IAB (Interactive Advertising Bureau) and its Data Center of Excellence today announced the winners of the inaugural IAB Data Rockstar Awards, celebrating top industry leaders and practitioners who have demonstrated achievement in data science or technology. The top finalists were selected by the IAB Data Center of Excellence Board of Directors and were evaluated based on demonstrated excellence, creativity or forward-thinking approaches to solving problems in data science, as well as the impact their contributions have made to their company or industry. Chalasani developed a highly efficient, distributed, extreme-scale, single-pass online logistic regression learning system in Scala/Spark, using variants of Stochastic Gradient Descent, capable of handling hundreds of millions of sparse features and billions of training observations. His system incorporates a number of state-of-the-art techniques that do not exist together in any other machine learning system, including adaptive feature-scaling, adaptive gradients, feature-interactions and feature-hashing. Chalasani work is central to MediaMath's vision for every addressable interaction between a marketer and a consumer to be driven by Machine Learning optimization against all available, relevant data at that moment, to maximize long-term marketer business outcomes.

Crunching vast tracts of data is a growing task for defense, intelligence and security agencies. They need analysis, fast, of what's going on in the air and on the ground to assess battlefields, secure environments, and decide when and how to deploy people or humanitarian aid. Artificial intelligence may be the key to digesting the barrage of data from multiple sources. To unlock insights from this data, agencies are increasingly turning to GPU-powered deep learning, with algorithms that can identify relevant content and patterns in raw data at machine speed. The GPU is the engine of modern AI, NVIDIA solution architect Jon Barker Barker told a broad audience from the defense, intelligence and homeland security communities at the recent annual GEOINT Symposium.

Do you get what overfitting means in machine learning? If you don't, then you better learn about it if you want to use or leverage machine learning. Because overfitting can ruin the effectiveness of machine learning. I wrote this blog because I found existing explanations of overfitting to be too technical. I hope this one is more consumable by non specialists. Machine learning involves a fairly complex workflow, see Machine Learning Algorithm!

London-based BenevolentAI believes it has built the kind of artificial intelligence tech that will allow it to identify and develop drugs faster and better than any group of mere scientific mortals can hope for. And now J&J is handing over some experimental molecules it needs to prove it's right. The upstart joins a long line scrambling to apply vast amounts of computational power towards drug development. Their goal is to usher in the long-awaited "pharma 2.0" and finally bend the expensive curve of late-stage trial failure. It's unclear how BenevolentAI's algorithms are any better at evaluating the potential of any small-molecule than other computationally-taxing approaches developed by other groups -- and it's all driven by the data.

It's now a hired gun for thousands of companies in at least 20 industries. David Kenny took the helm of IBM's Watson Group ibm in February, after Big Blue acquired The Weather Company, where Kenny had served as CEO. In the months since then, the Watson business has grown dramatically, with well over 100,000 developers worldwide now working with more than three dozen Watson application program interfaces (APIs). Fortune Deputy Editor Clifton Leaf caught up with Kenny in mid-October, when IBM Watson's General Manager was in San Francisco, getting ready to open Watson West--the AI system's newest business outpost--and to launch the company's second World of Watson conference, a gathering of its burgeoning ecosystem of partners and users, in Las Vegas on Oct. 24. KENNY: Deep learning is a subset of machine learning, which essentially is a set of algorithms. Deep-learning uses more advanced things like convolutional neural networks, which basically means you can look at things more deeply into more layers. Machine learning could work, for example, when it came to reading text.

Many have written about how deep learning is taking over the world and why that is important; I cannot echo them enough. Playing with deep learning is the closest I've ever felt to being a magician, and it's become clear to me that every (great) piece of software will be powered by deep learning within the next 3 years. However, deep learning isn't mainstream yet, so I thought I'd share work by some very talented contributors, in the hopes to bring it just that little bit closer. Quick note: I've started a weekly email newsletter covering all things deep learning and self-driving cars. I call it Transmission, sign up today!

Few industries are as competitive as ecommerce. Not only are online retailers competing with other online stores and brick-and-mortar locations, but also the overall noise that is the Internet. We live in a world where consumer attention span is getting shorter and shorter: 40 percent of people abandon a website that takes more than three seconds to load, and the average shopping cart is abandoned more than 68 percent of the time. I'm hard pressed to find an ecommerce site that is not constantly scrambling to engage more and drive more sales. Technology is finally helping with those efforts in a big way.

Recruiters everywhere want to do work that matters. Work that encompasses their wide array of skills – skills that can attribute to a candidate's job placement and a company's overall success. So big that recruiters can make a lasting difference on the economy, which means they literally have the potential to change the world. They can change the world not because of their exceptional ability to sift through countless piles of resumes, or their skillfulness in searching job boards and analyzing data, they make a difference when putting their emotional intelligence into play – when engaging with candidates, building relationships, and placing high-quality talent that go on to lead a company to greatness. This is where recruiting potential lies – in our human nature.

Our analytics engine Kairos processes unstructured data from millions of sites, blogs, and social platforms like Twitter and Tumblr. Billions of public posts are then analyzed and classified across 25,000 topics, emotions, and demographics--turning noisy social data into insights. In order to create predictions around the elections using our analytics platform Kairos, we built 4 metrics: Awareness, Positivity, Negativity and Intent, of which only Negativity and Intent proved to be valuable in predicting elections. Negativity and Intent are natural language processing classifiers which take advantage of sentence structure as well as keyword matching. Then we modeled the data against survey polls, primary results, and survey pools to obtain weights of influence for each of the social indices.

Confidence interval is abbreviated as CI. In this new article (part of our series on robust techniques for automated data science) we describe an implementation both in Excel and Perl, and discuss our popular model-free confidence interval technique introduced in our original Analyticbridge article, as part of our (open source) intellectual property sharing. This is part of our series on data science techniques suitable for automation, usable by non-experts. The next one to be detailed (with source code) will be our Hidden Decision Trees. Figure 1 is based on simulated data that does not follow a normal distribution: see section 2 and Figure 2 in this article. Classical CI's are just based on 2 parameters: mean and variance.