Deep Learning Weekly | Issue #81: Why the AI revolution hasn't happened yet, spherical CNNs, adversarial attacks in medicine

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Welcome to another week in deep learning. In industry, we start off with a through-provoking article
April 22 · Issue #81 · View online
Deep Learning Weekly
Welcome to another week in deep learning.
In industry, we start off with a through-provoking article by one of the giants in the field, take a peak behind the curtain of the banking industry to see why AI has not yet lived up to the hype.
We learn, this week, why RNN’s and LSTM are going the way of the dinosaurs and how to handle bias in text embeddings.
Of course, the mills of academia did not stand still this week either producing fascinating papers such as CNNs for spherical images, performing audio-visual speech separation or how the domain of medicine might be most at risk for adversarial exploits.

As always, happy reading and hacking!

Artificial Intelligence — The Revolution Hasn’t Happened Yet
Researchers Teach AI to Think Like Dogs and Find Out What They Know About the World - The Verge
AI in Banking: The Reality Behind the Hype – Financial Times
AI in the UK
Personalized Hey Siri
Events & Conferences
JupyterCon focuses on real-world practices and how to successfully implement interactive computation in your workflow and projects. You’ll discover the best practices for collaborative and reproducible data science; new use cases, and the expertise you need to transform your workflow with Jupyter tools.
The fall of RNN / LSTM
Instance Embedding: Segmentation Without Proposals
Google Developers Blog: Text Embedding Models Contain Bias
Hallucinogenic Deep Reinforcement Learning using Python and Keras
Jupyter: Tips, Tricks, Best Practices with Sample Code for Productivity Boost
Libraries & Code
Introducing TensorFlow Probability
Papers & Publications
Paper Repro: Deep Neuroevolution
Research Blog: Looking to Listen: Audio-Visual Speech Separation
Imagine This! Scripts to Compositions to Videos
Spherical CNNs
Adversarial Attacks Against Medical Deep Learning Systems