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DeepMind已开发具有三维想象力的视觉计算机“亚博体育app官网下载”

作者:博亚体育app下载   发布时间:2021-09-29   点击量:

本文摘要:DeepMind, Googles 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.坐落于伦敦的谷歌人工智能子公司DeepMind,近日研发了一款自我训练的视觉计算机。

DeepMind, Googles 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.坐落于伦敦的谷歌人工智能子公司DeepMind,近日研发了一款自我训练的视觉计算机。据其首席执行官讲解,这款计算机“仅有利用几张2D快照就能分解一个原始的3D场景模型”。The system, called the Generative Query Network, can then imagine and render the scene from any angle, said Demis Hassabis.杰米斯·哈萨比斯回应,这套被称作“生成式查找网络”的系统可以从任何角度想象和呈现出场景。

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GQN is a general-purpose system with a vast range of potential applications, from robotic vision to virtual reality simulation.GQN是一个标准化系统,具备从机器人视觉到虚拟现实仿真的普遍的应用于潜力。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科学家马蒂亚斯·茨威格称之为:“值得一提的是,DeepMind的科学家研发了只倚赖自身图像传感器所输出信息,就可以自律自学的系统,且需要人类监督。”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近期的一个,这些项目展出了一种之前不曾预料到的人工智能系统自学能力--在编程人员为其原作基本参数之后。

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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.去年10月,DeepMind的AlphaGo自学了棋士这种超级简单的棋类游戏,然后精彩打败了人类棋手。上个月,DeepMind的另一个人工智能系统学会了在迷宫中找寻路径,其方式类似于人类大脑的导航系统功能。

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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.未来的GQN系统未来将会比今天的计算机视觉技术的功能更加强劲,所需的处置能力也不会更加较低。目前的计算机视觉技术是用由人类分解的大量带上标示的图像数据集来训练的。


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