This is the latest in a series of high-profile DeepMind projects, which are demonstrating a previously unanticipated ability by AI systems to learn by themselves, once their human programmers have set the basic parameters.
这是DeepMind一系列备受瞩目的项目中最新的一个,这些项目展示了一种之前未曾预料到的人工智能系统自学能力--在编程人员为其设定基本参数之后。
In October DeepMind's AlphaGo taught itself to play Go, the ultra-complex board game, far better than any human player. Last month another DeepMind AI system learned to find its way around a maze, in a way that resembled navigation by the human brain.
去年10月,DeepMind的AlphaGo自学了围棋这种超级复杂的棋类游戏,然后轻松击败了人类棋手。上个月,DeepMind的另一个人工智能系统学会了在迷宫中寻找路径,其方式类似于人类大脑的导航功能。
Future GQN systems promise to be more versatile and to require less processing power than today's computer vision techniques, which are trained with large data sets of annotated images produced by humans.
未来的GQN系统有望比今天的计算机视觉技术的功能更为强大,所需的处理能力也会更低。目前的计算机视觉技术是用由人类生成的大量带标注的图像数据集来训练的。
【DeepMind已开发具有三维想象力的视觉计算机】相关文章:
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