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.
Today, the global economy is in a perilous situation. Wage growth is stagnant across all industrial economies. Jobs for less skilled workers are in constant decline and this decline is accelerating…
This is an illustration of a multi-compartment neural network model for deep learning. Left: Reconstruction of pyramidal neurons from mouse primary visual
This tutorial walks through a synchronous single-thread single-GPU (read malnourished) game-agnostic implementation of the recent AlphaGo Zero paper by DeepMind. It’s a beautiful piece of work that trains an agent for the game of Go through pure self-play without any human knowledge except the rules of the game.
We’ve co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats.
Abstract: Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose a method for learning useful skills without a reward function. We maximize an information theoretic objective using a maximum entropy policy.
Jump to:
Overview
Motivation
Notation
IRL Algorithms
LP small state space
Degeneracy heuristics
LP infinite state space
LP sampled trajectories
Summary
Numerical optimization is at the core of much of machine learning. In this post, we derive the L-BFGS algorithm, commonly used in batch machine learning applications.
Deep neural networks can be trained with relatively modest amounts of information and then successfully be applied to large quantities of unstructured data. …
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