A Google artificial intelligence program has beaten the European champion of the board game Go. The Chinese game is viewed as a much tougher challenge than chess for computers because there are many more ways a Go match can play out. The tech company's DeepMind division said its software had beaten its human rival five games to nil. One independent expert called it a breakthrough for AI with potentially far-reaching consequences. The achievement was announced to coincide with the publication of a paper, in the scientific journal Nature, detailing the techniques used.

These days, Google is making headlines as its artificial intelligence (AI) AlphaGo beated top pro Go player Lee Se-dol 2:0 in a highly publicized five-game Go series. The internet search giant is expanding its AI business by taking over four robotics companies including DeepMind which designed AlphaGo. But in Korea, AI is an underdeveloped and poorly invested sector. "Korean companies have not made much progress in AI research. They still have a long way to go in terms of AI commercialization," said Jin Jeong-yeol, director of the Kohyoung Technology.

I absolutely love where I'm at and grateful to be the steward of this brand and interesting niche space. Well, I don't reflect on achievements too often, quite honestly. Rather, I'm always striving to grow as a person and professional. But in the past year, I've experienced more growth in my skill-sets than ever before. I'm a self-taught web developer and have been growing my expertise in product development, UX and other facets of design.

Henri Waelbroeck seems to fit the popular image of the scientist transplanted into the world of high finance and hedge fund trading, the sort of stereotype found in books like "The Fear Index" by Robert Harris. Waelbroeck, director of research at machine learning-enhanced trade execution system Portware, was previously a professor at the Institute of Nuclear Sciences at the National University of Mexico (UNAM). His areas of expertise include: complex systems science, quantum gravity theories, genetic algorithms, artificial neural networks, chaos theory. The impression Waelbroeck conveys is one of precision. He explains that algorithms have grown in complexity since being introduced to the world of trading around 2000. This has made it increasingly difficult for traders to understand each vendor's full algorithm platform and how to optimally select an algorithm for each particular trade that comes in from a portfolio manager.

Whenever I read articles about data science I feel like there is some important aspect missing: evaluating the performance and quality of a machine learning model. Consequently, the first post on this blog will deal with a pretty useful evaluation technique: lift analysis. When evaluating machine learning models there is a plethora of possible metrics to assess performance.

Whenever I read articles about data science I feel like there is some important aspect missing: evaluating the performance and quality of a machine learning model. There is always a neat problem at hand that gets solved and the process of data acquisition, handling and model creation is discussed, but the evaluation aspect too often is very brief. But I truly believe it's the most important fact, when building a new model. Consequently, the first post on this blog will deal with a pretty useful evaluation technique: lift analysis. Machine learning covers a wide variety of problems like regression and clustering.

If you were tweeting and drinking between July 2013 to 2014, your tweets might have been used as part of an experiment by computer science students at the University of Rochester. Nabil Hossain and colleagues trained a computer to identify alcohol-related tweets and used the data to monitor alcohol-related activity in a particular area. The research could help with understanding and responding to public health issues, according to the authors of the study. The researchers collected more than 11,000 geotagged tweets from New York City and Monroe County, where Rochester is located, in the northern part of the state. They filtered all of the tweets that mentioned alcohol-related words such as beer, drunk, hangover, wasted or party (as well as variations such as "druuuuuunk").

Fast, novel, automated: threats are routinely getting past traditional security tools. Security now, more than ever, needs to be top of the CEO's agenda. We are seeing a host of new, innovative threats attacking companies on a daily basis. A recent example, detected by Darktrace's'immune system' approach, highlights how machine learning can help in this new era of advanced threat. Within a week of installing threat detection software into one customer's security stack, Darktrace discovered a serious compromise.

Variable selection is an important aspect of model building which every analyst must learn. After all, it helps in building predictive models free from correlated variables, biases and unwanted noise. A lot of novice analysts assume that keeping all (or more) variables will result in the best model as you are not losing any information. Sadly, that is not true! How many times has it happened that removing a variable from model has increased your model accuracy?

AlphaGo handily beat 18-time world Go champion Lee Sedol 4-1, and in doing so taught us several interesting lessons about where AI research is today, and where it is headed. One fascinating thing about AlphaGo is the unusual way it was designed. The software combined deep learning--the hottest AI technique out there today--with a much older, and far less fashionable, approach. Deep learning involves using very large simulated neural networks, and usually it eschews logic or symbol manipulation of the kind pioneered by the likes of Marvin Minksy and John McCarthy. But AlphaGo combines deep learning with something called tree-search, a technique invented by one of Minksy's contemporaries and colleagues, Claude Shannon.