情节概要：本周接受采访时Nuance的硅谷办公室与客人查尔斯·奥尔蒂斯，人工智能和自然语言（NL）处理实验室Nuance通讯在硅谷的导演记录现场。在这一集里，奥尔蒂斯谈到他所认为的自然语言处理（NLP）在过去几年最重要的发展，有什么进步，给我们带来了我们今天的情况，并在进度可能需要NLP在今后未来几年（无论是在细微差别和超越）。
专长：AI理论与应用，机器人，大规模的信息和多智能体系统，知识表示和推理。He spent over 13 years at SRI as the Director of the AI Center’s Teambotics Program before coming to Nuance, where he has been since 2012. In addition to serving as a postdoctoral research fellow (collaborative multi-agent systems) at Harvard University, Ortiz has received his PhD in Computer and Information Science from University of Pennsylvania, his MS in Computer Science from Columbia University, and his bachelor’s degree in Physics from MIT.
介绍识别：查尔斯·奥尔蒂斯拥有超过20年以上的在人工智能研究和技术管理经验和超过50同行评审的技术出版物。
当前所属机构：Nuance的实验室主任AI和NLP
访谈要点：
（1:36）什么是被允许的语音和自然语言处理向前推动，就像我们今天所看到的行业技术荟萃的发展趋势？
查尔斯·奥尔蒂斯：In the same way that robotics became more, there was more investment in robotics, because of things like form factor reduction and processors, the costs went down, and so the universities could purchase robots that they could do research on that didn’t cost a fortune; in a similar way, the work that’s being done in NLP and AI is actually has been facilitated first in this reduction in form factor as well…and also enabled by the advances in speech understanding
由于这一点，像Siri的系统扩散的，它想当然地认为，这可能在一定程度上;我们试图在这里做的是把它的下一步，下一代个人，这些系统的
（3:30）谈论Nuance的历史一点点把球推进到现在，语音识别。
CO：我的实验室在这里已经四年左右，我们并不真正在我们的有关语音识别实验室工作......在汽车领域，推动引进私人助理进入车内，这是一个自然的域的言论和因为需要降低风险，确保驾驶者的认知负荷没有被拉伸的地步，他们不能留意交通等语言。人们希望能够有机会获得资源，在互联网上，但不能被点击手机上用你的双手的应用程序和你不能指着除了在非常有限的事情;这意味着，讲话，是在那种域通信的天然培养基，但我们需要做的，采取进一步的就是思考如何在任务和解决方案建模的任务驱动程序或用户感兴趣，所以这不只是汽车，而是我给，作为一个例子...
我们的系统有可能成为在能够理解用户想要做，不只是在回答上来讲，像刷钱“有什么温度外面？”It’s more that the user wants to do something, say reserve a table somewhere for dinner or they want to find a store that has something in particular…you have to take the natural language processing in the front and carry it forth to the backend, which is responsible for doing the reasoning about the task.
（6:36）我想你必须要经过很多AI的使用情况...并想通过什么意向的常见情况：...就是它每用户校准，它是整个谈话校准......怎么办我们更接近下一步 - 理解意图是什么？
CO：有在工作中一对夫妇推力，我们正在这样做的驾驶是什么在起作用。First, I should say that the focus is primarily on conversation and dialogue as opposed to one-shut utterances – this would be “what’s the capital of France” or “who owns the Golden State Warriors”, that’s a question you would get an answer and you’d be done with it; If I’m using a search engine and I I ask a question, the next question doesn’t have anything to do with it, that’s not a natural way to communicate with a human, language has some very useful properties and it’s very efficient, you can communicate a lot without saying much, and that’s by virtue of being able to carry around this context of a dialogue; if Im planning a trip and trying to reserve some flights to particular cities on particular days and then I switch to trying to get a hotel reservation, I don’t need do repeat where or what day, etc., and that’s a simple example
…的领域,有一个巨大的挑战,我们要更多的便携式新域名的地址通过系统的优点有很多背景常识知识,这是一个巨大的挑战在AI,不是明天的事情要解决,但我们致力于将更多这个世界的知识,这样系统可以很容易地设计为特定的域
（12:49）谈谈常识性知识......一些，这将是可能的if-then情况下，基于逻辑的，我想一定是从失败和成功梳理出...你怎么样了大约接近常识性知识的机器？
CO：我们叫它大的知识，因为它侧重于更广泛的信息，从大数据区分开来;这是一个非常具有挑战性的问题...有你可以做这三种基本方式，而我们正在采取一种混合的方法来构建这些知识基础。
一种方式是当然的手，...另一个是做众包，你有系统，每天的人可以促进...。而且，你可以学习为好，尽量从因特网上的文件学习或提取知识，例如，或一些其他来源。What we’ve done in our approach is build the first framework where we could support these three forms of knowledge of acquisition and be able to do things like deconflict between one representation and one another, because you’re going to have some cases where some of that information in one conflicts with another, or the names aren’t exactly the same – they refer to the same things, but they don’t use the exact same names for whatever reason, so we have that framework in place and then we’re making use of all 3 types of knowledge…
We’re not claiming that we’re going to have in a couple of years the big knowledge database of all common sense knowledge, but what we’re trying to do i,s in the domains of interest to the business in which Nuance is involved, we have a sufficient backbone of world knowledge, so that we can then make our system more robust
(18:21) You’re thinking a lot about the future of this technology….in terms of where you see the possibility for transformative impact in that improvement of speech technology…where do you see 2 to 5 years where this could really take off and have a grip?
CO：In any case, the main advance is going to be conversational systems, dialogue-based systems that can engage with a user in a multi-utterance interaction, because that’s the way you and I talk, and we don’t want to try to teach people to talk in a different way; being able to support a dialogue is going to be one major advance that we see, and we’re focusing our efforts there, and the other is imbuing systems with more和更多的这个世界的知识，常识性知识，这就是摹卷板机，使系统缓慢，但希望可以肯定，更强大，它是人工智能的大挑战之一...
I should mention…there’s been an effort in the last few years to come up with better measures of progress in AI, and these are in the form of alternatives to what’s called the Turing test, and one of those that we’re supporting has been promoted by the AI research organizations, the Winograd Skeema challenge (WSC) and it is meant to do two things…help measure progress in AI in this very challenging area and get more of academia involved, in terms of professors and students, in pushing the technology forward.
