Top /r/MachineLearning Posts, 2016: Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment

/r/MachineLearning continues to be one of the best places for breaking machine learning news, projects, discussions, and exclusive content such as AMAs, for veteran machine learning scientists as well as beginners, and the entire spectrum in between. As the new year begins, we will look back at the top stories on /r/MachineLearning from 2016. To help convey a sense of what a particular thread/discussion/post is about, I have included a poignant quote from said item when appropriate. The top 10 /r/MachineLearning posts of 2016 were: 1. AMA: We are the Google Brain team. We’d love to answer your questions about machine learning. We’re a group of research scientists and engineers that work on the Google Brain team. Our group’s mission is to make intelligent machines, and to use them to improve people’s lives. For the last five years, we’ve conducted…
Original Post: Top /r/MachineLearning Posts, 2016: Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment

Top KDnuggets tweets, Dec 14-20: False positives versus false negatives: Best explanation ever

Most popular @KDnuggets tweets for Dec 14-20 wereMost Retweeted:False positives versus false negatives: Best explanation ever https://t.co/YAJW01V6ro https://t.co/4V3eILADD2Most Favorited:False positives versus false negatives: Best explanation ever https://t.co/YAJW01V6ro https://t.co/4V3eILADD2Most Viewed:#MachineLearning & #AI experts: Main Developments 2016, Key Trends 2017 https://t.co/ZccewwG8dV @AjitJaokar @dtunkelang @randal_olson https://t.co/kH2HPP6WylMost Clicked:#MachineLearning & #AI experts: Main Developments 2016, Key Trends 2017 https://t.co/GI0GLkxAY4 @xamat @pmddomingos @brohrer @etzioni https://t.co/X7FpbHne4ETop 10 most engaging Tweets False positives versus false negatives: Best explanation ever https://t.co/YAJW01V6ro https://t.co/4V3eILADD2 #MachineLearning & #AI experts: Main Developments 2016, Key Trends 2017 https://t.co/GI0GLkxAY4 @xamat @pmddomingos @brohrer @etzioni https://t.co/X7FpbHne4E Official code repository for #MachineLearning with #TensorFlow book https://t.co/P24WagesuX https://t.co/b1BFNhXcHq Top 10 Essential Books for the #Data Enthusiast https://t.co/MroBU0npwl https://t.co/o8vtgola37 #DeepLearning Works Great Because the Universe, Physics and the Game of Go are Vastly Simpler than Prior Models https://t.co/KvrYHnrnCM https://t.co/qMzK0Fe8Ne Building Jarvis by some guy named Mark Zuckerberg via @Facebook #AI…
Original Post: Top KDnuggets tweets, Dec 14-20: False positives versus false negatives: Best explanation ever

What we can learn from AI mistakes

By Lukas Biewald, CEO CrowdFlower.AI has been making a lot of progress lately by almost any standard. It has quietly become part of our world, powering markets, websites, factories, business processes and soon our houses, our cars and everything around us. But the biggest recent successes have also come with surprising failures. Tesla impressed the world by launching a self driving car, but then crashed in cases a human would have easily handled. AlphaGo beat the human champion Go player years before most experts possible, but completely collapsed after its opponent played an unusual move. These failures might seem baffling if we follow our intuition and think of artificial intelligence the same way we think about human intelligence. AI competes with the world’s best and then fails in seemingly simple situations. But the state of the art in artificial intelligence…
Original Post: What we can learn from AI mistakes

Predictions for Deep Learning in 2017

Deep learning is all the rage as we move into 2017. Grounded in multilayer neural networks, this technology is the foundation of artificial intelligence, cognitive computing, and real-time streaming analytics in many of the most disruptive new applications. For data scientists, deep learning will be a top professional focus going forward. Here are my predictions for the chief trends in deep learning in the coming year: The first hugely successful consumer application of deep learning will come to market: I predict that deep learning’s first avid embrace by the general public will come in 2017. And I predict that it will be to process the glut of photos that people are capturing with their smartphones and sharing on social media. In this regard, the golden deep-learning opportunities will be in apps that facilitate image search, auto-tagging, auto-correction, embellishment, photorealistic rendering,…
Original Post: Predictions for Deep Learning in 2017

New Book: TensorFlow for Machine Intelligence – KDnuggets Holiday Offer

TensorFlow for Machine Intelligence is a hands-on introduction to learning algorithms and the “TensorFlow book for humans.” For a limited holiday special, KDnuggets readers get a 40% discount, available here. TensorFlow for Machine Intelligence has been dubbed a “TensorFlow book for humans.” It’s a hands-on introduction to learning algorithms, and is for beginners who want to learn TensorFlow and Machine Learning. This book doesn’t bog you down with too much math, but it provides you with enough to help you understand TensorFlow. If you know a little machine learning (or not), and have heard about TensorFlow, but found the documentation too daunting to approach, this book is for you. The machine learning is covered after you’ve become comfortable with TensorFlow’s mechanics and the core API. If you are interested in leveling up for the new year, this book is available…
Original Post: New Book: TensorFlow for Machine Intelligence – KDnuggets Holiday Offer

Implementing a CNN for Human Activity Recognition in Tensorflow

In this post, we will see how to employ Convolutional Neural Network (CNN) for HAR, that will learn complex features automatically from the raw accelerometer signal to differentiate between different activities of daily life. By Aaqib Saeed, University of Twente. In the recent years, we have seen a rapid increase in smartphones usage which is equipped with sophisticated sensors such…
Original Post: Implementing a CNN for Human Activity Recognition in Tensorflow

Introduction to Trainspotting: Computer Vision, Caltrain, and Predictive Analytics

We previously analyzed delays using Caltrain’s real-time API to improve arrival predictions, and we have modeled the sounds of passing trains to tell them apart. In this post we’ll start looking at the nuts and bolts of making our Caltrain work possible. By Chloe Mawer, Colin Higgins & Matthew Rubashkin, Silicon Valley Data Science. Here at Silicon Valley Data Science, we have a slight…
Original Post: Introduction to Trainspotting: Computer Vision, Caltrain, and Predictive Analytics