Deep Hunt — Issue #60

Source: Deep Learning on Medium

Here are the highlights from an eventful week — Germany plans 3 billion euros in AI investment; How to Teach Artificial Intelligence Some Common Sense; Google open sources BigGAN generators; ImageNet/ResNet-50 Training in 224 Seconds


Germany plans 3 billion in AI investment

German govt wants to promote the use of AI applications in business within a framework that protects fundamental social values and individual rights and are planning to invest 3 billion euros!

Standard Cognition raises $40M to replace retailers’ cashiers with cameras

Just an year old, Standard Cognition competes with Amazon Go in making the shopping experience seamless. Unlike Amazon Go, they deploy overhead cameras that identify you by shape and movement, not facial recognition.


How to Teach Artificial Intelligence Some Common Sense

This WIRED article explores the current limits, and current power of deep learning and the challenges of making AI truly able to reason.

These Animated AI Bots Learned to Dress Themselves, Awkwardly

A study by researchers at Georgia Institute of Technology shows creating systems that can perform more mundane tasks — such as dressing themselves — is proving to be an enormous challenge as well.

Tutorials, Tools and Tips

Heads-up for Deploying Scikit-learn Models to Production: Quick Checklist

This is a quick checklist of important things one needs to keep in mind while pushing your scikit-learn models into production.

Key Papers in Deep RL

Here’s a list of papers in Deep Reinforcement Learning curated by the folks at OpenAI. It’s a great resource for someone looking to get started and it’s a lot of reading!

Large Scale GAN Training for High Fidelity Natural Image Synthesis

DeepMind has opensourced the BigGAN generators on TFHub. Dig in to explore the world of most impressive GAN samples generated yet.


Gradient Descent Finds Global Minima of Deep Neural Networks

This research work has generated a lot of conversation among the researchers. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet).

ImageNet/ResNet-50 Training in 224 Seconds

By applying a two techniques – batch size control and 2D-Torus all-reduce this paper claims to have successfully trained ImageNet/ResNet-50 in 224 seconds without significant accuracy loss on ABCI cluster!

A quick summary of the progress made over the years