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亚慱足彩体育app-DeepMind已开发具有三维想象力的视觉计算机

 


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本文摘要:DeepMind, Googles artificial intelligence subsidiary in London, has developed a self-training vision computer that generates a full 三维 model of a scene from just a handful of 3D snapshots, according to its chief executive.位于纽约的Google人工智能分公司DeepMind,前不久产品研发了一款自身训炼的视觉效果计算机。

DeepMind, Googles artificial intelligence subsidiary in London, has developed a self-training vision computer that generates a full 三维 model of a scene from just a handful of 3D snapshots, according to its chief executive.位于纽约的Google人工智能分公司DeepMind,前不久产品研发了一款自身训炼的视觉效果计算机。据其CEO解读,这款计算机“仅有运用多张3D快照更新就能溶解一个初始的三维场景模型”。The system, called the Generative Query Network, can then imagine and render the scene from any angle, said Demis Hassabis.杰伊斯·哈萨比斯答复,这套称之为“生成式搜索互联网”的系统能够从一切视角想像和展现出情景。

GQN is a general-purpose system with a vast range of potential applications, from robotic vision to virtual reality simulation.GQN是一个规范化系统,具有从机器人视觉到虚拟现实技术模拟仿真的广泛的运用于发展潜力。Remarkably, the DeepMind scientists developed a system that relies only on inputs from its own image sensors -- and that learns autonomously and without human supervision, said Matthias Zwicker, a computer scientist at the University of Maryland.马里兰大学的计算机生物学家马蒂亚斯·茨威格称作:“值得一提的是,DeepMind的生物学家产品研发了只仰仗本身光学镜头所輸出信息内容,就可以自我约束通过自学的系统,且必须人类监管。”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.它是DeepMind一系列备受关注的新项目中最近的一个,这种新项目展览了一种以前未曾想到的人工智能系统通过自学能力--在软件程序员为其原著主要参数以后。

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In October DeepMinds 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.上年十月,DeepMind的AlphaGo通过自学了棋士这类超级简单的益智游戏,随后精彩纷呈击败了人类象棋大师。上月,DeepMind的另一个人工智能系统学会了在谜宫中寻找途径,其方法类似人类人的大脑的导航栏系统作用。Future GQN systems promise to be more versatile and to require less processing power than todays computer vision techniques, which are trained with large data sets of annotated images produced by humans.将来的GQN系统将来可能比今日的计算机视觉技术性的作用更为强悍,需要的处理能力也不会更为较低。

现阶段的计算机视觉技术性是用由人类溶解的很多携带标识的图象数据来训炼的。


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