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Language learning
自然语言学习
Facebook’s New York office is a three-minute stroll up Broadway from LeCun’s office at NYU, on two floors of a building constructed as a department store in the early 20th century. Workers are packed more densely into the open plan than they are at Facebook’s headquarters in Menlo Park, California, but they can still be seen gliding on articulated skateboards past notices for weekly beer pong. Almost half of LeCun’s team of leading AI researchers works here, with the rest at Facebook’s California campus or an office in Paris. Many of them are trying to make neural networks better at understanding language. “I’ve hired all the people working on this that I could,” says LeCun.
从LeCun在纽约大学的办公室沿着百老汇往上走三分钟，就到了Facebook的纽约办公室，这栋建筑在20世纪初建成，是一个百货商店，办公室就在建筑的二楼。工作人员在开敞布置里挤在一起，比Facebook在加利福尼亚Menlo公园的总部要拥挤，但仍然可以看到他们穿着溜冰鞋滑过每周啤酒聚会的告示。LeCun团队的几乎半数主要人工智能研究者都在这里工作，剩下的在Facebook加利福尼亚园区或在巴黎的办公室。他们中很多人都在努力使神经网络更好的理解自然语言。LeCun说：“我已经雇佣了所有在方面有所工作的人。”
A neural network can “learn” words by spooling through text and calculating how each word it encounters could have been predicted from the words before or after it. By doing this, the software learns to represent every word as a vector that indicates its relationship to other words—a process that uncannily captures concepts in language. The difference between the vectors for “king” and “queen” is the same as for “husband” and “wife,” for example. The vectors for “paper” and “cardboard” are close together, and those for “large” and “big” are even closer.
神经网络“学习”语言的方式是扫描这些词语，计算遇到的每个词语如何通过前面或后面的文字预测出来。这样，软件将每个词语都表示成为一个向量，代表与其他词语的关系，这是神秘的在语言中捕获概念的过程。比如，“国王”与“王后”的向量之间的差别和“丈夫”与“妻子”之间的差别是一样的。“纸张”与“硬纸板”的向量应当是很类似的，“large”与“big”的向量应当也是一样的。
The same approach works for whole sentences (Hinton says it generates “thought vectors”), and Google is looking at using it to bolster its automatic translation service. A recent paper from researchers at a Chinese university and Microsoft’s Beijing lab used a version of the vector technique to make software that beats some humans on IQ-test questions requiring an understanding of synonyms, antonyms, and analogies.
对于整个句子，有相同的方法（Hinton说这产生“思想向量”），Google希望能将其用于支持自动翻译服务上。最近一所中国大学和微软北京研究院有一篇文章，用了这种向量技术制作了软件，在一个需要理解同义词、反义词和类比的IQ测试里，击败了一些人类参与者。
LeCun’s group is working on going further. “Language in itself is not that complicated,” he says. “What’s complicated is having a deep understanding of language and the world that gives you common sense. That’s what we’re really interested in building into machines.” LeCun means common sense as Aristotle used the term: the ability to understand basic physical reality. He wants a computer to grasp that the sentence “Yann picked up the bottle and walked out of the room” means the bottle left with him. Facebook’s researchers have invented a deep-learning system called a memory network that displays what may be the early stirrings of common sense.
LeCun小组的工作计划更加长远。他说：“语言本身没有那么复杂，复杂的是对语言和整个世界要有深入的理解，这会让你拥有常识。这是我们真正感兴趣的，可以集成到机器里面去。”LeCun的常识的意思就像亚里士多德所指的这个词语的意思：理解基本物质现实的能力。他希望一台计算机在理解这个句子“Yann拿起瓶子，走出房间”时，能知道这个瓶子跟着他也出了房间。Facebook的研究者已经创造了一个深度学习系统，称为记忆网络，这可能是常识的早起萌芽。
A memory network is a neural network with a memory bank bolted on to store facts it has learned so they don’t get washed away every time it takes in fresh data. The Facebook AI lab has created versions that can answer simple common-sense questions about text they have never seen before. For example, when researchers gave a memory network a very simplified summary of the plot of Lord of the Rings, it could answer questions such as “Where is the ring?” and “Where was Frodo before Mount Doom?” It could interpret the simple world described in the text despite having never previously encountered many of the names or objects, such as “Frodo” or “ring.”
记忆网络是一个神经网络，附带一个记忆库存，用来存储学习到的事实，这样当每次新数据来的时候，不会被冲刷掉。Facebook的人工智能实验室已经开发了几个版本，已经可以回答一些简单的常识问题，这些文字是它们从来没有看到的。比如，当研究者给记忆网络一个非常简化版本的《魔戒》的剧情，它可以回答像“戒指在哪里？”和“Mount Doom之前Frodo在哪里”这样的问题。它可以解释文字里描述的简单的世界，虽然之前从来没有遇到过这些名字和物体，比如“Frodo”或“戒指”。
The software learned its rudimentary common sense by being shown how to answer questions about a simple text in which characters do things in a series of rooms, such as “Fred moved to the bedroom and Joe went to the kitchen.” But LeCun wants to expose the software to texts that are far better at capturing the complexity of life and the things a virtual assistant might need to do. A virtual concierge called Money-penny that Facebook is expected to release could be one source of that data. The assistant is said to be powered by a team of human operators who will help people do things like make restaurant reservations. LeCun’s team could have a memory network watch over Moneypenny’s shoulder before eventually letting it learn by interacting with humans for itself.
软件是怎样学到这些基本常识的呢？之前会给出如何回答简单问题的示例，给出的简单文本中要有角色在一系列空间中做事情的句子，比如“Frodo移到了卧室，Joe到了厨房”。但LeCun希望软件接收的文本要复杂的多，比描述生活的复杂性还要复杂，或是接收一个虚拟助理需要做的事。Facebook希望推出的一个虚拟看门人，叫Money-penny，可以是这种数据的一个来源。这个助理由一个团队进行维护，可以帮人做一些事，比如订餐。LeCun团队可以有一个记忆网络接收Money-penny所做的事，当然是在让它自己与人类互动学习之前。
Building something that can hold even a basic, narrowly focused conversation still requires significant work. For example, neural networks have shown only very simple reasoning, and researchers haven’t figured out how they might be taught to make plans, says LeCun. But results from the work that has been done with the technology so far leave him confident about where things are going. “The revolution is on the way,” he says.
做出能够进行基本的话题很小的对话的算法也是需要大量工作的。比如，LeCun说，神经网络只有简单的推理能力，研究者还没弄清楚怎样教网络去做计划。但已经做过的工作得到的结果让他对事情的进展感到有信心，他说：“革命正在路上。”
Some people are less sure. Deep-learning software so far has displayed only the simplest capabilities required for what we would recognize as conversation, says Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence in Seattle. The logic and planning capabilities still needed, he says, are very diferent from the things neural networks have been doing best: digesting sequences of pixels or acoustic waveforms to decide which image category or word they represent. “The problems of understanding natural language are not reducible in the same way,” he says.
一些人则没那么确定。Oren Etzioni是西雅图艾伦人工智能研究所的CEO，他说，目前的深度学习软件进行对话的能力只是最基本最简单的，仍然需要的逻辑与计划的能力，与神经网络可以做的事非常不一样：接收像素序列或语音波形来确定图像属于哪个类别，语音代表哪个字。他说：“理解自然语言的问题不能以同样的方式进行简化。”
Gary Marcus, a professor of psychology and neural science at NYU who has studied how humans learn language and recently started an artificial-intelligence company called Geometric Intelligence, thinks LeCun underestimates how hard it would be for existing software to pick up language and common sense. Training the software with large volumes of carefully annotated data is fine for getting it to sort images. But Marcus doubts it can acquire the trickier skills needed for language, where the meanings of words and complex sentences can flip depending on context. “People will look back on deep learning and say this is a really powerful technique—it’s the first time that AI became practical,” he says. “They’ll also say those things required a lot of data, and there were domains where people just never had enough.” Marcus thinks language may be one of those domains. For software to master conversation, it would need to learn more like a toddler who picks it up without explicit instruction, he suggests.
Gary Marcus是纽约大学一个心理学和神经科学的教授，研究过人类如何学习语言，最近成立了一个人工智能公司，名叫几何智能，他认为LeCun低估了现有软件学习语言和常识的难度。用大量仔细标注的数据对软件进行训练是可以对图像进行分类的。但Marcus怀疑这对于语言这种需要更复杂技巧的问题是不够的，在不同的上下文环境中，文字和复杂句子的意思可以完全不一样。他说：“人们将来回望深度学习的时候，会说这确实是很强大的技术，这是人工智能第一次变得实用，人们也会说这些东西需要大量数据，总有一些领域人们永远也不会有足够的数据。”Marcus认为语言就是这样一个领域。他认为，对于想掌握对话技巧的软件，应当更像一个蹒跚学步的孩子在没有明确指令的情况下去学习。
Deep belief
深度学习的信仰
At Facebook’s headquarters in California, the West Coast members of LeCun’s team sit close to Mark Zuckerberg and Mike Schroepfer, the company’s CTO. Facebook’s leaders know that LeCun’s group is still some way from building something you can talk to, but Schroepfer is already thinking about how to use it. The future Facebook he describes retrieves and coordinates information, like a butler you communicate with by typing or talking as you might with a human one.
在加利福尼亚的Facebook总部里，LeCun团队在西海岸的成员与扎克伯格和公司CTO Mike Schroepfer坐在一起。Facebook的领导者知道LeCun小组还正在构建可以对话的东西的过程中，但Schroepfer已经正在想如何去使用它了。他所描述的Facebook的未来能检索和整合信息，就像正在与一个管家正在交流，通过打字或谈话，并且与一个人类管家的能力应当类似。
“You can engage with a system that can really understand concepts and language at a much higher level,” says Schroepfer. He imagines being able to ask that you see a friend’s baby snapshots but not his jokes, for example. “I think in the near term a version of that is very realizable,” he says. As LeCun’s systems achieve better reasoning and planning abilities, he expects the conversation to get less one-sided. Facebook might offer up information that it thinks you’d like and ask what you thought of it. “Eventually it is like this super-intelligent helper that’s plugged in to all the information streams in the world,” says Schroepfer.
Schroepfer说：“你可以用上一个在更高层次真正理解概念和语言的系统。”比如，他设想系统当看到朋友的宝宝时会发问，而看到他的笑话时则不，他说：“我认为在近期其可行性是很高的。” 当LeCun的系统拥有了更好的推理和计划的能力时，他希望对话不要那么片面。Facebook可能会提供你可能会喜欢的信息，并问你认为怎样。Schroepfer说：“最终它会像一个超级智能的帮手，连接着世界上所有的信息流。”
The algorithms needed to power such interactions would also improve the systems Facebook uses to filter the posts and ads we see. And they could be vital to Facebook’s ambitions to become much more than just a place to socialize. As Facebook begins to host articles and video on behalf of media and entertainment companies, for example, it will need better ways for people to manage information. Virtual assistants and other spinouts from LeCun’s work could also help Facebook’s more ambitious departures from its original business, such as the Oculus group working to make virtual reality into a mass-market technology.
支持这种连接的算法肯定也会帮助Facebook系统改进帖子和广告的过滤。Facebook的志向远不止是一个进行社交的地方，这些技术对这个志向是至关重要的。比如，当Facebook开始以媒体和娱乐公司提供文章和视频时，需要以更好的方式管理信息。LeCun工作中的虚拟助理和其他方面也会帮助Facebook从最初的生意向远方航行，比如Oculus小组正在使虚拟现实成为一个巨大市场的技术。
None of this will happen if the recent impressive results meet the fate of previous big ideas in artificial intelligence. Blooms of excitement around neural networks have withered twice already. But while complaining that other companies or researchers are over-hyping their work is one of LeCun’s favorite pastimes, he says there’s enough circumstantial evidence to stand firm behind his own predictions that deep learning will deliver impressive payoffs. The technology is still providing more accuracy and power in every area of AI where it has been applied, he says. New ideas are needed about how to apply it to language processing, but the still-small field is expanding fast as companies and universities dedicate more people to it. “That will accelerate progress,” says LeCun.
如果人工智能前一个宏大思想成为了现实，那么这一切都不会发生了。关于神经网络的兴奋已经萎缩了两次了。但在抱怨其他公司过度宣传他们的工作时，他说深度学习已经像他语言那样得到了足够的回报，这项技术仍然在应用的每个领域都提供了更多的准确性和能量。需要新的想法将其应用在自然语言处理中，但这个仍然很小的领域正在快速膨胀，公司和大学都在投入更多的人，“这将会加速这个过程”，LeCun说。
It’s still not clear that deep learning can deliver anything like the information butler Facebook envisions. And even if it can, it’s hard to say how much the world really would benefit from it. But we may not have to wait long to find out. LeCun guesses that virtual helpers with a mastery of language unprecedented for software will be available in just two to five years. He expects that anyone who doubts deep learning’s ability to master language will be proved wrong even sooner. “There is the same phenomenon that we were observing just before 2012,” he says. “Things are starting to work, but the people doing more classical techniques are not convinced. Within a year or two it will be the end.”
深度学习是否能够实现Facebook所预言的信息大管家的功能现在还不是很清除，即使可以，这个世界怎样从中受益也不是很确定，但不需要多久我们就会发现结果。LeCun猜测具有语言功能的虚拟助手软件在两到五年内就会出现。他希望很快就能证明怀疑深度学习是否能掌握语言技能的人是错误的。他说：“在2012年前我们看到了相同的现象，算法正在起作用，但持传统技术观点的人还没有被说服，一两年后就会出现结果。”
Teaching Machines to Understand Us 让机器理解我们 之三 自然语言学习及深度学习的信仰
标签：bank tar think plot fas 软件 logic 管理 offer
原文地址：https://www.cnblogs.com/mycenae/p/8780269.html
