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