DeepMind is a British AI startup which was relatively unknown until it was bought by Google for around $600 million in 2014. Since then DeepMind has continued to refine its neural-network driven technology which has broken new frontiers with machine learning, particularly deep learning. Perhaps DeepMind's most famous accomplishment so far is being the brains behind AlphaGo, the first computer program to beat a professional human player of the board game Go. AlphaGo was developed by feeding DeepMind's machine learning algorithms with 30 million moves from historical tournament data, and then having it play against itself and learn from each defeat or victory. DeepMind's work is based on a solid grounding in neuroscience.

If you think this election is insane, wait until 2020. I want you to imagine how, in four years' time, technologies like AI, machine learning, sensors and networks will accelerate. Political campaigns are about to get hyper-personalized thanks to advances in a few exponential technologies. Imagine a candidate who now knows everything about you, who can reach you wherever you happen to be looking, and who can use info scraped from social media (and intuited by machine learning algorithms) to speak directly to you and your interests. Here's what future election campaign marketing might feel like… In 2016, 78% of Americans have a social media profile.

AI feels like this kind of scary, uncertain, futuristic thing, but it's not. It's right here today and many of us are already benefiting from it without even knowing it. Here's a mix of things AI can do for you, right now, today. Seriously, you might already be using them. Repetitious tasks are ripe for AI disruption, and nothing is more repetitious then the predictable back-and-forth of scheduling a meeting.

It's all well and good to ask if androids dream of electric sheep, but science fact has evolved to a point where it's beginning to coincide with science fiction. No, we don't have autonomous androids struggling with existential crises -- yet -- but we are getting ever closer to what people tend to call "artificial intelligence." Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don't have to be explicitly programmed but can change and improve their algorithms by themselves. Today, machine learning algorithms enable computers to communicate with humans, autonomously drive cars, write and publish sport match reports, and find terrorist suspects.

I've been meaning to learn Theano for a while and I've also wanted to build a chess AI at some point. So why not combine the two? That's what I thought, and I ended up spending way too much time on it. Chess is a game with a finite number of states, meaning if you had infinite computing capacity, you could actually solve chess. Every position in chess is either a win for white, a win for black, or a forced draw for both players.

"Artificial Intelligence": this term has become so popular/hyped/*add an adjective of your choice* in this decade, that we're talking about it more than ever. So much so that anything about AI becomes front page news. Tech media must be having a crush on AI for sure. Popular voices in the ecosystem, are so polarizing that you're left scared or excited. Mr. Singularity Ray Kurzweil says: We all must have heard of X-Prize, if not check out.

Twelve days into the strangest poker tournament of their lives, Jason Les and his companions returned to their hotel, browbeaten and exhausted. Huddled over a pile of tacos, they strategized, as they had done every night. With about 60,000 hands played -- and 60,000 to go -- they were losing badly to an unusual opponent: a computer program called Libratus, which was up nearly $800,000 in chips. That wasn't supposed to happen. In 2015, Les and a crew of poker pros had beaten a similar computer program, winning about $700,000.

Just a decade ago, few would have guessed that virtual goods could create a real market. Then the smartphone age sparked a whole new universe of ephemeral, yet lucrative, commerce. "People have gotten much more comfortable with the idea of paying for things that are virtual," says Joost van Dreunen, the co-founder and CEO of SuperData, a gaming research firm. For startups in this fast-growing market, the goods may be fake, but the sales are real. Some of the most promising new areas of business are hidden behind what can sound like Millennial smartphone-speak: Kimoji!

Two of modern science's most powerful voices, Elon Musk and Stephen Hawking, have both issued warnings about the dangers of artificial intelligence in the past (Musk has even been tinkering with ways humanity can augment themselves to keep up). But good news: Musk and Hawking are jumping onboard the ethical AI bandwagon. In an open letter published by Future of Life Institute (FLI) last Monday, Musk and Hawking joined several AI and robotics researchers in a comprehensive outline called "Asilomar AI principles" - 23 guidelines for avoiding an artificial intelligence armageddon. The goal is to guide AI research toward beneficial intelligence rather than "undirected intelligence." The principles are the product of the FLI's 2017 Beneficial AI conference.

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand.