Part of being the training data arm of today's (and tomorrow's) biggest AI and machine learning initiatives means every day we're talking to people building the coolest dang stuff. Hearing about what our customers are up to and what's on their roadmaps is so interesting--these are the technologies of the future! Many of these peeps are just like our customers were before signing on: building mind-blowing things, but facing significant challenges, especially around acquiring and annotating training data. And okay, of course we're interested in their projects and problems--we aim to boost and solve them (respectively). We all like to have a peek into the experiences of our peers, to know what sort of developments they're cookin' up, where they're finding success, and what roadblocks they're hitting.

Cancer, the single most feared diagnosis imaginable, is being tackled by some of the biggest companies in the world using the most formidable weapon: à la artificial intelligence. In fact, the milestones we have hit in past two years in diagnosing rare forms of cancer using clinical data, has stumped the scientific community. At the University of Tokyo's Institute of Medical Science, IBM's Watson matched a patients' symptoms against 20 million clinical oncology studied. In the western world, Google and Amazon are helping scientists analyze genetic data. Both, Google Genomics and Amazon Web Services, are offering analytical functions of the cloud to help scientists make sense of genomics data.

Spare5 makes gathering accurate, labeled training data easy and scalable. Our tooling for computer vision and natural language is fully customizable and we do all the heavy lifting for you. You can easily integrate the process into your workflow by hooking into our API.

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.

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.

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.

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.

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 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.