The Apple logo is displayed on a screen at Apple's annual Worldwide Developers Conference presentation at the Bill Graham Civic Auditorium in San Francisco on June 13, 2016. SAN FRANCISCO -- One of Silicon Valley's tech behemoths is taking a step further into digital medicine. Apple quietly acquired the personal health data start-up Gliimpse, according to a report from Fast Company. Gliimpse takes a patient's medical records and uses coding to produce a personalized, shareable electronic health record. Anil Sethi, a former systems engineer at Apple with a master's of science from Johns Hopkins, founded the start-up in 2013, according to his LinkedIn profile.

We can identify some of the 5 to 10 year future possibilities in this intersection of robotics and AI now, (three possible scenarios are described below) but perhaps even more valuable is identifying some people who are likely sources of future knowledge about possible futures.

Whether you call it soccer or football, this sport is the world's favorite to watch and play. Thanks to Hugo Mathien who compiled, cleaned, and shared a dataset of stats on European professional football on Kaggle, it can become a data scientist's favorite playground, too. Among other data points, the database includes 25,000 matches from 2008 to 2011, 10,000 players from 11 countries, and betting odds from up to 10 providers. This impressive collection of data allows Kagglers test their machine learning techniques by building models predicting match outcomes (can you beat the bookies?) and find insights through data visualization and storytelling. In this interview, Hugo explains how he pulled data from a number of sources using Python's Scrapy and overcame data integrity issues with manual effort to build this incredible dataset for Kagglers to enjoy.

By John Markoff SUNNYVALE: For more than a decade, Silicon Valley's technology investors and entrepreneurs obsessed over social media and mobile apps that helped people do things like find new friends, fetch a ride home or crowdsource a review of a product or a movie. Now Silicon Valley has found its next shiny new thing. And it does not have a "Like" button. The new era in Silicon Valley centers on artificial intelligence (AI)and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

The deal, announced Tuesday, will bring the Genee team into the Microsoft fold and put them to work on bringing intelligence into Office 365. Acquiring Genee is part of Microsoft's ongoing crusade to build intelligent productivity software and services using AI. It's unclear whether the functionality from the assistant will be making its way directly into Office 365, or if the team behind Genee will just be put to work improving a variety of Microsoft's products. This all plays into Satya Nadella's ongoing strategy of aggressively acquiring companies to shore up Microsoft's capabilities and growth areas.

The ancient Chinese game of Go has simple rules, yet is extremely sophisticated. With a large board and few restrictions, the game is said to be a googol (10 to the hundredth power) times more complex than chess. There are more possible positions in Go than there are atoms in the universe. The game, in which opponents take turns placing black or white stones on a board, is played largely through intuition. Players understand that moves made early in the game can shape the match dozens of plays later. Go's subtleties, patterns, and elegance have captivated players, scholars, and mathematicians for millennia.

BT and Ticketmaster are more than just global leaders in their industries – they are example enterprise organisations already implementing artificial intelligence technology like Natural Language Process, Digital Assistants and Text Analytics into their strategies to improve customer support, experience and business growth. With the news of Google's DeepMind machine learning system and IBM's breakthrough artificial (AI) imitation of brain neurons, it's easy to assume that AI is still designated to hi-tech facilities with scientists in white lab coats and robots rolling past – and business leaders are not exempt from this belief. Although common rebuffs about artificial intelligence claim'there aren't many use cases in the space', AI within enterprise businesses is already available and being implemented – perhaps just not on the same scale or format of what we see in media today (ie the all-robot staff at the Henn-na Hotel in Japan). "We've identified 191 discrete use cases where artificial intelligence is being used today, or will be used in the near future," notes Clint Wheelock, CEO, managing director of Tractica, a market intelligence firm focused on human interaction with technology. "These use cases span 27 different industries and range from well-known applications like algorithmic trading or static image recognition to more specialized emerging areas such as emotion recognition or processing of healthcare patient data."

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Silicon Valley, or the Greater Bay Area, is the 18th largest economy in the world, more than half the size of Canada's economy and bigger than Switzerland, Saudi Arabia or Turkey. This is because the region has become the world leader in research and development of emerging technologies such as artificial intelligence, robotics, software and virtual reality. "Software is eating the world," said Silicon Valley investor Marc Andreessen famously in 2011. It was controversial but prescient. Five years later, software-driven machines and drones perform surgery, write news stories, compose music, translate, analyze, wage war, guard, listen, speak and entertain.

A consequence of this decision is that Keras has its own graph datastructure for handling computational graphs, rather than relying on the native graph datastructure from TensorFlow or Theano. As a result, Keras can do offline shape inference in Theano (a much needed yet missing feature in Theano), and can do easy model sharing or model copying. For instance, when you call a Keras model on a new input ( y model(x)), Keras is reapplying all operations contained in the graph underlying your model, which is made possible by the fact that Keras manages that graph independently of TensorFlow/Theano. In fact, it's even possible to: 1) define a Keras model with the Theano backend, 2) switch to the TensorFlow backend, and 3) re-apply your (Theano-built) Keras model on a TensorFlow input, this creating a TF version of what was initially a Theano model (note that in practice we don't allow to switch backends in the middle of a session, since that would be quite unsafe --the user may mix up TF and Theano tensors-- but it's possible to do it manually if you are familiar with Keras internals).