An internet pioneer who first taught the world how to find friendship, love, and sex online a quarter century ago is trying to determine at what point artificial intelligence develops emotional intelligence, and he thinks he can do it with an art contest. About 31 different robots competed for 100,000 in prizes at the first annual International Robot Art Competition. The vote was based on 2,200 votes cast on Facebook as well as judgment from six art critics who have experience working with technology. The fan and judge favorite was TAIDA from the National Taiwan University. TAIDA won first place and 30,000 for several Pointillism-style works, including a still life of fruit, landscape of the Taiwan coast, and a portrait of Albert Einstein.

The artificial intelligence revolution is clearly happening. A.I. will transform medicine, give us all super-smart virtual assistants, fight crime and a thousand things more. In order for A.I. to work its miracles, it's going to need data. And I'm predicting that we'll willingly give that data. Do you use Siri, Google Now, Cortana or Alexa?

Spring may finally have arrived for artificial intelligence, Google executives said Friday. Speaking at the Google I/O developers conference in Mountain View, executives said that artificial intelligence and machine learning have advanced to the point where they are proving genuinely useful, through such technologies as speech recognition and language translation. But there remains great room for improvement. "We've seen extraordinary results in fields that hadn't really moved the needle for many years," said John Giannandrea, vice president of engineering for Google. "I think we're in an AI spring right now."

Back-Propagation is the most common algorithm for training neural networks. Here's how to implement it in C#. Back-propagation is the most common algorithm used to train neural networks. There are many ways that back-propagation can be implemented. This article presents a code implementation, using C#, which closely mirrors the terminology and explanation of back-propagation given in the Wikipedia entry on the topic.

Machine Learning (ML) services are quickly becoming a taken-for-granted part of the software developer's toolbox, in any domain. These days, databases or networking are a standard component of almost any non-trivial application, so easily integrated that almost no special expertise is required. We expect to see Machine Learning becoming, in the very near future, a similar layer in the software stack. This commoditization of ML services has been driven so far by Service-oriented platforms such as BigML, which have provided a key ingredient of the process: abstraction. Simple and easy to use REST APIs hide away not only the details of the sophisticated algorithms underlying the services at hand, but also the complexities of scaling those computations both over CPU cycles and input data volumes.

Google has developed its own accelerator chips for artificial intelligence it calls tensor processing units (TPUs) after the open source TensorFlow algorithms it released last year. The news was the big surprise saved for the end of a two-hour keynote at the search giant s annual Google IO event in the heart of Silicon Valley. We have started building tensor processing units TPUs are an order of magnitude higher performance per Watt than commercial FPGAs and GPUs, they powered the AlphaGo system, said Sundar Pichai, Google s chief executive, citing the Google computer that beat a human Go champion. The accelerators have been running in Google s data centers for more than a year, according to a blog by Norm Jouppi, a distinguished hardware engineer at Google. TPUs already power many applications at Google, including RankBrain, used to improve the relevancy of search results and Street View, to improve the accuracy and quality of our maps and navigation, he said.

When he introduced the TPU at the I/O conference, Google CEO Sundar Pichai said it provides an order of magnitude better performance per watt than existing chips for machine learning tasks. While it's unlikely to usurp CPUs and GPUs already in use in the machine learning world, the TPU could potentially speed the machine learning process without using much more energy. Google has been carefully guarding the details of the TPU project, but it's been generally understood that the project was in progress. Based on the company's job postings, it had become evident over the past year that Google was working on a chip of some kind. Now, Google confirms the chip has been under development for about two years.

Then there are'quantum machine learning algorithms,' developed over the last decade following a breakthrough by Harrow, Hassidim, and Lloyd, which do address problems like clustering, classification, support-vector machines, etc. But these algorithms typically require a bunch of conditions to work: for example, that the data are well-conditioned; that they can be accessed in quantum superposition (for example, using a "quantum RAM") or else computed on the fly; and that the properties of the data one cares about can actually be estimated by measuring the resulting quantum states. And we don't yet know how often those conditions will hold in practical applications---and equally important, in the cases where they do hold, we don't have strong evidence that there couldn't be classical random sampling algorithms with similar performance to the quantum algorithms.

When all you have is a hammer, everything looks like a nail. When neural networks are hot in the field of machine learning, everything looks like a pattern-matching problem. When you leave the milk out, a robot will tell you. Watch-Bot is basically your mom, if your mom was a Kinect sensor programmed with unsupervised learning algorithms. It's trained on data from videos of regular people doing regular people things.

Life would be pretty dull without imagination. In fact, maybe the biggest problem for computers is that they don't have any. That's the belief motivating the founders of Vicarious, an enigmatic AI company backed by some of the most famous and successful names in Silicon Valley. Vicarious is developing a new way of processing data, inspired by the way information seems to flow through the brain. The company's leaders say this gives computers something akin to imagination, which they hope will help make the machines a lot smarter.