Sam Altman is the cofounder of AI research lab OpenAI, which developed the software that took on the humans at Dota 2 . Machines have scored another victory over humans in the ongoing man vs machine saga after a team of AIs working together successfully beat five semi-professional humans at the multiplayer online battle arena (MOBA) video game of Dota 2. 'Silicon beat meat' (as one spectator put it) 2-1. The team of AIs are known as OpenAI Five and they were developed by OpenAI, an artificial intelligence research lab founded by Elon Musk and Y Combinator president Sam Altman. OpenAI's agents previously beat some of the top human players in 1v1 matches but this is the first time a group of agents have come together to beat a team of humans in the top 99.95 percentile at Dota 2. The team modes are harder as they require more coordination and long-term planning. Greg Brockman, OpenAI cofounder and CTO, described the day of play as an "emotional ride" on Twitter.

The researchers are with OpenAI, a nonprofit artificial intelligence research group established in 2015.

OpenAI Five, the artificial intelligence bot that previously destroyed humans in the video game Dota 2, has just undergone another "benchmark" test -- taking on semi-professional Dota 2 players ranked in the 99.95th percentile in the world. Good news for humans: We fared a little better this time. After wiping the floor in warm up games with the audience, OpenAI Five had to take on a team of humans including former Dota 2 professionals and casters Merlini, Fogged, Cap and Blitz. In a three game series, OpenAI Five started strongly, winning the first two games comprehensively. In the final game, the OpenAI team let the audience select their team of five heroes, severely handicapping our future robot overlords.

Missing values widely exist in many real-world datasets, which hinders the performing of advanced data analytics. Properly filling these missing values is crucial but challenging, especially when the missing rate is high. Many approaches have been proposed for missing value imputation (MVI), but they are mostly heuristics-based, lacking a principled foundation and do not perform satisfactorily in practice. In this paper, we propose a probabilistic framework based on deep generative models for MVI. Under this framework, imputing the missing entries amounts to seeking a fixed-point solution between two conditional distributions defined on the missing entries and latent variables respectively. These distributions are parameterized by deep neural networks (DNNs) which possess high approximation power and can capture the nonlinear relationships between missing entries and the observed values. The learning of weight parameters of DNNs is performed by maximizing an approximation of the log-likelihood of observed values. We conducted extensive evaluation on 13 datasets and compared with 11 baselines methods, where our methods largely outperforms the baselines.

OpenAI has trained a human-like robot hand to manipulate physical objects with unprecedented dexterity. Their system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality, adapting to real-world physics. Dactyl learns from scratch using the same general-purpose reinforcement learning algorithm and code as OpenAI Five. The results show that it's possible to train agents in simulation and have them solve real-world tasks, without physically-accurate modeling of the world. Dactyl is a system for manipulating objects using a Shadow Dexterous Hand.

It's pretty crazy how far artificial intelligence has come, and what this robotic hand from Elon Musk's OpenAI is able to do is pretty amazing proof. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. It's pretty crazy how far artificial intelligence has come, and what this robotic hand from Elon Musk's OpenAI is able to do is pretty amazing proof.

OpenAI, a nonprofit, San Francisco-based AI research company backed by Elon Musk, Reid Hoffman, and Peter Thiel, among other titans of industry, made headlines in June when it announced that the latest version of its Dota 2-playing AI -- dubbed OpenAI Five -- managed to beat amateur players. Today, it unveiled another first: a robotics system that can manipulate objects with humanlike dexterity. In a forthcoming paper ("Dexterous In-Hand Manipulation"), OpenAI researchers describe a system that uses a reinforcement model, where the AI learns through trial and error, to direct robot hands in grasping and manipulating objects with state-of-the-art precision. All the more impressive, it was trained entirely digitally, in a computer simulation, and wasn't provided any human demonstrations by which to learn. "While dexterous manipulation of objects is a fundamental everyday task for humans, it is still challenging for autonomous robots," the team writes.

That's how much virtual computing time it took researchers at OpenAI, the non-profit artificial intelligence lab funded by Elon Musk and others, to train its disembodied hand. The team paid Google $3,500 to run its software on thousands of computers simultaneously, crunching the actual time to 48 hours. After training the robot in a virtual environment, the team put it to a test in the real world.