Google’s artificial intelligence arm has made a breakthrough in the development of thinking computers by creating a learning machine that combines a “neural network” computing system with conventional computer memory.谷歌(Google)的人工智能部门在研发思维...
Google’s artificial intelligence arm has made a breakthrough in the development of thinking computers by creating a learning machine that combines a “neural network” computing system with conventional computer memory.谷歌(Google)的人工智能部门在研发思维计算机方面获得一项突破，他们建构了一台融合“神经网络”计算出来系统与常规计算机内存的自学机器。Scientists at DeepMind, the tech group’s London-based AI unit, have built a “differentiable neural computer”, or DNC, that for the first time can solve small-scale problems without prior knowledge, such as planning the best route between distant stations on the London Underground or working out relationships between relatives on family trees.这家高科技集团设于伦敦的人工智能部门DeepMind的科学家们，打造出了一台“可微分神经计算机”(DNC)，首次需要在没先验科学知识的情况下解决问题各种小规模问题，比如在两个距离很远的伦敦地铁车站之间规划最佳路线，或者厘清家谱上亲属之间的关系。Neural networks — connected systems modelled on biological networks such as the brain — have played a big role in the recent and rapid progress in AI research. They are excellent at deducing patterns, for example, to enable speech recognition in digital assistants such as Google Voice or Apple’s Siri. But until now they have only been able to access the data contained within their own network. In the journal Nature the 20-strong DeepMind team said the DNC provides neural networks with access to previously incompatible external data, such as text encoded in conventional digital form.神经网络——以大脑这样的生物网络为蓝本打造出的点对点系统——在近期人工智能研究的较慢进展中起着了相当大的起到。
20人的DeepMind团队在《大自然》(Nature)期刊公开发表的论文中回应，DNC获取了神经网络，可以采访之前不相容的外部数据，比如以常规数字格式编码的文本。“The trouble is that the memory in a neural network is bound up within the computation itself, which makes it rather fragile and hard to scale up,” said Alex Graves, head of the DNC project. “We decided that the way to make it more robust is to separate out the memory, so that we can expand it without affecting the processor.”“困难在于，神经网络中的记忆被初始化在计算出来内部，这使得它非常薄弱，无法拓展，”DNC项目负责人亚历克斯格雷夫斯(Alex Graves)回应。“我们得出结论，使其更加强劲的方法是分离出来记忆，以便我们可以拓展它，而会影响处理器。
”Jay McClelland, director of Stanford University’s Centre for Mind, Brain and Computation, called the DeepMind paper “a very interesting and important milestone in AI research”.斯坦福大学(Stanford University)心智、脑和计算中心(Center for Mind, Brain and Computation)主任杰伊麦克利兰(Jay McClelland)称之为，DeepMind的这篇论文是“人工智能研究中十分有意思的最重要里程碑”。However, to make the DNC more useful in the real world than existing AI systems, it will need to be expanded to access far larger memories. “That will require a lot of engineering work,” said Mr Graves. “This is a research paper and I don’t want to speculate too much about where this is going in terms of practical problems.”然而，为了使DNC在现实世界中比现有的人工智能系统更加简单，它将必须拓展，以采访小得多的存储器。“这将必须大量的工程工作，”格雷夫斯说道。
“这是一篇研究论文，我想过分推断这对解决问题实际问题有多大指导意义。”Even so, independent computer scientists who reviewed the paper before publication said the range of applications for a general purpose DNC could be vast. Possible applications might include generating video commentaries and extracting meaning from text.即使如此，在公开发表之前评审了这篇论文的独立国家计算机科学家回应，一般用途DNC的应用于范围有可能十分极大。
潜在的应用于有可能还包括分解视频新闻报导和从文本中萃取涵义。DeepMind was founded in London as an AI start-up in 2010 and acquired by Google for ￡400m in 2014.DeepMind于2010年在伦敦正式成立，是一家人工智能初创企业，2014年被谷歌以4亿英镑并购。
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