DeepMind, Google's artificial intelligence subsidiary in London, has developed a self-training vision computer that generates "a full 3D model of a scene from just a handful of 2D snapshots", according to its chief executive.

位于伦敦的谷歌人工智能子公司DeepMind，近日开发了一款自我训练的视觉计算机。据其首席执行官介绍，这款计算机“仅利用几张2D快照就能生成一个完整的3D场景模型”。

The system, called the Generative Query¹ Network, can then imagine and render the scene from any angle, said Demis Hassabis.

杰米斯·哈萨比斯表示，这套被称为“生成式查询网络”的系统可以从任何角度想象和呈现场景。

GQN is a general-purpose system with a vast range of potential applications, from robotic vision to virtual reality simulation.

GQN是一个通用系统，具有从机器人视觉到虚拟现实模拟的广泛的应用潜力。

"Remarkably², the DeepMind scientists developed a system that relies only on inputs³ from its own image sensors⁴ -- and that learns autonomously⁵ and without human supervision," said Matthias Zwicker, a computer scientist at the University of Maryland.

马里兰大学的计算机科学家马蒂亚斯·茨威格称：“值得一提的是，DeepMind的科学家开发了只依赖自身图像传感器所输入信息，就可以自主学习的系统，且无需人类监督。”

This is the latest in a series of high-profile DeepMind projects, which are demonstrating a previously⁶ unanticipated ability by AI systems to learn by themselves, once their human programmers have set the basic parameters⁷.

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 maze⁸, 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 versatile⁹ and to require less processing power than today's computer vision techniques, which are trained with large data sets of annotated¹⁰ 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。