Everything You Know About Artificial Intelligence is Wrong. Will the Singularity Artificial General Intelligence winners be Hedge Fund Managers, the Military and Spy Agencies? When Artificial Intelligence (AI) work began over 50 years ago, the AI field was directly aimed at the construction of "thinking machines"--that is, computer systems with human-like general intelligence. But this goal proved very difficult to achieve; and so, over the years, AI researchers have come to focus mainly on producing "narrow AI" systems: software displaying intelligence regarding specific tasks in relatively narrow domains. This "narrow AI" work has often been exciting and successful.

The combination of drinking, social media, and sharing has provided Rochester researchers with an innovative test case for analyzing ongoing behavior by Twitter users and then using this analysis to study patterns about drinking in different communities. In a new paper, PhD student Nabil Hossain reports that he and his collaborators have taught computers to analyze tweets about drinking in an effort to predict where Twitter users are when they report drinking. Heatmaps show concentrations of tweets while drinking from Monroe County, NY (left) and New York City. Hossain is a student in the computer science group led by Henry Kautz, the Robin and Tim Wentworth Director of the Goergen Institute for Data Science. He posted the paper on the arXiv.org

The latest computer games can be fantastically realistic. Surprisingly, these lifelike virtual worlds might have some educational value, too--especially for fledgling AI algorithms. Adrien Gaidon, a computer scientist at Xerox Research Center Europe in Grenoble, France, remembers watching someone play the video game Assassins Creed when he realized that the game's photo-realistic scenery might offer a useful way to teach AI algorithms about the real world. Gaidon is now testing this idea by developing highly realistic 3-D environments for training algorithms how to recognize particular real-world objects or scenarios. The idea is important because cutting-edge AI algorithms need to feed on huge quantities of data in order to learn to perform a task.

With ever increasing data, Machine Learning is becoming the only way to get analytics done, making it possible to glean insights from vast amounts of data. But when starting a project, it is easy to ignore a critical fact: the value of data is also time sensitive – it expires! In order to get the highest value from data, Machine Learning needs to be applied in a rapid and repeatable way, so you can go from data to insight quickly. A Machine Learning API makes this possible. In this workshop, Poul Petersen CIO of BigML will give an overview of BigML's Machine Learning API and then show real-world examples of predictive applications that can be built using Python and node.js. Several tools that have been built on top of BigML's API will be demonstrated including a loan risk assessment, real estate arbitrage, and the world's first voice controlled predictive assistant.

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. Thereby we will see that a neuron and a convolutional net are very similar information processing machines. While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century. This blog post is complex as it arcs over multiple topics in order to unify them into a coherent framework of thought. I have tried to make this article as readable as possible, but I might have not succeeded in all places.

IBM announced that its Watson AI is getting image recognition capabilities earlier in the year, but this site that lets you feed in your own photos to see what it thinks is in them is both impressive and scary. The visual recognition demo lets you give Watson an image URL or upload a photo and it'll come back in a few seconds with what it thinks it sees. This year's edition of TNW Conference in Amsterdam includes some of the biggest names in tech. In my tests I fed Watson a few random photos I had on hand and the accuracy was quite surprising. It could figure out what was in landscape shots, animals (down to the breed) and even what's in the background.

SAN FRANCISCO, March 21 (Reuters) - Andy Grove, the Silicon Valley elder statesman who made Intel into the world's top chipmaker and helped usher in the personal computer age, died on Tuesday at age 79, Intel said. The company did not describe the circumstances of his death but Grove, who endured the Nazi occupation of Hungary during World War Two, living under a fake name, and came to the United States to escape the chaos of Soviet rule, had suffered from Parkinson's. Grove was Intel's first hire after it was founded in 1968 and became the practical-minded member of a triumvirate that eventually led "Intel Inside" processors to be used in more than 80 percent of the world's personal computers. With his motto "only the paranoid survive," which became the title of his best-selling management book, Grove championed an innovative environment within Intel that became a blueprint for successful California startups. Grove, who was named man of the year by Time magazine in 1997, encouraged disagreement and insisted employees be vigilant of disruptions in industry and technology that could be major dangers - or opportunities - for Intel.

While many tech moguls dream of changing the way we live with new smart devices or social media apps, one Russian internet millionaire is trying to change nothing less than our destiny, by making it possible to upload a human brain to a computer, reports Tristan Quinn. "Within the next 30 years," promises Dmitry Itskov, "I am going to make sure that we can all live forever." It sounds preposterous, but there is no doubting the seriousness of this softly spoken 35-year-old, who says he left the business world to devote himself to something more useful to humanity. "I'm 100% confident it will happen. Otherwise I wouldn't have started it," he says. It is a breathtaking ambition, but could it actually be done?

Artificial intelligence is breathing down our necks: Software built by Google startled the field last week by easily defeating the world's best player of the Asian board game Go in a five-game match. Go resembles chess in the deep, complex problems it poses but is even harder to play and has resisted AI researchers longer. It requires mastery of strategy and tactics while you conceal your own plans and try to read your opponent's. Mastering Go fits well into the ambitious goals of AI research. It shows us how much has been accomplished and forces us to confront, as never before, AI's future plans.

It may not look like one of Isaac Asimov's robots or sound like HAL from "2001: A Space Odyssey," but artificial intelligence is here, and it is already having a huge impact on how the world works. From the way you shop for a pair of shoes online to how fast a Formula 1 team can push its car's engine, AI is helping businesses across the globe save millions by improving performance and efficiency. Still, problems like trust and security, not to mention fears of the so-called singularity, when artificial intelligence would overtake human thinking, remain hurdles that the technology must overcome before it goes mainstream. AI hit the news this week after a program called AlphaGo, developed by engineers at DeepMind, the AI startup acquired by Google in 2014 for 580 million, defeated the world's No. 1 Go player Lee Sedol. AlphaGo beat Sedol 4 games to 1, claiming a 1 million prize.