in Palazzo Strozzi, by Chinese artist Ai WeiWei. Apple has given its clearest indication yet that it's working on a self-driving car – or at least working with car manufacturers to make the plans a reality. Tagged In The Wall Street Journal Wired (magazine) Apple Inc Washington, D C Minivan Mc Laren Machine Learning National Highway Traffic Safety Administration Victoria (australia) Financial Times Mc Laren Technology Group Paul Krugman would stand in line to meet Charlie Stross.