When Elon Musk isn't outlining plans to use his massive rocket to leave a decaying Planet Earth and colonize Mars, he sometimes talks about his belief that Earth isn't even real and we probably live in a computer simulation. "There's a billion to one chance we're living in base reality," he said at a conference in June. Musk is just one of the people in Silicon Valley to take a keen interest in the "simulation hypothesis", which argues that what we experience as reality is actually a giant computer simulation created by a more sophisticated intelligence. If it sounds a lot like The Matrix, that's because it is. According to this week's New Yorker profile of Y Combinator venture capitalist Sam Altman, there are two tech billionaires secretly engaging scientists to work on breaking us out of the simulation.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

Make it easy for your audience to quickly determine what they're about to digest. Use an abstract or introduction to recall your objectives and clearly state them for your readers. What is the problem that you've set out to solve? If you have a desired outcome or any expectations of your audience, say it, as this is the entire reason you're presenting them with your analysis. You then cover everything from your preamble in this section: the question you've been on a mission to answer, your hypothesis, and the methodology you've used.

DeepMind will not build an unstoppable AI on its own. Instead, both companies will release a series of programming tools on early 2017 that will allow researchers and hobbyist around the world build and train their bots to play Starcraft II. Google's DeepMind researcher Orion Vinyals made the announcement during BlizzCon 2016 at Anaheim, California. Vinyals was the top-ranked SC2 player in Spain's leaderboards before becoming a top scientist in the British AI startup. Vinyals believes the results of the investigation could translate to the real life.

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Some of the earliest applications of artificial intelligence in healthcare were in diagnosis--it was a major push in expert systems, for example, where you aim to build up a knowledge base that lets software be as good as a human clinician. Expert systems hit their peak in the late 1980s, but required a lot of knowledge to be encoded by people who had lots of other things to do. Hardware was also a problem for AI in the 1980s. The promise of AI in diagnostics is that you can help people in locations where there aren't enough doctors. Computers are not as creative as human pattern matchers, but that fact also means they can be more consistent than people.

Teaching computers to play the board game Go is impressive, but if we really want to push the limits of machine intelligence, perhaps they'll need to learn to rush a Zerg or set a trap for a horde of invading Protoss ships. StarCraft, a hugely popular space-fiction-themed strategy computer game, will soon be accessible to advanced AI players. Blizzard Entertainment, the company behind the game, and Google DeepMind, a subsidiary of Alphabet focused on developing general-purpose artificial intelligence, announced the move at a games conference today. Teaching computers to play StarCraft II expertly would be a significant milestone in artificial-intelligence research. Within the game, players must build bases, mine resources, and attack their opponents' outposts.

Vyacheslav is a PhD candidate at the Oxford Internet Institute. His research uses social psychology and machine learning to understand networks of people and networks of ideas. Imagine a typical day in 2020: Your personal AI assistant wakes you up with a friendly greeting before preparing your favorite breakfast. During your morning workout, it plays new songs that perfectly match your musical tastes. For your driverless commute to work, it has pre-selected a few articles based on the duration of your commute and what you've read in the past.

This Website contains a short introduction to Noisy Data together with the more relevant bibliography and it also contains the complementary material to the SCI2S research group papers on Noisy Data in Data Mining.

Over the next 15 years, AI technologies will continue to make inroads in nearly every area of our lives, from education to entertainment, health care to security. "Now is the time to consider the design, ethical, and policy challenges that AI technologies raise," said Grosz. The report investigates eight areas of human activity in which AI technologies are already affecting urban life and will be even more pervasive by 2030: transportation, home/service robots, health care, education, entertainment, low-resource communities, public safety and security, employment, and the workplace. Some of the biggest challenges in the next 15 years will be creating safe and reliable hardware for autonomous cars and health care robots; gaining public trust for AI systems, especially in low-resource communities; and overcoming fears that the technology will marginalize humans in the workplace.