Latest Headline News: Samsung acquires Viv, a next-gen AI assistant built by the creators of Apple's Siri . Wei: Some people are just smart, or shrewd, more than we can imagine. I am talking about Fathers of Siri, who have been so successful with their technology that they managed to sell the same type of technology twice, both at astronomical prices, and both to the giants in the mobile and IT industry. What is more amazing is, the companies they sold their tech-assets to are direct competitors. How did that happen? How nice this world is, to a really really smart technologist with sharp business in mind. What is more stunning is the fact that, Siri and the like so far are regarded more as toys than must-carry tools, intended at least for now to satisfy more curiosity than to meet the rigid demand of the market. The most surprising is that the technology behind Siri is not unreachable rocket science by nature, similar technology and a similar level of performance are starting to surface from numerous teams or companies, big or small. I am a tech guy myself, loving gadgets, always watching for new technology breakthrough. To my mind, something in the world is sheer amazing, taking us in awe, for example, the wonder of smartphones when the iPhone first came out. But some other things in the tech world do not make us admire or wonder that much, although they may have left a deep footprint in history. For example, the question answering machine made by IBM Watson Lab in winning Jeopardy. They made it into the computer history exhibition as a major AI milestone. More recently, the iPhone Siri, which Apple managed to put into hands of millions of people first time for seemingly live man-machine interaction. Beyond that accomplishment, there is no magic or miracle that surprises me. I have the feel of seeing through these tools, both the IBM answering robot type depending on big data and Apple's intelligent agent Siri depending on domain apps (plus a flavor of AI chatbot tricks). Chek: @ Wei I bet the experts in rocket technology will not be impressed that much by SpaceX either, Wei: Right, this is because we are in the same field, what appears magical to the outside world can hardly win an insider's heart, who might think that given a chance, they could do the same trick or better. The Watson answering system can well be regarded as a milestone in engineering for massive, parallel big data processing, not striking us as an AI breakthrough. what shines in terms of engineering accomplishment is that all this happened before the big data age when all the infrastructures for indexing, storing and retrieving big data in the cloud are widely adopted. In this regard, IBM is indeed the first to run ahead of the trend, with the ability to put a farm of servers in working for the QA engine to be deployed onto massive data. But from true AI perspective, neither the Watson robot nor the Siri assistant can be compared with the more-recent launch of the new Google Translate based on neural networks. So far I have tested using this monster to help translate three Chinese blogs of mine (including this one in making), I have to say that I have been thrown away by what I see . As a seasoned NLP practitioner who started MT training 30 years ago, I am still in disbelief before this wonder of the technology showcase. Chen: wow, how so? Wei: What can I say? It has exceeded my imagination limit for all my dreams of what MT can be and should be since I entered this field many years ago. While testing, I only needed to do limited post-editing to make the following Chinese blogs of mine presentable and readable in English, a language with no kinship whatsoever with the source language Chinese. Question answering of the past and present Introduction to NLP Architecture Hong: Wei seemed frightened by his own shadow.Chen: Chen: The effect is that impressive? Wei: Yes. Before the deep neural-nerve age, I also tested and tried to use SMT for the same job, having tried both Google Translate and Baidu MT, there is just no comparison with this new launch based on technology breakthrough. If you hit their sweet spot, if your data to translate are close to the data they have trained the system on, Google Translate can save you at least 80% of the manual work. 80% of the time, it comes so smooth that there is hardly a need for post-editing. There are errors or crazy things going on less than 20% of the translated crap, but who cares? I can focus on that part and get my work done way more efficiently than before. The most important thing is, SMT before deep learning rendered a text hardly readable no matter how good a temper I have. It was unbearable to work with. Now with this breakthrough in training the model based on sentence instead of words and phrase, the translation magically sounds fairly fluent now. It is said that they are good a news genre, IT and technology articles, which they have abundant training data. The legal domain is said to be good too. Other domains, spoken language, online chats, literary works, etc., remain a challenge to them as there does not seem to have sufficient data available yet. Chen: Yes, it all depends on how large and good the bilingual corpora are. Wei: That is true. SMT stands on the shoulder of thousands of professional translators and their works. An ordinary individual's head simply has no way in digesting this much linguistic and translation knowledge to compete with a machine in efficiency and consistency, eventually in quality as well. Chen: Google's major contribution is to explore and exploit the existence of huge human knowledge, including search, anchor text is the core. Ma: I very much admire IBM's Watson, and I would not dare to think it possible to make such an answering robot back in 2007. Wei: But the underlying algorithm does not strike as a breakthrough. They were lucky in targeting the mass media Jeopardy TV show to hit the world. The Jeopardy quiz is, in essence, to push human brain's memory to its extreme, it is largely a memorization test, not a true intelligence test by nature. For memorization, a human has no way in competing with a machine, not even close. The vast majority of quiz questions are so-called factoid questions in the QA area, asking about things like who did what when and where , a very tractable task. Factoid QA depends mainly on Named Entity technology which was mature long ago, coupled with the tractable task of question parsing for identifying its asking point, and the backend support from IR, a well studied and practised area for over 2 decades now. Another benefit in this task is that most knowledge questions asked in the test involve standard answers with huge redundancy in the text archive expressed in various ways of expressions, some of which are bound to correspond to the way question is asked closely. All these factors contribute to IBM's huge success in its almost mesmerizing performance in the historical event. The bottom line is, shortly after the 1999 open domain QA was officially born with the first TREC QA track, the technology from the core engine has been researched well and verified for factoid questions given a large corpus as a knowledge source. The rest is just how to operate such a project in a big engineering platform and how to fine-tune it to adapt to the Jeopardy-style scenario for best effects in the competition. Really no magic whatsoever. Google Translated from 【泥沙龙笔记：从三星购买Siri之父的二次创业技术谈起】 , with post-editing by the author himself. 【Related】 Question answering of the past and present Introduction to NLP Architecture Newest GNMT: time to witness the miracle of Google Translate Dr Li’s NLP Blog in English
http://osxdaily.com/2012/09/20/enable-siri-ipad/ How to Enable Siri on iPad 3 Siri has made it’s way onto iPad 3 thanks to iOS 6 and it’s actually one of the best reasons to upgrade for 3rd gen iPad owners. Though you should see the option to enable Siri during the first reboot and basic setup after updating to iOS 6 or getting a brand new iPad, if you somehow skipped it or didn’t see that option, here’s all you need to do to get Siri on the iPad: Open Settings and tap “General” Find “Siri” and flip the switch to “ON”, make any changes to Voice Feedback, Language, and your identity as necessary Close out of Settings and Siri is ready to go With Siri enabled, hold down the Home button for about 2 seconds to activate Siri and start asking questions, request information, and even launch apps. The voice recognition aspect is just like Dictation in iOS and OS X , but with the responses it’s obvious Siri has undergone some major improvements behind the scenes, and the ability to answer even some obscure questions has dramatically improved. Sports fans will find the new sports features a welcome change too, letting you easily get game schedules, rankings, stats, and much more, perfect for couch lounging on Saturdays and Sundays.
话说这苹果真是能折腾，一个技术课题硬是折腾成大众话题，弄得满世界都在谈论苹果爱疯的贴身小蜜 “死日”（Siri，没追踪来源，但瞧这名字起的），说是她无所不能，能听得懂主人的心思，自动打理各项事务，从天气预报，到提供股票信息，甚至做笔记。不服不行，人家就是把这个科幻世界的机器人功能产品化了，挑起了大众的好奇心。虽然毁誉参半，批评者与追星者一样多，还是为语言技术扬了名。这不，圣诞节到了，调查表明，美国青少年最喜欢的圣诞礼品有三：（1）礼物券，也就是钱，爱怎么花自己定当然好；（2）时装（爱美之心）；（3）苹果产品（因为那是时髦的代名词）。 前些时候，与朋友谈到死日，我说它有三大来源：首先是语言技术，包括语音识别和文句分析。语音识别做了很多年了，据说技术相当成熟可用了（语音虽然是我的近邻了，但隔行如隔山，我就不评论了）。文句分析（这可是我的老本行）当然有难度，但是因为死日是目标制导，即从目标app反推自然语言的问句表达法，所以分析难度大为降低，基本上是 tractable 的（见《立委随笔： 非常折服苹果的技术转化能力 》）。第二个来源是当年 AskJeeves 借以扬名的 million-dollar idea (见《 【 IT风云掌故：金点子起家的 AskJeeves 】 》)，巧妙运用预知的问题模板，用粗浅的文句分析技术对应上去，反问用户，从而做到不变应万变，克服机器理解的困难。最近有人问死日：Where can I park the car? 死日就反问道：you asked about park as in a public park, or parking for your vehicle? 虽然问句表明了这位贴身小蜜是绣花枕头，徒有其表，理解能力很有限，但是对于主人（用户）来说，在两个选项中肯定一个不过是举“口”之劳的事情。第三个来源就是所谓聊天系统，网上有不少类似的玩具（见 【 立委科普 ： 问答系统的前生今世 】 第一部分 ) ，他是当年面临绝路的老 AI 留下的两大遗产之一（另一个遗产是所谓专家系统）。 最近摆弄汉语自动分析，有老友批评得很到位： Quote 俺斗胆评论一下，您的系统长项应该在于自然 语言理解 至于语法树，应该是小儿科。韩愈说“句读之不知,惑 之不解”。 语法树的作用在于“知句读”，而您的系统应该强调“解惑”。 俺感觉照现在的发展速度，一个能够真正通过图灵检验的系统应该离我们不远了。虽然现在已经有系统号称能通过，但是都是聊天系统，干的本身就是不着调的工作。离真正意义的图灵检验还有距离。 是小儿科，可是很多人弄不了这小儿科呢。 日期: 12/05/2011 13:41:30 从high level看，从100年后看，说小儿科也差不多。 但是你所谓的解惑，离开现实太远。 一般来说，机器擅长分析、抽取和挖掘，上升到预测和解惑还有很长的路，除非预测是挖掘的简单延伸，解惑就是回答黑白分明的问题。 聊天系统，干的本身就是不着调的工作，一点儿不错，那是所谓 old AI 的残余。不过，即便如此，我在 苹果 Siri 中看到的三个来源（1.自然语言技术：语音和文字 2 Askjeeves 模板技术；3. 所谓 AI 聊天系统）中也看到了它的影子，它是有实用价值的，价值在于制造没有理解下的 人工智能 的假象。 昨天甜甜秀给我看：Dad, somebody asked Siri: what are you wearing? Guess how he replies? 这种 trick，即便知道是假的，也让人感觉到设计者的一份幽默。 那天在苹果iPhone4s展示会上，临结束全场哄堂大笑，原来苹果经理最后问了一个问题：Who are you? Siri 扭着细声答道： I am your humble assistant. 面对难以实现的人工智能，来点儿幽默似的假的人工智能，也是一种智慧。 相关篇什： 《 立委随笔：非常折服苹果的技术转化能力 。。。》 《 从新版iPhone发布，看苹果和微软技术转化能力的天壤之别 》 科学网—【 立委科普 ： 问答系统的前生今世 】 科学网—《立委随笔：人工“智能”》 【置顶：立委科学网博客NLP博文一览（定期更新版）】
标签: siri
