As OpenAI’s CTO and co-founder Greg Brockman told The Verge earlier this year, if it takes a human between 12,000 and 20,000 hours of practice to master a certain skill, then the bots burn through “100 human lifetimes of experience every single day.” As a result, the bots have to play Dota 2 at an accelerated rate, cramming 180 years of training time into each day. As you might guess, this is an extremely inefficient way to learn. This means the bots start out playing completely randomly, and over time, they learn to connect certain behaviors to rewards.
“100 human lifetimes of experience every single day” OpenAI’s engineers help this process along by rewarding them for completing certain tasks (like killing an opponent or winning a match) but nothing more than that. (It has its weaknesses, but it also produces incredible results, including AlphaGo.) Instead of coding the bots with the rules of Dota 2, they’re thrown into the game and left to figure things out for themselves. This is a common training method that’s essentially trial-and-error at a huge scale. The five bots (which operate independently but were trained using the same algorithms) were taught to play Dota 2 using a technique called reinforcement learning. By training its team of Dota 2 bots (dubbed the OpenAI Five), the lab says it wants to develop systems that can “handle the complexity and uncertainty of the real world.” The bots were created by OpenAI as part of its broad research remit to develop AI that “benefits all of humanity.” It’s a directive that justifies a lot of different research and has attracted some of the field’s best scientists. Image: Valve Learning like a bot: if at first you don’t succeedįirst, let’s put last week’s matches in context. Gameplay is complex, and matches typically last more than 30 minutes. In other words, they’re closer to the sorts of problems we want AI to tackle in real life.Ī screenshot of Dota 2, a fantasy arena battle game where two teams of five heroes fight to destroy one another’s base. They withhold information from players take place in complex, ever-changing environments and require the sort of strategic thinking that can’t be easily simulated. Although video games lack the intellectual reputation of Go and chess, they’re actually much harder for computers to play. Recently, researchers have turned their attention to video games as the next challenge. Most notable was the defeat of the world’s best Go players by DeepMind’s AlphaGo, an achievement that experts thought out of reach for at least a decade. In the human-AI scorecard, artificial intelligence has racked up some big wins recently. But the match was also something of a litmus test for artificial intelligence: the latest high-profile measure of our ambition to create machines that can out-think us.
The competitors were playing Dota 2, a phenomenally popular and complex battle arena game.
#We need to go deeper ai crew pro
In a best-of-three match, two teams of pro gamers overcame a squad of AI bots that were created by the Elon Musk-founded research lab OpenAI. Last week, humanity struck back against the machines - sort of.Īctually, we beat them at a video game.
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