Ajit Jaokar is a leading expert working at the intersection of Data Science, IoT, AI, Machine Learning, Big Data, Mobile, and Smart Cities. He teaches IoT and Data Science at Oxford and also is a director of Smart Cities Lab in Madrid. Ajit's work involves applying machine learning techniques to complex problems in the IoT and Telecoms domains. You can follow him on twitter @AjitJaokar and his blogs at Future Text. We are beyond thrilled to announce that Ajit will not only be speaking at our Big Data, Berlin meetup February 17, but he will also be at the head of the second workshop of our'Dataconomy Presents' series.

SAN JOSE, CA--(Marketwired - Feb 1, 2017) - Skytree, the leader in Enterprise Machine Learning on big data, announces the release of Skytree 16.0 and Skytree's Machine Learning as a Service offering. We continue our trend of increasing ease of use via unprecedented automation, further enabling non-data scientist users to access the power of enterprise grade machine learning, gain insights, and to add value to their business. Skytree Smart Search continues to deliver industry-leading automation for finding the most accurate models. This is accomplished in much fewer iterations than brute-force grid searches, by performing a mathematically optimal exploration of the hyper-parameter space. Skytree Smart Search delivers highly tuned models with comprehensive cross-validation in every step, and delivers reliable execution with automatic resource estimation for the powerful Skytree distributed algorithm implementations that are highly optimized for scale, performance and accuracy.

McKinsey's 2016 data and analytics study found 120 potential machine learning applications in 12 industries -- and the evolution is accelerating. Join our latest VB Live interactive event for a deep dive into how machine learning can transform your business. The more data your customer gives you, the more you can infer their needs. AI and machine learning have advanced to the point where you don't just know what they want -- you know it before they do. "That's the side people would say is kind of creepy," says Quinn Banks, senior product marketing manager at Farmers Insurance.

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.