• Turing Award Won by 3 Pioneers in Artificial Intelligence
  On Wednesday, the Association for Computing Machinery, the world’s largest society of computing professionals, announced that Drs. Hinton, LeCun and Bengio had won this year’s Turing Award for their work on neural networks. The Turing Award, which was introduced in 1966, is often called the Nobel Prize of computing, and it includes a $1 million prize, which the three scientists will share...

• Towards Robust and Verified AI:
  Specification Testing, Robust Training, and Formal Verification
  In our latest post, our Robust & Verified AI team introduces our work on rigorous specification-testing (catching bugs), robust training (eliminating bugs) and formal verification (proving the absence of bugs) of ML models...

• How malevolent machine learning could derail AI
  AI security expert Dawn Song warns that “adversarial machine learning” could be used to reverse-engineer systems—including those used in defense...

 
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• Unsolved research problems vs. real-world threat models
  I personally think adversarial examples are highly worth studying, and should inspire serious concern. However, most of the justifications for why exactly they’re worrisome strike me as overly literal. I think much of the confusion comes from conflating an unsolved research problem with a real-world threat model...

• An algorithm can transform your doodles into photorealistic images
  In December of last year, at one of the world’s largest AI research conferences, American chipmaker Nvidia showed off an incredible new concept: using generative adversarial networks, or GANs (remember them?), to turn simple sketches into photorealistic scenes. The idea was the technology could easily render new virtual environments for video games and movies, or for training self-driving cars. Now the company has turned those same algorithms into a new doodling app called GauGAN, named after post-Impressionist artist Paul Gauguin...

• Unifying Physics and Deep Learning with TossingBot
  Though considerable progress has been made in enabling robots to grasp objects efficiently, visually self adapt or even learn from real-world experiences, robotic operations still require careful consideration in how they pick up, handle, and place various objects -- especially in unstructured settings. To explore this concept, we worked with researchers at Princeton, Columbia, and MIT to develop TossingBot: a picking robot for our real, random world that learns to grasp and throw objects into selected boxes outside its natural range...

• Automatically Charting Symptoms From Patient-Physician Conversations Using Machine Learning
  The beginning of converting to #AI voice-generated notes instead of doctors functioning as data clerks and use of human scribes...

• McDonald's Acquires Machine-Learning Startup Dynamic Yield for $300 Million
  Mention McDonald’s to someone today, and they're more likely to think about Big Mac than Big Data. But that could soon change: The fast-food giant has embraced machine learning, in a fittingly super-sized way...

• Machine Learning with Differential Privacy in TensorFlow
  Blog post that walks you through the few changes that need to be made to stochastic gradient descent to make it differentially private...

• Meta-Reinforcement Learning
  The general trend in machine learning research is to stop fine-tuning models, and instead use a meta-learning algorithm that automatically finds the best architecture and hyperparameters. However, in this blogpost I’ll call “meta-RL” the special category of meta-learning that uses recurrent models, applied to RL, as described in (Wang et al., 2016 arXiv) and (Wang et al, 2018 Nature Neuroscience)...

• TinyML Sees Big Hopes for Small AI
  A group of nearly 200 engineers and researchers gathered here to discuss forming a community to cultivate deep learning in ultra-low power systems, a field they call TinyML. In presentations and dialogs, they openly struggled to get a handle on a still immature branch of tech’s fastest-moving area in hopes of enabling a new class of systems...

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Want to post here? Email us for details >> team@datascienceweekly.org
 

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• Data Scientist (Analytics) - Pear Therapeutics - San Francisco or Boston
  
  At Pear Therapeutics, we have the privilege of building the world’s first-ever class of prescription digital therapeutics. By nature of our therapeutics as digital applications, we have access to rich datasets and unique opportunities to drive clinical outcomes. As a Data Scientist, you will be responsible for shaping and delivering data-driven insights. We are looking for data scientists with a deep product sense, who have an innate curiosity, and are eager to dive into large, complex datasets and create actionable insights...

        Want to post a job here? Email us for details >> team@datascienceweekly.org

• tf.constant_initializer: TensorFlow Constant Initializer
  Learn how to use TensorFlow constant initializer operation to initialize a constant in TensorFlow, via a screencast video and full tutorial transcript...

• Interpretable Machine Learning:
  A Guide for Making Black Box Models Explainable
  Computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book (free online) is about making machine learning models and their decisions interpretable...

• Common statistical tests are linear models (or: how to teach stats)
  This document shows the linear models underlying common parametric and non-parametric tests. Formulating all the tests in the same language highlights the many similarities between them...

• Reproducible Research with R and R Studio
  "a very practical book that teaches good practice in organizing reproducible data analysis and comes with a series of examples..."...
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page
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  P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian