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.

Wall Street, venture capitalists, technology executives – all have important reasons to understand the growth and opportunity of artificial intelligence, but the inherent vagueness of the term makes any single valuation extremely difficult. Indeed, the term "artificial intelligence" is notorious for having a relatively amorphous definition, itself. In order to put together an executive brief for market size and projected growth of AI, I've molded this article around (a) AI-related industry market research forecasts, and (b) a limited number of reputable research sources for further insight into AI valuation and forecasting, in addition to select and relevant quotes. Bear in mind that different market research firms define "artificial intelligence." To make this summary article more useful, we've quickly broken down all reports by source, definition / meaning of "artificial intelligence", valuation, and timeline.

CAPE CANAVERAL, Fla. – Fresh supplies shipped out late Tuesday for the International Space Station, where the shelves finally are getting full after a string of failed deliveries. Launching beneath the light of a nearly full moon, the unmanned Atlas V rocket provided late-night sparkle as it headed north with its precious cargo and paralleled the East Coast on its way to orbit. Orbital ATK's Cygnus capsule holds nearly 8,000 pounds of food, equipment and scientific research for NASA, including a commercial-quality 3-D printer anyone can rent and experimental robotic grippers modeled after the thousands of sticky hairs on geckos' feet. There's also a fire experiment that will remain on the Cygnus. Researchers will ignite a large-scale blaze, in a contained box, to see how it spreads in weightlessness.

Mohri is most famously known for his work with finite state transducers(FST). So as you can see his very second lecture is on Finite State Automata(FSA). FSTs and FSAs are very powerful formalisms which using the principle of compositionality can be applied to all parts of the speech recognition pipeline - acoustic modelling, context modelling, lexical modelling, and language modelling. If you like getting your hands dirty, Kaldi is a good first place to start:http://kaldi-asr.org/. And the easiest place to start hacking to see what is going on under the hood is the speech decoder.

Data scientists must always remember that data sets are not objective - they are selected, collected, filtered, structured and analyzed by human design. Naked and hidden biases in selecting, collecting, structuring and analyzing data present serious risks. For example, a recent Wall Street Journal article entitled "Tweets Provide New Way to Gauge TV Audiences" provides evidence of a disconnect between mainstream viewers and folks who use Twitter. The chart above shows the disconnect between the most popular and most tweeted shows - the most tweeted show is not a top ten show. While Twitter data can be useful for detecting trends and sentiments for certain areas (e.g., disease surveillance, natural disaster surveillance, product sentiments, financial trading, politics) in limited circumstances using scientific methods, it can also mislead and present a false view of reality.