Machine learning algorithms and artificial intelligence tools are receiving a lot of attention in the analytics world these days, and industry experts and experienced users say the plaudits are well-deserved. "These models are making a big difference, and if you're not considering how to use them in your product, you probably should," said Jeff Dean, a senior fellow at Google who helped lead development of TensorFlow, the company's open source machine learning platform. Machine learning has come to play a central role in the majority of new products Google develops, Dean said in a presentation at Spark Summit 2016 in San Francisco. For example, it's at the core of training speech-recognition tools used in the Android mobile operating system. Machine learning technology also helped Google create a tool that automatically tags photos uploaded by users by examining what's happening in the photo.

Artificial intelligence has shown what it can do when facing off against humans in ancient board games, with Deep Blue and Alpha Go already proving their worth on the world stage. While computers playing chess is nothing new, an online version of the ancient game lifts the veil of AI to let players see what the AI is thinking. You make your move and then see the computer come to life, calculating thousands of possible counter moves. Thinking Machine 6 is an AI-based concept art piece created by Martin Wattenberg. Rather than making players into chess champions, it shows the AI thinking process.

The importance of algorithms in our lives today cannot be overstated. They are used virtually everywhere, from financial institutions to dating sites. But some algorithms shape and control our world more than others -- and these ten are the most significant. Just a quick refresher before we get started. Though there's no formal definition, computer scientists describe algorithms as a set of rules that define a sequence of operations.

This week we posted a new Tech Note in which Jesse Engel discusses a new technique for speeding up the training of deep recurrent neural networks. This is Part II of a multi-part series detailing some of the techniques we've used here at Baidu's Silicon Valley AI Lab (SVAIL) to accelerate the training of recurrent neural networks. While Part I focused on the role that minibatch and memory layout play on recurrent GEMM performance, we shift our focus here to tricks we can use to optimize the algorithms themselves. There are two main takeaways in this blog post. First, differentiable graphs are a simple and useful tool for visually calculating complicated derivatives.

Retailers and technology firms are experimenting with chatbots, powered by a combination of machine learning, natural language processing, and live operators, to provide customer service, sales support, and other commerce-related functions. Chris Messina of Uber recently coined the term "conversational commerce" to describe this movement, which he defines as: The net result is that you and I will be talking to brands and companies over Facebook Messenger, WhatsApp, Telegram, Slack, and elsewhere before year's end, and will find it normal. While messaging and voice interfaces are central components, they fit into a larger picture of increasing infusion of technology into our daily lives, which in turn is unlocking new potential for brand-to-consumer interaction. The fact is, technology overall is becoming more deeply woven into our lives, and the entire ecosystem is enjoying tighter cohesion through the increasing availability and sophistication of APIs. Smart companies are finding new and innovative touch points with consumers that are contextual, relevant, highly personal, and, yes, conversational.

His cursory description of the role -- "with the task to make machine learning as easy to use and widespread as it could possibly be" -- echoes Google's stated strategy. Both companies are competing for businesses to pay for their cloud services and for researchers with AI expertise. My favorite nugget of Smola's announcement: He only posted his full statement, intended just for CMU, because it leaked on Weibo, the social network in China, where machine learning is the rage and where Silicon Valley biggies want to be. CNBC's parent NBCUniversal is an investor in Recode's parent Vox, and the companies have a content-sharing arrangement.

Final month Google revealed that it's making its personal customized synthetic intelligence processing chips. Now it seems that you could be already be utilizing them. The chips are referred to as TPUs, quick for Tensor Processing Unit, named for Google's open supply AI framework, TensorFlow. At the moment on the 2016 WIRED Enterprise Convention in New York Metropolis, the top of Google's cloud enterprise, Diane Greene, mentioned that Google is already utilizing TPUs in additional than a hundred initiatives. These embrace Android's voice recognition system, the "Sensible Reply" function of its Inbox app, and Google's new cross-software search service Springboard--briefly, stuff you could already be utilizing.

Microsoft Corp. has agreed to acquire Wand Labs, a startup whose messaging technology will help upgrade the software giant's efforts in chatbots. Financial terms of the deal weren't disclosed. Earlier this week, Microsoft MSFT, 1.41% agreed to a blockbuster acquisition of LinkedIn Corp. LNKD, 0.02% for 26.2 billion. Redwood City, Calif.-based Wand Labs will join the Bing engineering and platform group at Microsoft. The Bing group has been developing technology that fits into Microsoft's strategy surrounding what Chief Executive Satya Nadella calls "conversation as a platform."

ROOBO, a fast-growing hardware and AI startup headquartered in Beijing, today unveiled a prototype of its newest product, a "pet robot" called Domgy. For the unfamiliar, ROOBO is the company behind Pudding, a voice-controlled, educational robot for kids. Pudding is used to teach kids vocabulary, geography, jokes and more. The company also makes the Idealens virtual reality headset, Skyseries drone and Runbone earbuds. Since its founding in 2014, ROOBO has grown to 300 employees, with 7 worldwide offices, including one in Seattle. The company previously raised a Series A round of venture funding, but declined to say how much capital they raised, who their investors are, or how much revenue they have generated to-date.

We combine Riemannian geometry with the mean field theory of high dimensional chaos to study the nature of signal propagation in generic, deep neural networks with random weights. Our results reveal an order-to-chaos expressivity phase transition, with networks in the chaotic phase computing nonlinear functions whose global curvature grows exponentially with depth but not width. We prove this generic class of deep random functions cannot be efficiently computed by any shallow network, going beyond prior work restricted to the analysis of single functions. Moreover, we formalize and quantitatively demonstrate the long conjectured idea that deep networks can disentangle highly curved manifolds in input space into flat manifolds in hidden space. Our theoretical analysis of the expressive power of deep networks broadly applies to arbitrary nonlinearities, and provides a quantitative underpinning for previously abstract notions about the geometry of deep functions.