• Key trends from NeurIPS 2019
  With 51 workshops, 1428 accepted papers, and 13k attendees, saying that NeurIPS is overwhelming is an understatement. I did my best to summarize the key trends I got from the conference...

• Why video games and board games aren’t a good measure of AI intelligence
  Beating humans at chess and Go is impressive, yes, but what does it matter if the smartest computer can be out-strategized in general problem-solving by a toddler or a rat? This is a criticism put forward by AI researcher François Chollet, a software engineer at Google and a well-known figure in the machine learning community. Chollet is the creator of Keras, a widely used program for developing neural networks, the backbone of contemporary AI. In this interview, we learn more...

• The year in AI: 2019 ML/AI advances recap
  It has become somewhat of a tradition for me to do an end-of-year retrospective of advances in AI/ML, so here we go again!...

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• Facebook has a neural network that can do advanced math
  Other neural nets haven’t progressed beyond simple addition and multiplication, but this one calculates integrals and solves differential equations...

• NeurIPS 2019 featured robot curling players and coffee makers
  One particularly active category of research this year was robotics, which saw workshop and paper contributions from Intel, the University of California at Berkeley, and other leaders. Perhaps the most intriguing of these were novel approaches to training a team of machines to jointly solve a problem, and a multi-stage learning technique that uses pixel-level translation of human videos to train robots to complete tasks...

• Algorithmic Bias Is Bad. Uncovering It Is Good.
  We keep stumbling across examples of discrimination in algorithms, but that’s far better than their remaining hidden...

• There’s a new way to tame language AI so it doesn’t embarrass you
  Models can now be steered to generate text based on the topic or sentiment of your choosing...

• Building AI that can master complex cooperative games with hidden info
  We’ve built an AI bot that achieves state-of-the-art results in Hanabi, a collaborative card game that has been cited as a benchmark game for AI research because it features both cooperative gameplay and imperfect information...

• SynSin: End-to-end View Synthesis from a Single Image
  Single image view synthesis allows for the generation of new views of a scene given a single input image. This is challenging, as it requires comprehensively understanding the 3D scene from a single image. As a result, current methods typically use multiple images, train on ground-truth depth, or are limited to synthetic data. We propose a novel end-to-end model for this task; it is trained on real images without any ground-truth 3D information...

• Scalable Active Learning for Autonomous Driving
  To address inefficiencies in training data selection for autonomous driving DNNs, we implemented a scalable active learning approach on our internal production-grade AI platform called MagLev...

• Famous Fluid Equations Spring a Leak
  Mathematicians have suspected for years that under specific circumstances, the Euler equations fail. But they’ve been unable to identify an exact scenario in which this failure occurs. Until now....

• A Simple Baseline for Bayesian Uncertainty in Deep Learning
  We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning...

 
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• Manager, Data Science - JetBlue - Long Island City, NY
  
  JetBlue is seeking a Data Science Manager to lead a team of data scientists who will design experiments and develop machine learning models to address the company’s most complex data problems. We are looking for an experienced data scientist with broad knowledge of machine learning and statistical techniques. This individual will establish best practices for data science workflows and knows how to create an environment that enables data scientists to perform at their best. Beyond a great culture, the benefits (free flights!) are hard to beat...

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• An Introduction to Neural Information Retrieval
  This tutorial introduces basic concepts and intuitions behind neural IR models, and places them in the context of classical non-neural approaches to IR. We begin by introducing fundamental concepts of retrieval and different neural and non-neural approaches to unsupervised learning of vector representations of text. We then review IR methods that employ these pre-trained neural vector representations without learning the IR task end-to-end...

• Generative Teaching Networks
  This video explores an exciting new Meta Learning paper in which the classifier learns its own training data! This video will explore the application of this to Neural Architecture Search, weight normalization, and the use of curriculum learning!...

• Common Voice: A Massively-Multilingual Speech Corpus
  The Common Voice corpus is a massively-multilingual collection of transcribed speech intended for speech technology research and development. The most recent release includes 29 languages, and as of November 2019 there are a total of 38 languages collecting data. Over 50,000 individuals have participated so far, resulting in 2,500 hours of collected audio...

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  P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian