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Conversational AI tutorial, RNNs for particle physics, InfoGAN, NLP Coursera, NLP book, killer robots, Code2pix, Google AI principles, relational reasoning

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Hi all, We cracked 3,000 subscribers! Woohoo! 🎉Thanks to all of you for reading! This edition contain
 
June 11 · Issue #25 · View online
NLP News
Hi all,
We cracked 3,000 subscribers! Woohoo! 🎉Thanks to all of you for reading!
This edition contains: slides of the Conversational AI tutorial and using RNNs for particle physics; an in-depth walk-through of the InfoGAN paper, an NLP Coursera course and an NLP book (by Jacob Eisenstein); articles about “killer robots”; posts on Code2pix and using text descriptions for interpretability; Google’s AI principles and how Alexa chooses which skill to use; and new research papers (one on why a map between two monolingual embedding spaces is non-linear and two on relational reasoning).

Alexa: remind me to feed the baby
Talks and slides
Deep Learning for Conversational AI
RNNs and Beyond
Resources
InfoGAN · Depth First Learning
Natural Language Processing | Coursera
Natural Language Processing book by Jacob Eisenstein
Flying Car Nanodegree Program
"Killer robots"
MIT fed an AI data from Reddit, and now it only thinks about murder
Mark Zuckerberg, Elon Musk and the Feud Over Killer Robots
More blog posts and articles
Notes on EAMT 2018
Code2Pix - Deep Learning Compiler for Graphical User Interfaces
Explain yourself, machine. Producing simple text descriptions for AI interpretability.
Git pro tip
git pro tip: stick to a consistent style for git commit messages. I suggest starting them with “In which our hero”. E.g.,
“In which our hero implements batch processing”
Credit: @ReinH
Industry news
AI at Google: our principles
The Scalable Neural Architecture behind Alexa’s Ability to Arbitrate Skills
Paper picks
Characterizing Departures from Linearity in Word Translation (ACL 2018)
Relational inductive biases, deep learning, and graph networks (arXiv)
Relational recurrent neural networks (arXiv)
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