Japan is spending more than 1 billion to resurrect the area around the wrecked Fukushima No. 1 nuclear plant as the country's "Innovation Coast." The region is trying to capitalize on technology developed in the five years spent cleaning up the worst nuclear disaster since Chernobyl, including Hitachi Ltd. and Toshiba Corp. robots that slither like snakes or cruise through radioactive water like speed boats to investigate the flooded reactors. Fukushima Prefecture -- like Beirut or post-bankruptcy Detroit -- is ripe to develop a strong tech community, according to Samhir Vasdev, an innovation consultant at the World Bank. "To lead the future from Fukushima, we must overcome our failures," Fukushima Gov. Masao Uchibori said at the Foreign Press Center in Tokyo last month. "Creating new industries will attract new people, which will be vital to revitalizing the region."

It seems it may only be a matter of time before the best Go player on the planet is a computer. AlphaGo beat the European champion in Go and was driven by machine learning, a technology that has underpinned the recent major advances in artificial intelligence in computer vision, speech recognition and language translation.1 Machine learning is a data driven approach to artificial intelligence. AlphaGo learnt how to play Go by many games played against itself, and by observing a large history of games played by professional players. The end result is that by the time of its first match against the European Champion AlphaGo had already played many more games of Go than any human could possibly play in their lifetime. And since that win AlphaGo has been actively learning to improve itself. Relentlessly playing all day and all night in an effort to ready itself to play the world champion.

Two years ago, a small company in London called DeepMind uploaded their pioneering paper "Playing Atari with Deep Reinforcement Learning" to Arxiv. In this paper they demonstrated how a computer learned to play Atari 2600 video games by observing just the screen pixels and receiving a reward when the game score increased. The result was remarkable, because the games and the goals in every game were very different and designed to be challenging for humans. The same model architecture, without any change, was used to learn seven different games, and in three of them the algorithm performed even better than a human! It has been hailed since then as the first step towards general artificial intelligence – an AI that can survive in a variety of environments, instead of being confined to strict realms such as playing chess. No wonder DeepMind was immediately bought by Google and has been on the forefront of deep learning research ever since.

In 1957, Herbert A. Simon, a pioneer in artificial intelligence and later a Nobel Laureate in economics, predicted that in 10 years a computer would surpass humans in what was then regarded as the premier battleground of wits: the game of chess. Though the project took four times as long as he expected, in 1997 my colleagues and I at IBM fielded a computer called Deep Blue that defeated Garry Kasparov, the highest-rated chess player ever. You might have thought that we had finally put the question to rest--but no. Many people argued that we had tailored our methods to solve just this one, narrowly defined problem, and that it could never handle the manifold tasks that serve as better touchstones for human intelligence. These critics pointed to weiqi, an ancient Chinese board game, better known in the West by the Japanese name of Go, whose combinatorial complexity was many orders of magnitude greater than that of chess. Noting that the best Go programs could not even handle the typical novice, they predicted that none would ever trouble the very best players.

Mergers and acquisitions are a reality in the technology world and one that most startups and small businesses have to confront at some point. One of the key questions your organisation will no doubt ask itself if approached for a buy-out or capital injection is: "Am I going to get the best value for my business in the current climate?" Similarly, large enterprises need to consider whether they're getting the most bang for their buck or punch for their pound when mulling an acquisition. So what does the landscape look like in 2016? In the past, the tech world has experienced'boom-and-bust' economic cycles - think the bursting of the dot-com bubble in the late-90s, or the impact of the pan-European recession from as early as 2007.

That was the result of the match between Google's AlphaGo and human champion Lee Sedol at the fiendishly complex game of Go, and it came with a disconcerting question: what next? Where will the machines claim their next victory: putting you out of a job; solving the mysteries of science; bettering human abilities in the bedroom? AlphaGo's success was down to artificial intelligence (AI): the computer program taught itself how to improve its game by playing millions of matches against itself. But the trouble with using games such as chess and Go as measures of technological progress is that they are competitions. There's a winner and there's a loser – and this month's biggest tech news story had a clear victor.

THIS week humankind was delivered a body blow by an artificial intelligence (AI) called AlphaGo that beat Go's world champion, Lee Sedol, so is it now time for humans to let the machines rule the world? Not just yet--while this adds to a growing list of machines that have beaten the best humans at chess, checkers and backgammon, Lee Sedol won a game back against AlphaGo, so there is still hope for us. The ancient Chinese strategy game Go has substantially more moves to consider each turn than chess. With the two players having to look several moves ahead with more possible outcomes than there are atoms in the universe before deciding what move to make. For each move in a game such as Go, the AI uses a tree search that plays out scenarios, notes which lead to the most victories, and then works back to find out the next move that will lead to the best scenario.

So, I'm another comp sci major, your average guy who has taken algorithms, datastructures, blah blah. I'm taking machine learning and it feels like a bunch of math with no intuition was just thrown at me. First week, professor throws a bunch of entropy equations at us, I have no clue what entropy even means, I don't know what all the equation means, what is "regression" or "information gain". I've never taken information theory before. Before I had time to digest it, we are moving to decision trees and neural networks. Those two feel more intuitive but still a lot of entropy equations to check for errors or something.

Last month I wrote an article describing the interfaces and capabilities of Microsoft and IBM's new cloud data science products. I observed that Azure ML presents a user-friendly drag and drop data mining app for businesses, while Watson Analytics focuses on natural language queries but is still too nascent for use. A similar query for "IBM Watson Analytics" turns up 730,000 documents. Amid the deluge of coverage on both services, one could lose sight of the many upstart companies offering cloud machine learning services. However, new product categories are typically pioneered by startups.

Azure Machine Learning Studio comes with a large number of machine learning algorithms that you can use to build your predictive analytics solutions. These algorithms fall into the general machine learning categories of regression, classification, clustering, and anomaly detection, and each one is designed to address a different type of machine learning problem. The question is, is there something that can help me quickly figure out how to choose a machine learning algorithm for my specific solution? The Microsoft Azure Machine Learning Algorithm Cheat Sheet is designed to help you sift through the available machine learning algorithms and choose the appropriate one to use for your predictive analytics solution. The cheat sheet asks you questions about both the nature of your data and the problem you're working to address, and then suggests an algorithm for you to try.