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.

In the video game, two teams of five players are placed at opposite ends of a square arena, and fight past each other using melee and spells to destroy their opponent's base. For the researcher's software to win against the pros, it would be like a robot learning to dunk on Michael Jordan. Games are an easy way for those of us without PhDs to understand how far AI research has come: When put in complex situations, can an AI beat humans? We understand what it meant for IBM's DeepBlue to beat Garry Kasparov in chess, and DeepMind's AlphaGo beating Lee Sedol in Go--decades of human practice and skill were defeated by mechanical computation. Outside of those publicized matches, AI researchers have worked for decades to build AI agents that are superhuman at playing Atari games, checkers, and even Super Smash Bros.

A teenager from London has been given some extra homework for the school holidays by the co-founder of Artificial Intelligence firm DeepMind. Aron Chase, 17, tweeted Shane Legg looking for five top tips on how to succeed in the burgeoning field of AI. Dr Legg replied with some very specific advice, including brushing up on linear algebra. Some AI scientists command six-figure salaries because of the current shortage of experts in the field. DeepMind, a sister company to Google, is considered by many to be at the cutting-edge of AI research.

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.

Aron Chase is a 17-year-old student in London.Aron Chase Some artificial intelligence researchers at companies like Google and Facebook are now earning more money than investment bankers at Goldman Sachs and J.P. Morgan. These researchers also have the privilege of working in a field of technology that's poised to have a major impact on the world we live in. But, for many people, it's not clear how to go about getting a job in AI. This week, 17-year-old Londoner Aron Chase asked Shane Legg -- the chief scientist and cofounder of DeepMind, an AI lab acquired by DeepMind for a reported £400 million -- for five pieces of advice for an AI enthusiast like himself. "Hey Shane I'm currently 17 from London England and am very passionate about AI, also learning about in-depth human needs. What would be the 5 pieces of advice and tips you would give to a young person like me?" Chase wrote on Twitter.

Aron Chase is a 17-year-old student in London.Aron Chase Some artificial intelligence researchers at companies like Google and Facebook are now earning more money than investment bankers at Goldman Sachs and J.P. Morgan. These researchers also have the privilege of working in a field of technology that's poised to have a major impact on the world we live in. But, for many people, it's not clear how to go about getting a job in AI. This week, 17-year-old Londoner Aron Chase asked Shane Legg -- the cofounder of DeepMind, an AI lab acquired by DeepMind for a reported £400 million -- for five pieces of advice for an AI enthusiast like himself. "Hey Shane I'm currently 17 from London England and am very passionate about AI, also learning about in-depth human needs. What would be the 5 pieces of advice and tips you would give to a young person like me?" Chase wrote on Twitter.

Elon Musk, the founders of DeepMind, and other AI luminaries have signed a letter that guarantees they won't develop "lethal autonomous weapons." It's the latest effort to draw attention to the moral risks raised by AI weapons, but prohibiting the technology may ultimately prove challenging. Big shots: The letter was signed by Musk; DeepMind's Demis Hassabis, Shane Legg, and Mustafa Suleyman; Skype founder Jaan Tallinn; and the well-known AI researchers Stuart Russell, Yoshua Bengio, and Jürgen Schmidhuber. Peace movement: Tech companies are being forced to examine military uses of their technology. Employee outrage recently prompted Google to promise that it wouldn't let its AI be used to make weapons.

This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventional ZSL methods have equally dealt with all the available attributes, but this sometimes causes misclassification. This is because an attribute that is effective for classifying instances of one class is not always effective for another class. In this case, a metric of classifying the latter class can be undesirably influenced by the irrelevant attribute. This paper solves this problem by taking the importance of each attribute for each class into account when calculating the metric. In addition to the proposal of this new method, this paper also contributes by providing a dataset for pose classification based on wearable sensors, named HDPoseDS. It contains 22 classes of poses performed by 10 subjects with 31 IMU sensors across full body. To the best of our knowledge, it is the richest wearable-sensor dataset especially in terms of sensor density, and thus it is suitable for studying zero-shot pose/action recognition. The presented method was evaluated on HDPoseDS and outperformed relative improvement of 5.9% in comparison to the best baseline method.