God moves the player, he in turn the piece. But what god beyond God begins the round Of dust and time and sleep and agony? As I write this column, a computer program called AlphaGo is beating the professional go player Lee Sedol at a highly publicized tournament in Seoul. Sedol is among the top three players in the world, having attained the highest rank of nine dan. The victory over one of humanity's best representatives of this very old and traditional board game is a crushing 4 to 1, with one more game to come.

This week, Google's AlphaGo beat a grandmaster at the complex game Go – an artificial intelligence milestone (see "How victory for Google's Go AI is stoking fear in South Korea", "Machines are teaching themselves to grapple with the real world" and "Humans strike back: How Lee Sedol won a game against AlphaGo"). Here's what the experts say AI's next big challenge should be. No-limit poker: Go represents the ultimate in games where all the information is available to the players. But AI still struggles with games where information is incomplete – like poker, where a player doesn't know what card is coming next. "Computers have beaten the best people at heads-up limit Texas Hold'em, but not yet at no-limit, a much more complicated game," says Peter Stone at the University of Texas at Austin.

Google is teaching its artificial intelligence how to understand language by making it predict, and replicate, the works of famous dead authors. The company is building systems that are capable of understanding natural language in the same way humans do, with the works of William Shakespeare, Mark Twain and others currently being analysed. "This work has the potential to enrich products through personalisation," Marc Pickett from Google's Natural Language Understanding research group wrote in a recent blog post. Researchers training the deep neural network -- using the work of authors from Project Gutenberg -- fed the AI an input sentence and asked it to say what would come next. The network is given millions of lines from a "jumble" of authors and then works out the style of individual writers.

Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed. We can make machines learn to do things! The first time I heard that, it blew my mind. That means that we can program computers to learn things by themselves! The ability of learning is one of the most important aspects of intelligence. Translating that power to machines, sounds like a huge step towards making them more intelligent. And in fact, Machine Learning is the area that is making most of the progress in Artificial Intelligence today; being a trendy topic right now and pushing the possibility to have more intelligent machines.

For example, Dean's affinity for cats comes in handy with his line of work. In this context, cats are a mere vehicle for determining how much a computerMachine learning is next big thing in programming. Read more ... » can see, learn, communicate and understand. It also turns out that machinesAI research nerve centre launched in Cambridge. Read more ... » and humans are complementary in skills.

Empathy is a tricky business. The range and complexity of human emotion makes it difficult, if not impossible, to ever really understand how someone else is feeling. Nevertheless, empathy is considered to be a crucial aspect of what makes us human--indeed, our brains appear to be hardwired for it. So perhaps it won't come as much of a surprise that as machine learning becomes ever more sophisticated and capable of mimicking some of the most complex functions of the human brain, figuring out a way to teach a computer empathy is quickly becoming a business in itself. Known as artificial empathy, the idea here is to train machines to recognize social signals from humans, aka'visual data,' and then produce an appropriate response.

The past few weeks we've talked a lot about the brand new algorithm that we have designed for Wide Ideas. The story behind Score, which is the name of the new functionality, is a bit interesting. Two years ago I asked myself if it in any way would be possible to use Machine Learning techniques to predict the outcome of football matches. Data mining To describe the process briefly I started by collecting as much data as I could get hold of. I mined data about old games from every different source and API I could find.

It's been a week of extremes for Google's artificial intelligence efforts, as the company luxuriates in the afterglow of winning a board game tournament against one of the world's top players, while it privately tries to sell one of its most visible robotics efforts. Google's decision to try to shed its Boston Dynamics robotics group highlights a fundamental research problem: software is far easier to develop and test than hardware. Today's industrial robots tend to be dumb machines, operating on pre-programmed routines, and are housed in metal cages to stop people walking into their zone of movement and potentially getting harmed. With Boston Dynamics, Google was working on machines that could break out of the rigid confines of the factory and perform a broader range of tasks. That requires dealing with a range of unsolved problems, requiring fundamental research.

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.

Artificial intelligence has never been as pervasive as it is today. From Google's self-driving cars from to Hilton's new Watson-powered hotel concierge, we are witnessing an explosion of AI capabilities. But while it may appear that machines are taking over, they are still tied to their human masters for one very important task: training. "We're in the middle of the'Big Bang' moment of AI," NVIDIA's Senior Product Manager Will Ramey says in the AISummit's new ebook on the topic. "We now have the deep neural networks, the explosion of big data, and now thanks to the leap in processing power with enhanced GPUs, we have the full package to see a real shift in the development of commercial real-world AI applications."