Intuition Machine - Issue #53

By Intuition Machine • Issue #53 • View online

Welcome to the weekly newsletter of Deep Learning AI and Blockchain Convergence. We hope that this newsletter will appeal to all those interested in Deep Learning developments and its relationship to decentralized consensus architectures.

Artificial Intuition : The Improbable Deep Learning Revolution

I challenge you to find a field as interesting and exciting as Deep Learning.

gumroad.com • Share

The Building Blocks of Interpretability

Interpretability techniques are normally studied in isolation. We explore the powerful interfaces that arise when you combine them – and the rich structure of this combinatorial space.

distill.pub • Share

Investigating Human Priors for Playing Video Games

Human gameplay on game version without any object priors Human gameplay on original game version

rach0012.github.io • Share

Reptile: A Scalable Meta-Learning Algorithm

We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards the final parameters learned on that task.

blog.openai.com • Share

ONNX

The new open ecosystem for interchangeable AI models

onnx.ai • Share

The Great AI Paradox - MIT Technology Review

Don’t worry about supersmart AI eliminating all the jobs. That’s just a distraction from the problems even relatively dumb computers are causing.

www.technologyreview.com • Share

Machine Learning Crash Course | Google Developers

Educational resources for machine learning

developers.google.com • Share

Center for Humane Technology

The Center for Humane Technology is a world-class team of former tech
insiders and CEOs who are advancing thoughtful solutions to change the
culture, business incentives, design techniques, and organizational
structures driving how technology hijacks our brains.

humanetech.com • Share

AI Just Learned How to Boost the Brain's Memory | WIRED

If we can’t understand our own brains, maybe the machines can do it for us.

www.wired.com • Share

Can increasing depth serve to accelerate optimization? – Off the convex path

Algorithms off the convex path.

www.offconvex.org • Share

Pandas on Ray - RISE Lab

Pandas on Ray accelerates Pandas queries by 4x on an 8-core machine, only requiring users to change a single line of code in their notebooks.

rise.cs.berkeley.edu • Share

Ubisoft's AI in Far Cry 5 and Watch Dogs could change gaming | WIRED UK

The gaming company’s Commit Assistant AI tool has been trained to spot when programmers are about to make a mistake

www.wired.co.uk • Share

Low-cost EEG can now be used to reconstruct images of what you see | KurzweilAI

(left:) Test image displayed on computer monitor. (right:) Image captured by EEG and decoded. (credit: Dan Nemrodov et al./eNeuro) A new technique developed

www.kurzweilai.net • Share

Notes from Coursera Deep Learning courses by Andrew Ng

My notes from the excellent Coursera specialization by Andrew Ng

www.slideshare.net • Share

Metalearning Symposium

Several approaches to metalearning have emerged, including those based on Bayesian optimization, gradient descent, reinforcement learning, and evolutionary computation. The symposium presents an overview of these approaches, given by the researchers who developed them.

metalearning-symposium.ml • Share

A Less-Artificial Intelligence - MIT Technology Review

Studying 70,000 mouse neurons could help Andreas Tolias build smarter AI.

www.technologyreview.com • Share

Counterintuitive Properties of High Dimensional Space - Lost in Spacetime

marckhoury.github.io • Share

Visualizing Outliers | FlowingData

FlowingData

flowingdata.com • Share

HALP: High-Accuracy Low-Precision Training · Stanford DAWN

Low-precision computation has been gaining a lot of traction in machine learning.

dawn.cs.stanford.edu • Share

[1803.00657] Evolutionary Generative Adversarial Networks

arxiv.org • Share

GitHub - onnx/onnxmltools: ONNXMLTools enables conversion of models to ONNX. Currently supports CoreML and SciKit

GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 80 million projects.

github.com • Share

[1803.02839v1] The emergent algebraic structure of RNNs and embeddings in NLP

arxiv.org • Share

Queryparser, an Open Source Tool for Parsing and Analyzing SQL

Written in Haskell, Queryparser is Uber Engineering’s open source tool for parsing and analyzing SQL queries that makes it easy to identify foreign-key relationships in large data warehouses.

eng.uber.com • Share

The Deep Learning AI Playbook

“Whatever you are studying right now if you are not getting up to speed on deep learning, neural networks, etc., you lose,” says Mark Cuban.
“We are going through the process where software will automate software, automation will automate automation.”

deeplearningplaybook.com • Share

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