It's an event that plays out thousands of times across the UK every day. A consumer tries to pay for their weekly grocery shop using a credit card but with the bags packed at the till is unexpectedly told that it has been'declined'. The card is well within its credit limit, the PIN number is correct, the consumer has made numerous other purchases in the preceding weeks and yet there is no way around the reality of having no plastic money to spend. For the financial services industry these'false positives' have become a growing issue. As well as annoying customers and merchants they cost the industry in terms of the manual intervention necessary to authenticate customers and unblock cards.

My machine learning model accurately predicted Villanova would win the championship, netted me first place out of 34 in my office pool, 63rd place out of 608 in the Kaggle competition (top 11%) and 123,000 out of 13 million in ESPN's overall leaderboard (top 1%). I wrote a post about this before the tournament with the promise that if it worked out, I'd do a full writeup and release the code. I'm thankful to say it all panned out and here we go! There is a boatload of luck that goes along with this. Anyone who watched even the last 5 seconds of the championship game knows this.

We provide a dependency parser for English tweets, TweeboParser . The parser is trained on a subset of a new labeled corpus for 929 tweets (12,318 tokens) drawn from the POS-tagged tweet corpus of Owoputi et al. (2013), Tweebank . These were created by Lingpeng Kong, Nathan Schneider, Swabha Swayamdipta, Archna Bhatia, Chris Dyer, and Noah A. Smith. Given a tweet, TweeboParser predicts its syntactic structure, represented by unlabeled dependencies. Since a tweet often contains more than one utterance, the output of TweeboParser will often be a multi-rooted graph over the tweet.

It is built upon one widely used machine learning package scikit-learn and two scientific computing packages Numpy and Scipy. It serves as a platform for facilitating feature selection application, research and comparative study. It is designed to share widely used feature selection algorithms developed in the feature selection research, and offer convenience for researchers and practitioners to perform empirical evaluation in developing new feature selection algorithms. Instructions of using this repository can be found in our project webpage at http://featureselection.asu.edu/

A year ago, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive. Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways.

A recent article titled "Why is AI Female?" made the connection that gendered labor, in service professions in particular, is fueling our expectations for gendered AI assistants and service robots. Furthermore, the author argues, this "feminizing -- and sexualizing -- of machines" signals a future with a disproportionate use of feminized VR and robots for a male-dominated sex industry. "Sex with robots is a big leap from asking Siri to set an alarm, but the fact that we've largely equated artificial intelligence with female personalities is worth examining. There are, after all, few sexualized male robots or avatars." Herbert Televox and Mr. Telelux, the early 20th century robots made by Westinghouse, were both male.

Neurensic, an artificial intelligence (AI) technology startup focused on software-as-a-service solutions for the financial services industry, has announced the formation of a new Innovation Lab, which will focus on applying its big data aggregation and AI capabilities to nascent technologies such as blockchain. "Neurensic's ultimate goal is to bring together all business processes currently completed post-trade into a single vendor platform that is able to function real-time and at-trade, opening new horizons for self-regulating markets and ultimately redefining all financial transactions. I have faith that our new Innovation Lab will accelerate this process", said said David Widerhorn, Chief Executive Officer. This lab will be led by Neurensic co-founder Zachary Watts, who is being named as Chief Innovation Officer. "Bringing a business intelligence layer to distributed ledger solutions is critical to forming a sustainable path toward adopting these technologies in the long-run," said Watts.

Google's AlphaGo program beat 18 time World Champion Lee Sedol in the complicated game of Go. By achieving this milestone, which many thought would not happen within the next decade, artificial intelligence proved it will have serious implications for medicine and healthcare. In 2011, IBM's new supercomputer named Watson beat two genius players in the television show Jeopardy! It was a battle of lexical knowledge. So why is everyone so excited that Google Deepmind's AlphaGo recently beat Lee Sedol, today's best Go player 4-1?

The future of machine learning seems to be very bright, with leaping advances in software & technology and the proliferation of the cloud. It is currently one of the fastest emerging technologies in the world, with many experts claiming that it holds the key to unlocking the doors to computing's most mystical evolution- artificial intelligence. But sci-fi concepts apart, machine learning is a powerful tool that is already being used to solve complex classification problems. But as machine learning systems continue to evolve, they will be an increased demand for smarter languages that will be able to process a number of complex issues and general paradigms, some of which might be too complicated for humans to process. With the industry's growing experience with smart machine learning systems, entire field of machine learning is being staged to shift from simple problem solving to the creation of powerful and complex algorithms that work based on advanced-level.

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