In addition to conducting pure research in artificial intelligence and machine learning (ML), the group will also develop new tools and products that make use of the technology. The company noted that it already offers several services to consumers that are based on machine learning technology, such as Google Translate, Photo Search, and Smart Reply for Inbox. The new research center is part of a broader initiative at Google to advance the field of machine learning. Researchers working at the company's existing engineering offices in Zurich have already made major contributions to the field, such as developing the conversation engine that powers the Google Assistant in the Allo smart messaging app, and developing the engine that powers Knowledge Graph. The Zurich office is already the company's largest engineering office outside of the US.

NVIDIA's (NASDAQ:NVDA) stock price is up 113% over the past 12 months, the company's revenues were up 13% year-over-year in the first quarter of its fiscal 2017, and CEO Jen-Hsun Huang says the company is seeing growth across all of its platforms. So why is NVIDIA in such a good position, and how might it stay there? Let's take a look at three things the company is doing well to answer that. The most obvious advantage the company has right now is its graphics processing unit (GPU) business. NVIDIA currently holds 76% of the discrete desktop GPU market, and it's continually growing the business.

The New York City taxi dataset is one of the largest publicly available datasets, with information about 1.1 billion NYC taxi rides. This dataset has been explored and visualized in a number of blog posts, using a variety of techniques and technologies (e.g., PostgreSQL, Apache Elastic Search). A recent blog post showed how to build ML models over one years' worth of this dataset using MRS running in a 4-node Hadoop cluster. In a new blog post, Microsoft Data Scientist Dmitry Pechyoni shows us how to build a binary classification model that will predict if a passenger will pay a tip. Dmitry was able to use Microsoft R Server (MRS) to drive the entire process of building and evaluating machine learning models over hundreds of millions of examples using a single commodity machine.

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. The company first integrated peer-to-peer payments into Messenger in 2015 and then launched a full chatbot API so businesses can create interactions for customers to occur within the Facebook Messenger app. While the most common uses of the device include playing music, making informational queries, and controlling home devices, Alexa (the device's default addressable name) can also tap into Amazon's full product catalog as well as your order history and intelligently carry out commands to buy stuff. Through Amazon's developer platform for the Echo (called Alexa Skills), developers can develop "skills" for Alexa that enable her to carry out new types of tasks.

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.