Google DeepMind has discovered a way to create AI algorithms that run faster than those coded by humans, which could lead to more cost-effective software development and computing that is more efficient and sustainable, according to the Alphabet company. The breakthrough, detailed in the journal Nature, is called AlphaDev. It uses a form of machine learning called reinforcement that allows computers to build on their successes, honing strategies independent of human programmers. In this case, faster algorithms were developed for computer-science functions like sorting and hashing. Continue reading Google DeepMind’s AlphaDev Can Create Faster Algorithms

Alphabet’s London-based DeepMind loosed AlphaZero, its AI-powered system that can master games without human intervention, on Stockfish, the highest rated chess game engine, and crushed it. DeepMind developed the self-training method, dubbed deep reinforcement learning, specifically to attack strategy board game “Go,” and an earlier iteration of the system beat one of the world’s best “Go” players, although it needed human guidance. AlphaZero trained itself in chess in three days, rejecting red-marked moves after a mere 1,000 simulations. Continue reading DeepMind’s AlphaZero Defeats Leading Chess Game Engine

DeepMind recently released the full evaluation of AlphaZero, a single system capable of playing “Go,” chess, and shogi (Japanese chess). This new project builds on AlphaGo, a program that beat one of the best players in the world at the board game “Go” in 2016, and AlphaGo Zero, software capable of mastering the game from first principles. AlphaZero represents a dramatic step forward in AI research as it is one of the first intelligent systems capable of generalizing solutions to new problems with little to no human input. Continue reading DeepMind’s Learning Algorithm Could Prove a Game-Changer

If measured in press impressions, 2017 has most definitely been the “Year of AI,” But looking past the hype, a few things are clear: 1) progress in actual machine intelligence capability has been slow and fragmented; 2) applied AI is still the domain of less than 20 companies; and 3) still, machine learning (not AI) is being deployed across enterprise domains of numerous business sectors and creating big value. Similarly, and since it will take another year or two for current advances in machine learning to trickle down to the consumer sector, we’re not really expecting much breakthrough in AI or even machine learning at CES 2018. Continue reading Artificial Intelligence at CES 2018: Expect More of the Same