DARPA正在利用GAMERS的大脑波来训练机器人群

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DARPA正在利用GAMERS的大脑波来训练机器人群

A team of artificial intelligence researchers at the University at Buffalo plans to study the brain waves and eye movements of around 25 people, Digital Trends reports, while they play a video game.

据Digital Trends报道,布法罗大学的一组人工智能研究人员计划研究大约25人玩电子游戏时的脑电波和眼球运动。

They’ll then use the information they glean from the gamers to build an advanced AI — so that it can then coordinate the actions of entire fleets of autonomous military robots.

然后,他们将使用从游戏玩家那里收集到的信息来构建一个先进的人工智能-这样它就可以协调整个自主军事机器人舰队的行动。

The U.S. Defense Advanced Research Projects Agency — better known as DARPA — has awarded the UB team a $316,000 grant for the study, which researcher Souma Chowdhury told Digital Trends is moving at “a pretty aggressive pace.”

美国国防高级研究计划局(U.S.Defense Advanced Research Projects Agency)-更好的名称是DARPA,已经为UB团队的这项研究提供了31.6万美元的资助。研究员Souma Chowdhury告诉Digital Trends,这项研究正在以“非常积极的步伐”进行。

The team still needs to gather the gamer data, but that shouldn’t take too long.

团队仍然需要收集玩家数据,但这不会花太长时间。

The researchers have already built a real-time strategy game for the study, with a round of the game taking about five to 10 minutes to complete.

研究人员已经为这项研究建立了一个实时策略游戏,一轮游戏大约需要5到10分钟才能完成。

If each of the gamers plays six or seven games, Chowdhury expects the team will have enough data to train its AI.

如果每个玩家都玩六到七场比赛,Chowdhury预计球队将有足够的数据来训练它的AI。

Ultimately, the researchers hope to end up with an AI that can guide the actions of groups of 250 robots on the ground and in the air, giving the fleet the ability to autonomously navigate unpredictable environments.

最终,研究人员希望最终得到一种人工智能,可以指导地面和空中250个机器人群体的行动,使舰队能够自主地在不可预测的环境中穿梭。

“Humans can come up with very unique strategies that an AI might not ever learn,” Chowdhury told Digital Trends.

“人类可以想出非常独特的策略,人工智能可能永远也学不会,”Chowdhury告诉Digital Trends。

“A lot of the hype we see in AI are in applications that are relatively deterministic environments. But in terms of contextual reasoning in a real environment to get stuff done? That’s still at a nascent stage.”

“我们在人工智能中看到的很多炒作是在相对确定性的环境中的应用程序。但是在真实的环境中进行上下文推理来完成事情呢?这还处于萌芽阶段。“