AIcannowlearnfromitsownmemoryindependentlyTheDeepMindartificialintelligence(AI)beingdevelopedbyGoogle'sparentcompany，Alphabet，cannowintelligentlybuildonwhat'salreadyinsideitsmemory，thesystem’s programmers have announced.谷歌母公司Alphabet正在开发的人工智能系统Deep Mind的工程师宣布，Deep Mind现在已经能够根据记忆自主学习。 Their new hybrid system – called a Differential Neural Computer (DNC) –pairs a neural network with the vast data storage of conventional computers， and the AI is smart enough to navigate and learn from this external data bank.他们新的混合系统可微神经计算机（DNC）将一个神经网络与传统电脑的海量存储数据结合，系统的智能已经可以自主分析并学习外部的存储数据。What the DNC is doing is effectively combining external memory (like the external hard drive where all your photos get stored) with the neural network approach of AI， where a massive number of interconnected nodes work dynamically to simulate a brain.DNC能够有效结合外部记忆存储（如同你存储照片的硬盘）和AI的神经网络，而神经网络中有大量相互联系的节点正在强有力地模仿着大脑的工作。'These models... can learn from examples like neural networks， but they can also store complex data like computers，' write Deep Mind researchers Alexander Graves and Greg Wayne in a blog post . “这些模型既能像神经网络通过样本进行学习，又可以像电脑一样存储复杂数据，”Deep Mind的研究人员Alexander Graves和Greg Wayne在博客上写道。 At the heart of the DNC is a controller that constantly optimises its responses， comparing its results with the desired and correct ones. Overtime， it's able to get more and more accurate， figuring out how to use its memory data banks at the same time.DNC的核心是一个能够将最需要的结果和仅仅正确的结果相比较，使反馈达到最优水平的控制器。长此以往，它就能够让自己的记忆储存同时工作，使工作变得更加精确。Take a family tree: after being told about certain relationships， the DNC was able to figure out other family connections on its own– writing， rewriting， and optimising its memory along the way to pull out the correct information at the right time.就拿家谱为例，DNC在被告知部分亲属关系的时候，它就能通过自主进行的编写、再编写推导出其他亲属关系，并且得出正确结果的过程中，在限定时间内将记忆最优化。Another example the researchers give is a public transit system， like the London Underground. Once it’s learned the basics， the DNC can figure out more complex relationships and routes without any extra help， relying on what it's already got in its memory banks.研究人员给出的另一个例子是公共交通系统，比如伦敦地铁。在得到地铁线路的基本线路之后，DNC可以在不用外界帮助的情况下，仅仅根据自己的记忆存储，推算出更复杂的站点关系和行程线路。In other words， it's functioning like a human brain， taking data from memory (like tube station positions) and figuring out new information (like how many stops to stay on for).换句话说，DNC就如同人类大脑一样运转，在从记忆中读取信息后推算出新的信息。Of course， any smartphone mapping app can tell you the quickest way from one tube station to another， but the difference is that the DNC isn't pulling this information out of a pre-programmed timetable – it's working out the information on its own， and juggling a lot of data in its memory all at once.当然，任何一款手机导航软件都可以推算出地铁站点之间的最快线路。但是DNC和这些软件之间的区别是，DNC并不是从预设的时间表当中得到的信息，而是直接通过自身的运算得到，处理自己的记忆存储内容都只要一瞬间！The approach means a DNC system could take what it learned about the London Underground and apply parts of its knowledge to another transport network， like the New York subway.这意味着DNC系统可以将它所了解到的伦敦地铁的部分知识应用到另一个运输网络，比如纽约地铁。The system points to a future where artificial intelligence could answer questions on new topics， by deducing responses from prior experiences， without needing to have learned every possible answer beforehand.Deep Mind展现了人工智能的未来——无需事先录入所有可能的答案，通过基于先前经验的演绎推断，就能回答关于新话题的问题。Of course， that's how Deep Mind was able to beat human champions at Go – by studying millions of Go moves. But by adding external memory， DNCs are able to take on much more complex tasks and work out better overall strategies， its creators say.当然，这也是Deep Mind之所以能够击败人类围棋冠军的原因——通过研究数以百万计的着数。它的创造者提到，要是添加外部存储，DNC还能够承担更加复杂的任务，制定更好的策略。'Like a conventional computer， [a DNC] can use its memory to represent and manipulate complex data structures， but， like a neural network， it can learn to do so from data，' the researchers explain in Nature .“就像一台传统计算机，（DNC）可以使用他的内存来表示并处理复杂的数据结构，但是，就像一个神经网络一样，它可以学会基于数据来这么做。”研究人员在《自然》杂志上这样解释道。 In another test， the DNC was given two bits of information: 'John is in the playground，' and 'John picked up the football.' With those known facts， when asked 'Where is the football?'， it was able to answer correctly by combining memory with deep learning.(The football is in the playground， if you're stuck.)在另一场测试中，DNC被导入了两条信息：“约翰在操场上”和“约翰拿起足球”。在这些已知事实的基础上，当被问及“足球在哪里”时，它能够准确地结合它的存储与深度学习来回答。（足球在操场上，如果你糊涂了的话。）Making those connections might seem like a simple task for our powerful human brains， but until now， it's been a lot harder for virtual assistants， such as Siri， to figure out.对于我们强劲的人类大脑来说，建立这些联系可能只是一个简单的任务，但目前为止，对于如Siri这样的虚拟助手来说这还是要困难很多。With the advances Deep Mind is making， the researchers say we're another step forward to producing a computer that can reason independently.研究人员标示，Deep Mind所取得的这些进步意味着我们向制造可以独立思考的电脑又迈进了一步。And then we can all start enjoying our robot-driven utopia – or technological dystopia – depending on your point of view.也许我们都将可以开始享受我们由机器人所驱动的乌托邦了——也可能是技术地狱——这取决于你的观点。 See U Next Wed.责编：陈熠文 周俊马采编：严馨旖 钱鲲鹏 王瑶琦美编：张梦瑄 王晨奕微信号：engage_CUCTVS