DeepMind已開發(fā)具有三維想象力的視覺(jué)計(jì)算機(jī)

Deep Mind, 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.

位于倫敦的谷歌人工智能子公司DeepMind，近日開發(fā)了一款自我訓(xùn)練的視覺(jué)計(jì)算機(jī)。據(jù)其首席執(zhí)行官介紹，這款計(jì)算機(jī)“僅利用幾張2D快照就能生成一個(gè)完整的3D場(chǎng)景模型”。

The system, called the Generative Query Network, can then imagine and render the scene from anyangle, said Demis Hassabis.

杰米斯·哈薩比斯表示，這套被稱為“生成式查詢網(wǎng)絡(luò)”的系統(tǒng)可以從任何角度想象和呈現(xiàn)場(chǎng)景。

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

GQN是一個(gè)通用系統(tǒng)，具有從機(jī)器人視覺(jué)到虛擬現(xiàn)實(shí)模擬的廣泛的應(yīng)用潛力。

"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.

馬里蘭大學(xué)的計(jì)算機(jī)科學(xué)家馬蒂亞斯·茨威格稱：“值得一提的是，DeepMind的科學(xué)家開發(fā)了只依賴自身圖像傳感器所輸入信息，就可以自主學(xué)習(xí)的系統(tǒng)，且無(wú)需人類監(jiān)督。”

 

 

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

這是DeepMind一系列備受矚目的項(xiàng)目中最新的一個(gè)，這些項(xiàng)目展示了一種之前未曾預(yù)料到的人工智能系統(tǒng)自學(xué)能力--在編程人員為其設(shè)定基本參數(shù)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 Deep Mind 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自學(xué)了圍棋這種超級(jí)復(fù)雜的棋類游戲，然后輕松擊敗了人類棋手。上個(gè)月，DeepMind的另一個(gè)人工智能系統(tǒng)學(xué)會(huì)了在迷宮中尋找路徑，其方式類似于人類大腦的導(dǎo)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 an notated images produced by humans.

未來(lái)的GQN系統(tǒng)有望比今天的計(jì)算機(jī)視覺(jué)技術(shù)的功能更為強(qiáng)大，所需的處理能力也會(huì)更低。目前的計(jì)算機(jī)視覺(jué)技術(shù)是用由人類生成的大量帶標(biāo)注的圖像數(shù)據(jù)集來(lái)訓(xùn)練的。