You know the moment when you go in for a yearly physical and the doctor asks, "So, how have you been?" I can't recall what I ate yesterday, let alone remember a pattern of headaches or the overall quality of my sleep. The problem is that I am human. I forget and can be lax when it comes to taking care of myself. Most health issues sneak up on us, and we're not inherently wired to remember patterns.

This was the title of a very popular book published in 2012, featuring several job interview questions (brain teasers) asked by Google's hiring managers to candidates. They apparently dropped all these questions, as they found out that they were not good indicators of career success. I had one phone interview with Google long ago, and was rejected right away. The interviewer was just focused on very technical details, and spent all her time arguing about Lasso regression, and was clearly looking for a specialist, dismissing people with a broad range of skills and non-standard approach to solving tech problems. Big companies do not value things like intuition, innovation, vision or a disruptive mindset (despite claiming the contrary), and for good reasons.

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.

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.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

Make it easy for your audience to quickly determine what they're about to digest. Use an abstract or introduction to recall your objectives and clearly state them for your readers. What is the problem that you've set out to solve? If you have a desired outcome or any expectations of your audience, say it, as this is the entire reason you're presenting them with your analysis. You then cover everything from your preamble in this section: the question you've been on a mission to answer, your hypothesis, and the methodology you've used.

The most entertaining video we posted on Video Friday a couple weeks ago was almost certainly Robot Drone Man, a parody of this PPAP (Pen Pineapple Apple Pen) video, which for some reason has 150 million views on YouTube. Parody or not, Robot Drone Man actually exists, and it's a project of Ilhan Bae, a researcher and futurist at the Korea Advanced Institute of Science and Technology (KAIST), who wrote in to tell us about it. Robot Drone Man is an avatar drone, in the same category as other telepresence robots like Double and Beam. It allows a remote human to have an embodied physical presence through a mobile robot, although in this case, the robot can fly, since most of it is a DJI S1000 octocopter. With a height of 1.4 meters (landed), it's designed to match the eye level of people interacting with it, and the remote operator can "gesticulate with two hands and head as if a distant operator exists in person," says Bae, adding that this is "the first trial to couple a telepresence robot in an upright position and drone platform into one body."

An Australian man acquitted of murder last month after his Tinder date plummeted off a balcony to her death faced new criticism after he acknowledged in a TV interview that the woman had screamed "no" a reported 33 times before she fell. "Yeah, she was certainly trying to make a lot of noise," Gable Tostee commented in the interview, which aired on the Nine Network's "60 Minutes." Critics also lashed out after The Courier Mail claimed Tostee was paid a six-figure sum for the interview. A senior detective called it "disgusting". The detective continued, "There's a lot of anger among police about this. Of course we respect the court's decision to find him not guilty but for him to now do a paid interview after everything this poor girl's family has been through, is horrific."

More recent deep lipreading approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman, 2016a). All existing works, however, perform only word classification, not sentence-level sequence prediction. Studies have shown that human lipreading performance increases for longer words (Easton & Basala, 1982), indicating the importance of features capturing temporal context in an ambiguous communication channel. Motivated by this observation, we present LipNet, a model that maps a variable-length sequence of video frames to text, making use of spatiotemporal convolutions, an LSTM recurrent network, and the connectionist temporal classification loss, trained entirely end-to-end. To the best of our knowledge, LipNet is the first lipreading model to operate at sentence-level, using a single end-to-end speaker-independent deep model to simultaneously learn spatiotemporal visual features and a sequence model. On the GRID corpus, LipNet achieves 93.4% accuracy, outperforming experienced human lipreaders and the previous 79.6% state-of-the-art accuracy.

Media reports say an artificial intelligence (AI) system called MogIA, developed by Sanjiv Rai, an innovator based in Mumbai, has predicted that Donald Trump will win Tuesday's presidential elections in the United States. Unveiled in 2004, the system apparently got it right in the last three presidential elections. It also predicted that Trump and Hillary Clinton will be the nominees of the Republican and Democratic Parties respectively. Rai is quoted as saying that the algorithm got even better as it has "learnt" from the last few rounds. MogIA is named after Mowgli from The Jungle Book.