NVIDIA Tesla P100 GPUs and Tesla K80 GPUs will be available on Google Cloud Platform, starting early next year. Delivering the power of our Pascal GPU architecture from the cloud gives businesses another great option for helping to put their data to work and build AI services. On Google Cloud Platform, Tesla P100 GPUs will be available to Google Compute Engine and Google Cloud Machine Learning users around the world. The Tesla P100 delivers high performance and efficiency to power the most computationally demanding applications -- including a 12x increase in neural network training performance compared with a previous-generation offering. The Tesla K80 GPU accelerator delivers exceptional performance, with increased throughput that allows researchers to advance their scientific discoveries and developers to boost their web services.

Bill Gates calls this market the "holy grail" of computer science. There are currently 1,031 AI start-ups listed on AngelList, with an average valuation of $5.2 million -- which equals nearly $5.4 billion in venture capital investments. The three most-followed companies on that list are robotics company Autonomous, team productivity software maker Crux, and AI social news aggregator Zero Slant. Leo is a Tech and Consumer Goods Specialist who has covered the crossroads of Wall Street and Silicon Valley since 2012. His wheelhouse includes cloud, IoT, analytics, telecom, and gaming related businesses.

Google is improving its language translation service with a new approach that interprets whole sentences at a time rather than phrases piece-by-piece, the search giant announced Tuesday. The firm says that should make translations from the service, called Google Translate, easier to understand. "It uses this broader context to help it figure out the most relevant translation, which it then arranges and adjusts to be more like a human speaking with proper grammar," Barak Turovsky, product lead for Google Translate, wrote in a new blog post. This new version of Google Translate is powered by neutral machine translation, which is a new method of teaching computers to translate human languages, according to the Association for Computational Linguistics. Google published updates regarding its research into this field in September; it's now integrating the technique directly into Google Translate.

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This episode brings two esteemed experts to discuss these issues and present guidance for both commercial companies and the public sector policymakers. Dr. David A. Bray began work in public service at age 15, later serving in the private sector before returning as IT Chief for the CDC's Bioterrorism Preparedness and Response Program during 9/11; volunteering to deploy to Afghanistan to "think differently" on military and humanitarian issues; and serving as a Senior Executive advocating for increased information interoperability, cybersecurity, and civil liberty protections. He serves as a Visiting Executive In-Residence at Harvard University, a member of the Council on Foreign Relations, and a Visiting Associate at the University of Oxford. He has received both the Arthur S, Flemming Award and Roger W. Jones Award for Executive Leadership. In 2015, he was chosen to be an Eisenhower Fellow to Taiwan and Australia and in 2016, Business Insider named him one of the top "24 Americans Who Are Changing the World".

This is a game built with machine learning. You draw, and a neural network tries to guess what you're drawing. But the more you play with it, the more it will learn. So far we have trained it on a few hundred concepts, and we hope to add more over time. We made this as an example of how you can use machine learning in fun ways.

Defined as a way to provide computers with the ability to learn without the need for programming, the term Machine Learning seems to be this year's big buzzword. In fact today you'd be hard-pressed to find a security vendor that doesn't claim to incorporate some form of machine learning or AI technology into their threat prevention solution. So how much is true and how much is smoke and mirrors? Despite being a concept first envisioned back in the 1950s by Alan Turing, the lack of computing power has held research teams from making any significant jump towards realising the technology. Nowadays we all rely on machine learning without even knowing it – Google's search engine is powered by machine learning APIs, as is Facebook's personalised news feed.

We are at an incredible moment in technology history. Thanks to cloud computing power, more advanced algorithms and the availability of massive amounts of data, the artificial intelligence (AI) field has exploded – allowing computer scientists to create technology many of us only dreamed about just a few years ago. Using deep learning, computers today can recognize the words in a conversation about as well as a person does and provide real-time translation. Thanks to advances in fields such as reinforcement learning, we are making tangible progress in the effort to build systems that have true artificial intelligence. At Microsoft, we believe everyone deserves to be able to take advantage of these breakthroughs, in both their work and personal lives.

We have discussed some AI topics in the previous posts, and it should seem now obvious the extraordinary disruptive impact AI had over the past few years. However, what everyone is now thinking of is where AI will be in five years time. I find it useful then to describe a few emerging trends we start seeing today, as well as make few predictions around machine learning future developments. The following proposed list does not want to be either exhaustive or truth-in-stone, but it comes from a series of personal considerations that might be useful when thinking about the impact of AI on our world. Companies like Vicarious or Geometric Intelligence are working toward reducing the data burden needed to train neural networks.

Want to play around with Google's AI projects? Well, now you can thanks to a website which makes the company's tinkering with artificial intelligence available for the public to sample. If you head over to the AI Experiments web page, you'll find eight different experiments on offer, designed to illustrate what machine learning can do and appeal to a wider audience (with the option to submit your own work, to boot, if you've concocted anything along these lines yourself). The web-based apps include one which is an experiment in seeing whether a neural network can recognise a sketch. With Quick Draw, you simply doodle something on your touchscreen and the AI gives its best guess as to what that object is.