A huge milestone has just been reached in the field of artificial intelligence: AlphaGo, a program developed by Google's DeepMind unit, has defeated legendary Go player Lee Se-dol in the first of five historic matches being held in Seoul, South Korea. Lee resigned after about three and a half hours, with 28 minutes and 28 seconds remaining on his clock. The series is the first time a professional 9-dan Go player has taken on a computer, and Lee is competing for a 1 million prize. "I was very surprised," said Lee after the match. "I didn't expect to lose. DeepMind founder Demis Hassabis expressed "huge respect for Lee Se-dol and his amazing skills," calling the game "hugely exciting" and "very tense." Team lead David Silver said it was an "amazing game of Go that really pushed AlphaGo to its limits." Go is an ancient Chinese board game that has long been considered one of the great challenges faced by AI. While computer programs now best the world's leading human players of games like checkers and chess, the high level of intuition and evaluation required by Go has made it tough for computers to crack. DeepMind's AlphaGo program is the most advanced effort yet, using a complex system of deep neural networks and machine learning; it beat European champion Fan Hui last year, but Lee Se-dol is another proposition entirely. "I don't regret accepting this challenge," said Lee. "I am in shock, I admit that, but what's done is done.

Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and getting presidents elected. But there is a push to use machine learning to do even more--to cure cancer and AIDS and possibly solve every problem humanity has. Pedro Domingos is at the very forefront of the search for the Master Algorithm, a universal learner capable of deriving all knowledge--past, present and future--from data. In this book, he lifts the veil on the usually secretive machine learning industry and details the quest for the Master Algorithm, along with the revolutionary implications such a discovery will have on our society. Pedro Domingos is a Professor of Computer Science and Engineering at the University of Washington, and he is the co-founder of the International Machine Learning Society.

Unexpected convergent consequences…this is what happens when eight different exponential technologies all explode onto the scene at once. This post (the second of seven) is a look at artificial intelligence. Future posts will look at other tech areas. An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., the Internet of Things), but try to predict the future when A.I., robotics, VR, synthetic biology and computation are all doubling, morphing and recombining. You have a very exciting (read: unpredictable) future. This year at my Abundance 360 Summit I decided to explore this concept in sessions I called "Convergence Catalyzers." For each technology, I brought in an industry expert to identify their Top 5 Recent Breakthroughs (2012-2015) and their Top 5 Anticipated Breakthroughs (2016-2018). Then, we explored the patterns that emerged.

Ever since I read "Singularity is Near" I've been fascinated by Ray Kurzweil – his wirings, ideas, a predictions. He's not been afraid to go on the limb and make some brave and seemingly outlandish forecasts about the upcoming technological advances and their oversize impact on people and society. One of the main reasons why I always found his predictions credible is that they can, in a nutshell, be reduced to just a couple of seemingly simple observations: 1. Information-technological advances are happening exponentially, and 2. Information technology in particular is driving all the other technological and societal changes. The rest, to put it rather crudely, are the details. In "How to Create a Mind" Kurzweil zeroes in on just one scientific/technological project – creating a functioning replica of the human mind. He uses certain insights from information technology and neurology to propose his own idea of what human mind (and by extension human intelligence) are all about, and to propose how to go about emulating it "in silico."

Welcome to robot nursery school," Pieter Abbeel says as he opens the door to the Robot Learning Lab on the seventh floor of a sleek new building on the northern edge of the UC-Berkeley campus. The lab is chaotic: bikes leaning against the wall, a dozen or so grad students in disorganized cubicles, whiteboards covered with indecipherable equations. Abbeel, 38, is a thin, wiry guy, dressed in jeans and a stretched-out T-shirt. He moved to the U.S. from Belgium in 2000 to get a Ph.D. in computer science at Stanford and is now one of the world's foremost experts in understanding the challenge of teaching robots to think intelligently. But first, he has to teach them to "think" at all. "That's why we call this nursery school," he jokes. He introduces me to Brett, a six-foot-tall humanoid robot made by Willow Garage, a high-profile Silicon Valley robotics manufacturer that is now out of business. The lab acquired the robot several years ago to experiment with. Brett, which stands for ...

Note: This is Part 2 of a two-part series on AI. We have what may be an extremely difficult problem with an unknown time to solve it, on which quite possibly the entire future of humanity depends. Welcome to Part 2 of the "Wait how is this possibly what I'm reading I don't get why everyone isn't talking about this" series. Part 1 started innocently enough, as we discussed Artificial Narrow Intelligence, or ANI (AI that specializes in one narrow task like coming up with driving routes or playing chess), and how it's all around us in the world today. We then examined why it was such a huge challenge to get from ANI to Artificial General Intelligence, or AGI (AI that's at least as intellectually capable as a human, across the board), and we discussed why the exponential rate of technological advancement we've seen in the past suggests that AGI might not be as far away as it seems. This left us staring at the screen, confronting the intense concept of potentially-in-our-lifetime ...

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words. Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it's vital we understand this new language, and what it's increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it. The language is a new class of machine learning known as deep learning, and the "whispered word" was a computer's use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat.

"It was a bright cold day in April, and the clocks were striking thirteen." The opening line of George Orwell's great novel 1984 sets up a cautionary tale of how the future could look. In its way, what we're confronting in 2020 is every bit as chilling, but it is also an exciting time of opportunity. Yet, just as the clocks "were striking thirteen", there could be alarming changes at work in the name of progress. "Within the next five years, 20 per cent of all the jobs that exist today will have been automated away," claims futurologist Rohit Talwar.

As retailers and brands predict and plan for the way consumers will shop in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond. Promising significant benefits for both retailers and consumers, AI is already around us and used everyday within shopping and payments. Businesses are embracing the benefits of the technology and progress within AI is accelerating at pace, with big things expected for the near, and distant, future. AI can process'big data' far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour – for example to detect fraud – as well as appropriate advertisements for upselling. Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the in-store interaction for consumers.

Now in its 30th year South by Southwest is synonymous with creativity and innovation for advertisers and marketers from around the world and this year has seen the program significantly expand with hundreds of new sessions catering to a record breaking 72,000 delegates across its interactive, music and film festivals. So beyond dizzying crowds, and the ever-present fear of missing out on the next big thing, what are some of the key trends to have emerged from the annual techie talkfest and what are the things which were hot in previous years but have now have lost their cool after going mainstream? Walking around Austin, VR is everywhere. Demand by festival goers to get their hands on the latest 360 experience has left people queuing day and night for their chance to play. Samsung, realising the round the clock queues their 5D sensory experience would attract, took things a step further with a tweet containing #VRonDemand summoning one of Austin's iconic hipster peddled pedicabs to your location wherever you were, taking the experience out of the showroom and into the streets. However, there was no sign of Microsoft's augmented reality visor the Hololens this year.