Lifeboat Foundation

An Instagram filter with AI-generated poetry.

So why not ask the neurons what they want to see?

Read: The human remembering machine

That was the idea behind XDREAM, an algorithm dreamed up by a Harvard student named Will Xiao. Sets of those gray, formless images, 40 in all, were shown to watching monkeys, and the algorithm tweaked and shuffled those that provoked the strongest responses in chosen neurons to create a new generation of pics. Xiao had previously trained XDREAM using 1.4 million real-world photos so that it would generate synthetic images with the properties of natural ones. Over 250 such generations, the synthetic images became more and more effective, until they were exciting their target neurons far more intensely than any natural image. “It was exciting to finally let a cell tell us what it’s encoding instead of having to guess,” says Ponce, who is now at Washington University in St. Louis.

Continue reading “AI Evolved These Creepy Images to Please a Monkey’s Brain” »

LIVERMORE, Calif. — As the demands on computers are rapidly changing to more data-centric tasks — such as image processing, voice recognition or autonomous driving functions — there quickly arises a need for greater computing efficiencies.

This paper published in Nature on 26th February 2015, describes a DeepRL system which combines Deep Neural Networks with Reinforcement Learning at scale for the first time, and is able to master a diverse range of Atari 2600 games to superhuman level with only the raw pixels and score as inputs.

For artificial agents to be considered truly intelligent they should excel at a wide variety of tasks that are considered challenging for humans. Until this point, it had only been possible to create individual algorithms capable of mastering a single specific domain. With our algorithm, we leveraged recent breakthroughs in training deep neural networks to show that a novel end-to-end reinforcement learning agent, termed a deep Q-network (DQN), was able to surpass the overall performance of a professional human reference player and all previous agents across a diverse range of 49 game scenarios.

Starting from zero knowledge and without human data, AlphaGo Zero was able to teach itself to play Go and to develop novel strategies that provide new insights into the oldest of games.

DeepMind’s Professor David Silver describes AlphaGo Zero, the latest evolution of AlphaGo, the first computer program to defeat a world champion at the ancient Chinese game of Go. Zero is even more powerful and is arguably the strongest Go player in history.

Previous versions of AlphaGo initially trained on thousands of human amateur and professional games to learn how to play Go. AlphaGo Zero skips this step and learns to play simply by playing games against itself, starting from completely random play. In doing so, it quickly surpassed human level of play and defeated the previously published champion-defeating version of AlphaGo by 100 games to 0.

Continue reading “AlphaGo Zero: Discovering new knowledge” »

2017 NIPS Keynote by DeepMind’s David Silver. Dr. David Silver leads the reinforcement learning research group at DeepMind and is lead researcher on AlphaGo. He graduated from Cambridge University in 1997 with the Addison-Wesley award.

Recorded: December 6th, 2017

O.o…

Two robotic parents can merge their code into an “offspring.”

With the possibility of millions or an infinite number of problems automating everything will cause all things to be solved digitally into a simple math problem. The problems could essentially be hacked by shores algorithm or maybe a theory of everything like m theory or Stephen Hawking’s theory of everything. Maybe it is just as simple as a basic formula like Einstein created E=mc². Also like some mathematicians have theorized maybe just one line of code that solves everything.

Automation is a game-changer for modern problem-solving – enabling not only visibility to real-time operations but the ability to effectively project the impact of potential solutions into the future. As problem-solvers become more comfortable using the new tools available to them, companies will be able to effectively isolate (and avoid) the impact of problems to their operations and focus their resources on solving the underlying issues and enabling long-term success. Learn More here.