Memristive devices can mimic brain's capability to change synaptic connectivity MoneyLion secures $22.5M to bring fresh talent to AI finance management Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

It is the end of the year again and a lot has happened in 2016. Google's AlphGo algorithm beat Lee Se-dol in the game of Go, Blockchain really took off and governments around the globe are investing heavily in smart cities. As every year, I will provide you with the big data trends for the upcoming year, just as I did for 2014, 2015 and 2016. The Big Data hype is finally over and, therefore, we can finally get started with Big Data. That is why I would like to call 2017 the Year of Intelligence.

The artificial intelligence researcher Russ Salakhutdinov made headlines today when he said was going to start publishing journal articles and spending time talking to academics. That wouldn't be news, except Salakhutdinov works for Apple--a company famous for an extreme breed of corporate secrecy. Over the past two decades, people who work at Apple haven't talked to much of anyone about the far-reaching research (or anything else) happening inside the company. And that certainly includes academics. Uber Buys a Mysterious Startup to Make Itself an AI Company In OpenAI's Universe, Computers Learn to Use Apps Like Humans Do Google's AI Reads Retinas to Prevent Blindness in Diabetics Google's Hand-Fed AI Now Gives Answers, Not Just Search Results In OpenAI's Universe, Computers Learn to Use Apps Like Humans Do In OpenAI's Universe, Computers Learn to Use Apps Like Humans Do Google's AI Reads Retinas to Prevent Blindness in Diabetics Google's AI Reads Retinas to Prevent Blindness in Diabetics Google's Hand-Fed AI Now Gives Answers, Not Just Search Results Google's Hand-Fed AI Now Gives Answers, Not Just Search Results But Salakhutdinov works in an area where secrecy just doesn't play.

More and more, 67-year-old Washington resident Lon Coleman feels like he's wandering through a fog. He walks into the living room and forgets why, or makes a phone call only to blank on whose number he dialed. An author of three books who once wrote up to five poems a day, now the lines that spring to his mind often slip away as soon as he puts pencil to paper. Sometimes the fog clears, and when his memory comes back, "it's amazing," he says. "Sometimes it doesn't, I have to admit."

As is so often the case, the giants of the Internet are chasing the same sparkly vision of the future: chatbots. In the coming months and years, these companies promise, you'll chat with Internet services in much the same way you now chat with friends and family. Bots will instantly answer questions, respond to requests, and even anticipate your needs. While chatting with some some old college pals about an upcoming reunion, you'll ask an OpenTable bot for restaurant recommendations. Google's New Allo Messaging App Gets Its Edge From AI Google Has Open Sourced SyntaxNet, Its AI for Understanding Language Facebook Open Sources Its AI Hardware as It Races Google Google's New Allo Messaging App Gets Its Edge From AI Google's New Allo Messaging App Gets Its Edge From AI But a major challenge remains: building chatbots that can actually chat.

MoneyLion secures $22.5M to bring fresh talent to AI finance management Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research. Despite recent successes of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential learning, LSTM neural networks have not, by themselves, been able to generate natural-sounding music conforming to music theory. To transcend this inadequacy, we put forward a novel method for music composition that combines the LSTM with Grammars motivated by music theory. The main tenets of music theory are encoded as grammar argumented (GA) filters on the training data, such that the machine can be trained to generate music inheriting the naturalness of human-composed pieces from the original dataset while adhering to the rules of music theory. Unlike previous approaches, pitches and durations are encoded as one semantic entity, which we refer to as note-level encoding. This allows easy implementation of music theory grammars, as well as closer emulation of the thinking pattern of a musician. Although the GA rules are applied to the training data and never directly to the LSTM music generation, our machine still composes music that possess high incidences of diatonic scale notes, small pitch intervals and chords, in deference to music theory.

Deep neural networks have had an enormous impact on image analysis. State-of-the-art training methods, based on weight decay and DropOut, result in impressive performance when a very large training set is available. However, they tend to have large problems overfitting to small data sets. Indeed, the available regularization methods deal with the complexity of the network function only indirectly. In this paper, we study the feasibility of directly using the $L_2$ function norm for regularization. Two methods to integrate this new regularization in the stochastic backpropagation are proposed. Moreover, the convergence of these new algorithms is studied. We finally show that they outperform the state-of-the-art methods in the low sample regime on benchmark datasets (MNIST and CIFAR10). The obtained results demonstrate very clear improvement, especially in the context of small sample regimes with data laying in a low dimensional manifold. Source code of the method can be found at \url{https://github.com/AmalRT/DNN_Reg}.

Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing different methods, it is not the natural way that humans annotate images. The number of annotated tags should depend on image contents. Inspired by the recent progress in machine translation and image captioning, we propose a novel Recurrent Image Annotator (RIA) model that forms image annotation task as a sequence generation problem so that RIA can natively predict the proper length of tags according to image contents. We evaluate the proposed model on various image annotation datasets. In addition to comparing our model with existing methods using the conventional top-k evaluation measures, we also provide our model as a high quality baseline for the arbitrary length image tagging task. Moreover, the results of our experiments show that the order of tags in training phase has a great impact on the final annotation performance.

The online giant has revealed its advanced concept for a store utilizing "Just Walk Out" technology. SAN FRANCISCO -- The Amazon Go grocery store, now in the testing stage in Seattle, has captured the public's attention. At a time of year when many people are standing in long lines to do their holiday shopping, the idea of being able to walk into a store, pick things up, walk out and have everything automatically charged to a credit card sounds like a dream come true. The video the Seattle company released online Monday said nothing about the underlying technology beyond some buzz-words: computer vision, deep learning algorithms and sensor fusion. But a patent filed by the company in 2014 and published in 2015 may shed some light on the process, and it looks like it's all about cameras and microphones.