An exciting new study from the University of Sheffield and published in the journal Swarm Intelligence has demonstrated (free pre-print version) a method of allowing computers to make sense of complex patterns all on their own, an ability that could open the door to some of the most advanced and speculative applications of artificial intelligence. Using an all-new technique called Turing Learning, the team managed to get an artificial intelligence to watch movements within a swarm of simple robots and figure out the rules that govern their behavior. It was not told to look for any particular signifier of swarm behavior, but simply to try to emulate the source more and more accurately and to learn from the results of that process. It's a simple system that the researchers think could be applied everywhere from human and animal behavior to biochemical analysis to personal security. Alan Turing was a multi-talented British mathematician who helped to both win the Second World War and invent the earliest computers, both while leading the Allied code-breaking efforts at Blechley Park.

It seems inevitable that we will soon be sharing the roads with autonomous vehicles, but it's not just other vehicles that our self-driving counterparts need to concern themselves with. Autonomous startup Drive.ai is working on technology that will help everyone communicate, potentially making our roads safer and more efficient. According to The Verge, Drive.ai's She's bringing her background in machine learning to autonomous vehicle development with the goal of creating a system that can learn behaviors rather than being forced to react to millions and millions of different scenarios. In addition to the machine learning, Drive.ai is working on a way for autonomous vehicles to communicate with others on the roads.

Here's How Google Will Use A.I. to Help Fight Cancer How UC Berkeley's New Center Could Prevent a Military A.I. Apocalypse Beauty.AI App the 1st international beauty contest judged by AI A treasure hunter went missing in the Rocky Mountains, and a computer algorithm found him ... Drive.ai wants to give self-driving cars more brainpower, personality

Google's DeepMind's Health initiative will soon work to explore how artificial intelligence could save hours and hours of precious time in the treatment of oral, head and neck cancers, the company announced on Tuesday. Whatever it is Zuckerberg is calling his personal home AI system. Yesterday he updated an audience in Rome about his personal project this year. He had Facebook (FB) engineers work on a computerized domestic assistant as his personal challenge. The AI controls his AC, lighting, opens his security gate, and can even make him toast.

AI is a field of technology where machines mimic human thinking and reasoning in their processing and carry out tasks usually performed by people. Several people have given various definitions for what actually is AI. Ram Sriharsha, senior architect and machine learning expert at Hortonworks, a data collection and data analytics software company, is quoted in Information Week as describing AI as being "very broad" and says "AI is being able to communicate, being able to plan and reason and take actions". The American author, computer scientist, inventor and futurist Raymond Kurzweil notes the most durable definition of AI is "the art of creating machines that perform functions that require intelligence when performed by people". However, he explains this quote is not a perfect one as it does not always fit "actual usage" and doesn't explain what exactly is meant by "artificial" or "intelligent".

The world's oldest board game still has a few moves to play. Go, a game of strategy and instinct considered more difficult to master than chess, was created roughly in the same era as the written word. The game is uniquely human - or, it was. Last year, a computer program called AlphaGo defeated an internationally ranked professional player. The computer's win signaled a significant evolution of information technology (IT) and artificial intelligence (AI), according to Fei-Yue Wang, a professor at the Chinese Academy of Sciences.

Here's How Google Will Use A.I. to Help Fight Cancer Could killer AI robots bring down America? How UC Berkeley's New Center Could Prevent a Military A.I. Apocalypse Beauty.AI App the 1st international beauty contest judged by AI A treasure hunter went missing in the Rocky Mountains, and a computer algorithm found him ... Drive.ai wants to give self-driving cars more brainpower, personality

Enterprise architecture (EA) is entering a new phase, driven by the ongoing splintering of enterprises. The discipline has been carefully crafted over the past couple of decades as a process-driven, well-documented exercise intended to develop a roadmap to prevent organizations from spending big money on technologies that don't produce value for the business. This model of EA worked well within established enterprises that viewed IT as a budget item that needed to be justified and monitored -- and constantly explained to executives. Nowadays, however, IT is integral to everything the business does, and is viewed as a strategic weapon. Tom Graves, a self-identified "enterprise architect, business-anarchist, confusionist,"whose work I greatly respect, says a major disruption is reshaping the role of EA and the mission of enterprise architects.

When I first became interested in using deep learning for computer vision I found it hard to get started. There were only a couple of open source projects available, they had little documentation, were very experimental, and relied on a lot of tricky-to-install dependencies. A lot of new projects have appeared since, but they're still aimed at vision researchers, so you'll still hit a lot of the same obstacles if you're approaching them from outside the field. In this article -- and the accompanying webcast -- I'm going to show you how to run a pre-built network, and then take you through the steps of training your own. I've listed the steps I followed to set up everything toward the end of the article, but because the process is so involved, I recommend you download a Vagrant virtual machine that I've pre-loaded with everything you need.

Consumer-facing predictive systems paint a seductive picture: espresso machines that start brewing just as you think it's a good time for coffee; office lights that dim when it's sunny and office workers don't need them; just in time diaper delivery. The value proposition is of a better user experience, but how will that experience actually be delivered when the systems involved regularly behave in unpredictable, often inscrutable, ways? Past machine learning systems in predictive maintenance and finance were designed by and for specialists, while recommender systems suggested, but rarely acted autonomously. Semi-autonomous machine learning-driven predictive systems are now in consumer-facing domains from smart homes to self-driving vehicles. Such systems aim to do everything from keeping plants healthy and homes safe to "nudging" people to change their behavior.