Speaking at Google Cloud Platform in San Francisco, the chairman of Alphabet and former Google CEO highlighted artificial intelligence as the "next evolution" in computing. This will will spur the creation of new apps and services, he said. "Machine learning and crowdsourcing data will be the basis and fundamentals of every successful, huge IPO win in five years, in the same sense that the transition to [mobile] apps five years ago created the modern corporations of Uber, Snapchat and others." Schmidt highlighted Google AlphaGo - which defeated Go champion Lee Sedol last week - as an example of the power of artificial intelligence. See also: Google DeepMind: What is it, how does it work and should you be scared? He also cited Google's Photos image search software as a practical example of how machine learning can be applied to create new products and services when applied to large crowd-sourced datasets.

A company that helps users to create their own websites now knows what kind of sites their 80 million users are building without pestering them with repeated questions. Wix, a Tel Aviv-based web development company, is using machine learning on Google's cloud platform to learn more about its users so it can help them find the images they need to build interesting and useful websites. That's just the beginning of how machine learning will be used in the cloud, according to industry analysts who say machine learning will be the biggest thing that's ever hit the cloud. David Zuckerman, head of developer experience for Wix, said machine learning in the cloud will be a boon to companies that don't have a major research division. "The cloud has brought this technology to everyone," he said.

This series of blog posts aims to provide an intuitive and gentle introduction to deep learning that does not rely heavily on math or theoretical constructs. Everything in life depends on time and therefore, represents a sequence. To perform machine learning with sequential data (text, speech, video, etc.) we could use a regular neural network and feed it the entire sequence, but the input size of our data would be fixed, which is quite limiting. Other problems with this approach occur if important events in a sequence lie just outside of the input window. What we need is (1) a network to which we can feed sequences of arbitrary length one element of the sequence per time step (for example a video is just a sequence of images; we feed the network one image at a time); and (2) a network which has some kind of memory to remember important events which happened many time steps in the past.

Jeff J Mitchell / Getty ImagesThe robots are coming. But it's not just low-paying positions that will get replaced. AI also could cause high-earning (like top 5% of American salaries) jobs to disappear. That's the theme of New York Times reporter Nathaniel Popper's new feature, "The Robots Are Coming for Wall Street." The piece is framed around Daniel Nadler, the founder of Kensho, an analytics company that's transforming finance.

In an unassuming, two-story Victorian town house in Bristol, people are being filmed, monitored, and tracked 24/7. Invisible sensors constantly keep a watchful eye as they go about their business. But what these folks lose in privacy could be our collective gain in life expectancy--that is, if the long-term data bears out. Pivotal to the 15-million ( 21M) Sensor Platform for Healthcare in a Residential Environment (SPHERE) project, this house has been invisibly fitted with dozens of cameras and sensors while its occupants are asked to don wearable devices. The aim is to research how health is related to everyday lifestyle and living conditions over time.

Dr. Quoc Viet Le is a research scientist at Google Brain known for his path-breaking work on deep neural networks (DNN). He is especially famous for his Ph.D work in image processing under Andrew Ng, one of the pioneers of the DNN revolution. Le's and Ng's work demonstrated how computers could be used to learn complicated features and patterns in a way similar to how the mammalian brain learns, with better performance than earlier neural network technology. One of their first breakthroughs was demonstrating the training of a large neural network to detect cats from YouTube videos. This revolutionized the interest in DNNs, and got the current giants of the computer industry such as Google, Facebook and Microsoft in a race to incorporate AI techniques into their software.

It was the unspooling of an unfortunate series of events involving artificial intelligence, human nature, and a very public experiment. Amid this dangerous combination of forces, determining exactly what went wrong is near-impossible. But the bottom line is simple: Microsoft has awful lot of egg on its face after unleashing an online chat bot that Twitter users coaxed into regurgitating some seriously offensive language, including pointedly racist and sexist remarks. On Wednesday morning, the company unveiled Tay, a chat bot meant to mimic the verbal tics of a 19-year-old American girl, provided to the world at large via the messaging platforms Twitter, Kik and GroupMe. According to Microsoft, the aim was to "conduct research on conversational understanding."

Yahoo is today announcing the release of a large-scale data set that describes people's usage of news feeds on several of the company's web services, including Yahoo News and Yahoo Finance. The idea is to empower machine learning researchers in academia with very rich data. The release of data is not, in and of itself, new for Yahoo -- there have been 56 previous releases in the Yahoo Labs Webscope program, which encompasses advertising, image, social, and ratings data, among other categories. This data set in particular covers 20 million people over the course of four months in 2015, and shows the types of devices people used to visit pages, how far down they got in the articles, and the top subjects of articles. There is data on people's locations, their ages (in some cases), and their gender -- all in an anonymized way. What's interesting about today's release is the size of the data set: 13.5TB.

Human beings who design intelligent computers have a long history of getting those computers to beat other humans at games to prove how great their computers are. Think IBM's Deep Blue taking down chess legend Garry Kasparov, or the same company's Watson cleaning house on Jeopardy! But there is one game that artificial intelligence has long struggled to master: Go, a board game with roots in ancient China. Go players pick either black stones or white stones, with each player placing one stone of their color every turn. The idea is to capture and remove an opponent's stones by surrounding them with your own.

In turn, Google will contribute to Movidius' neural network technology roadmap. This agreement enables Google to deploy its advanced neural computation engine on Movidius' ultra-low-power platform, introducing a new way for machine intelligence to run locally on devices. Local computation allows for data to stay on device and properly function without internet connection and with fewer latency issues. This means future products can have the ability to understand images and audio with incredible speed and accuracy, offering a more personal and contextualized computing experience. "What Google has been able to achieve with neural networks is providing us with the building blocks for machine intelligence, laying the groundwork for the next decade of how technology will enhance the way people interact with the world," said Blaise Ag?era y Arcas, head of Google's machine intelligence group in Seattle.