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Data Science Weekly Newsletter
March 28, 2019
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...
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Data Science Resume Guide. This guide shows how to make your resume promote your best parts, what to leave out, how to tailor it to each job you want, as well as how to make your cover letter so good it can't be ignored!
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...
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...
It takes a lot of data to train an algorithm that is production-ready. For an algorithm to be highly accurate, you absolutely need ata that is labeled and annotated with nearly flawless quality. If you’re not sure what it takes to produce enormous volumes of quality training data, you need the Blueprint....
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Reproducible Research with R and R Studio
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