Gripping something with your hand is one of the first things you learn to do as an infant, but it's far from a simple task, and only gets more complex and variable as you grow up. This complexity makes it difficult for machines to teach themselves to do, but researchers at Elon Musk and Sam Altman-backed OpenAI have created a system that not only holds and manipulates objects much like a human does, but developed these behaviors all on its own. Many robots and robotic hands are already proficient at certain grips or movements -- a robot in a factory can wield a bolt gun even more dexterously than a person. But the software that lets that robot do that task so well is likely to be hand-written and extremely specific to the application. You couldn't for example, give it a pencil and ask it to write.

A new system has vastly improved robots' abilities to grip, slide and manipulate objects with almost the same ease as a human hand. OpenAI, a robotics research group that's backed by tech titans including Elon Musk and Peter Thiel, trained the robot hand to be able to manipulate objects using a sophisticated system called Dactyl. Researchers let a computer simulation of a robot hand learn new movements via trial and error, which served as the dataset for the actual robot hand - meaning it required zero human intervention. Researchers at OpenAI first trained a virtual hand, powered by a neural network, to learn how to manipulate a cube using various grasps. Via simulations, the virtual hand could try out thousands of different poses in just a few seconds.

Vid Human hands are surprisingly dexterous: they can knit clothes, stuff delivery packages with things, play the piano, and so on, albeit with practice. Yet if you're worried machines are going to take these pleasures away from us, be assured us mortals can, for now, pick up these skills faster than robots can, judging from the following findings. Researchers at OpenAI trained, using about a hundred years of simulated experience, a robotic system called Dactyl to rotate and orientate a cube. Dactyl exists not just in its virtual world, though. It can also control a Shadow Dexterous Hand: a metal meathook complete with five fingers, force sensors, and 24 degrees of freedom – pretty close to a human's 27 degrees of freedom. The cube it's told to fondle features a specific letter and color on each of its six faces, and it has to figure out how to manipulate the object so that it finds the requested symbol.

It's still early days in creating the kind of human-like androids we see in the movies, but new research brings us ever closer to the idea. Boston Dynamics has become the de facto image of locomotion for both humans and their pets, while LG already has its CLOi porter'bots and DARPA is working on centaur-like designs for disaster relief. Now, researchers at the Elon Musk-founded OpenAI are working on making robot hands more dextrous. According to a blog post, the team has trained a human-like robot hand called the Shadow Dextrous Hand to manipulate real-world objects like a child's block. It uses the same algorithms and code from its OpenAI Five project, which has been training DOTA 2 bots to play video games.

In-hand manipulation is one of those things that's fairly high on the list of "skills that are effortless for humans but extraordinarily difficult for robots." Without even really thinking about it, we're able to adaptively coordinate four fingers and a thumb with our palm and friction and gravity to move things around in one hand without using our other hand--you've probably done this a handful (heh) of times today already, just with your cellphone. It takes us humans years of practice to figure out how to do in-hand manipulation robustly, but robots don't have that kind of time. Learning through practice and experience is still the way to go for complex tasks like this, and the challenge is finding a way to learn faster and more efficiently than just giving a robot hand something to manipulate over and over until it learns what works and what doesn't, which would probably take about a hundred years. Rather than wait a hundred years, researchers at OpenAI have used reinforcement learning to train a convolutional neural network to control a five-fingered Shadow hand to manipulate objects, all in just 50 hours.

Elon Musk is kinda worried about AI. ("AI is a fundamental existential risk for human civilization and I don't think people fully appreciate that," as he put it in 2017.) So he helped found a research nonprofit, OpenAI, to help cut a path to "safe" artificial general intelligence, as opposed to machines that pop our civilization like a pimple. Yes, Musk's very public fears may distract from other more real problems in AI. But OpenAI just took a big step toward robots that better integrate into our world by not, well, breaking everything they pick up. OpenAI researchers have built a system in which a simulated robotic hand learns to manipulate a block through trial and error, then seamlessly transfers that knowledge to a robotic hand in the real world.

We've created an AI system, OpenAI Five, which has started to defeat amateur human teams. This video contains an overview of our system, some example gameplay, and professional caster Blitz's analysis of our bot, as we start to gear up for playing a professional team at this year's Dota world championships, The International. See our blog post for more details: https://blog.openai.com/openai-five.

The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.

Subcommittee on Research and Technology and Subcommittee on Energy Hearing - Artificial Intelligence - June 26th, 2018 Dr. Tim Persons, chief scientist, GAO Mr. Greg Brockman, co-founder and chief technology officer, OpenAI Dr. Fei-Fei Li, chairperson of the board and co-founder, AI4ALL OpenAI was founded by Elon Musk and Sam Altman

Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2. While today we play with restrictions, we aim to beat a team of top professionals at The International in August subject only to a limited set of heroes. We may not succeed: Dota 2 is one of the most popular and complex esports games in the world, with creative and motivated professionals who train year-round to earn part of Dota's annual $40M prize pool (the largest of any esports game). OpenAI Five plays 180 years worth of games against itself every day, learning via self-play. It trains using a scaled-up version of Proximal Policy Optimization running on 256 GPUs and 128,000 CPU cores -- a larger-scale version of the system we built to play the much-simpler solo variant of the game last year. Using a separate LSTM for each hero and no human data, it learns recognizable strategies.