The Semantic Web In Practice: Opportunities And Limitations: Fill & Download for Free

Not Your Dad’s Voice Recognition SystemIt is perhaps easy to discount Siri as just another Voice recognition application, albeit a rather good one. Siri it turns out is far more than this. It is far more than the Artificial Intelligence infrastructures that are dynamically used. It is also far more than the continual learning and contextual awareness systems. Siri is all this and something that could only be held to the definition of true synergy, e.g.: “Two or more things functioning together to produce a result not independently obtainable”. None of the individual parts are "new" but the combination Siri created has never really been seen before.It has been the Holy Grail of computer researchers to one day create a device that could become conversational and intelligent in such a way that it would appear that the dialog is human generated.We have all pretty much experienced the rather funny hit or miss solutions from most Voice recognition systems. Thus far there has not been the convergence of technology and the synergy it would produce until just the last few years. Siri is a byproduct of this.DARPA Helps Invent The Internet And Helps Invent SiriWith Siri, Apple is using the results of over 40 years of research funded by DARPA (http://www.darpa.mil/ Contract numbers FA8750-07-D-0185/0004) via SRI International’s Artificial Intelligence Center (http://www.ai.sri.com/ Siri Inc. was a spin off of SRI Intentional) through the Personalized Assistant That Learns Program (PAL, https://pal.sri.com) and Cognitive Agent that Learns and Organizes Program (CALO).This includes the combined work from research teams from Carnegie Mellon University, the University of Massachusetts, the University of Rochester, the Institute for Human and Machine Cognition, Oregon State University, the University of Southern California, and Stanford University. This technology has come a very long way with dialog and natural language understanding, machine learning, evidential and probabilistic reasoning, ontology and knowledge representation, planning, reasoning and service delegation.Born In The 1960sThe long history of Siri starts in 1966 when SRI International was tasked by the Defense Department for the “development of computer capabilities for intelligent behavior in complex situations”. For decades and up to the present SRI International Artificial Intelligence Center (http://www.ai.sri.com/timeline/) has created a path of innovations with a permanent staff of the largest (about 99 computing professionals) and most highly trained (about 55 percent with a Ph.D. or its equivalent) groups of AI professionals in the world.It took the amazing vision, imagination and fortitude of Dag Kittlaus (http://www.crunchbase.com/person/dag-kittlaus), Siri Inc.'s former CEO and co-founder and Adam Cheyer (http://adam.cheyer.com/about.html), Siri Inc.'s former VP Engineering and co-founder, to build Siri, and it was far from easy. I was very remise in not mentioning them in an early version of this post. They really are the two Fathers of the Siri Apple has today. Both Dag and Adam worked tirelessly to reintroduce voice to the world, this time wrapped up in the ground breaking research and technology from DARPA. I am rather certain we will hear a great deal more from Dag and Adam.The Right Timing With The Right TechnologyThe failure of earlier forms of voice recognition and AI had a number of break points. The primary ones were based on computational power and the workable model for an operateable system. Moore’s Law, the Internet and Apple has delivered the computer horse power and some 40 years of University research has delivered the other part, Siri. Siri has focused on the 3 important points for this technology:Conversational InterfacePersonal Context AwarenessService DelegationThe 4th Computer InterfaceIt is very important to note that Siri is currently just a 1.0 version of the product, to gain relativity look back on any 1.0 version of a product. Siri will become the 4th and perhaps the most important way to interact with devices. The Mechanical User Interfaces: keyboard, mouse and gestures will always be around and are not going away anytime soon. In fact, I am predicting based on Apple patents a new set of hand gestures and holographic display technology: http://www.quora.com/How-will-Apple%E2%80%99s-new-3D-display-technology-and-3D-hand-gestures-operate?q=apple+3d. However the way humans usually interact is in an even flow of questions and answers most effectively by speaking. There is a huge barrier for most simple questions that is presented once one has to reach for a device and compose a question in a physical manner. The old way of shaping just the right question to get just the right answer in a search field is also not going away anytime soon. But asking a device for a quick answer in just the same manner you would ask a librarian or perhaps a friend will become very, very powerful.Smaller Needs To Be SmarterThe screen real estate of even the rumored iPhone 5 is limited. Rather than trying to be a search engine, Siri is focusing on mobile use cases (to start with) where models of context like place, time, personal history and limited form factors, magnify the profound power of an Intelligent Assistant. The smaller screen form factor combined with the mobile context and limited mobile bandwidth conspire to make voice the more important interface for most questions. There are a number of benefits by being offered just the right level of detail or being prompted with just the right questions. This interactive process can make the difference between fast task completion or an experience riddled with middle tasks and perhaps endpoint failure.In a mobile environment, you just don’t have time to wade through pages of links and disjointed interfaces and apps to get at simple answers. Just one question can replace 20 tasks by the user. This is the power of Siri.Task Completion Is The GoalUsing the traditional input systems, the Mechanical User Interfaces, it is hard to see all the tasks that take place. Currently to get at an answer it may require at least a handful of steps to arrive at a satisfying answer.We take all of these steps for granted because there was no other way to do it. With Siri we will be able to eliminate many of the manual tasks to just a simple question. This can be broken out to 3 basic conceptual modes:Does Things For You- Task completion:- Multiple Criteria Vertical and Horizontal searches- On the fly combining of multiple information sources- Real time editing of information based on dynamic criteria- Integrated endpoints, like ticket purchases, etc.Gets What You Say- Conversational intent:- Location context- Time context- Task context- Dialog contextGets To Know You- Learns and acts on personal information:- Who are your friends- Where do you live- What is your age- What do you likeIn the cloud there is quite a bit of heavy lifting working at producing an acceptable result. This encompasses:Location AwarenessTime AwarenessTask AwarenessSemantic DataOut Bound Cloud API ConnectionsTask And Domain ModelsConversational InterfaceText To IntentSpeech To TextText To SpeechDialog FlowAccess To Personal Informations And DemographicsSocial GraphSocial DataOf course the A5 dual core processor on the iOS device is also performing quite bit of the front end work. The primary focus is to feed the Awareness data to the Cloud along with the preprocessing of the voice recognition data.Practical UsageSiri was demonstrated on October 4th, 2011 using a "Press to ask" system. Siri also has the ability to use the accelerometer and spacial position to use a feature that will be known as "Lift to ask". Siri will also be able maintain active listening mode during a long interaction where no manual activation will be needed. This feature will likely not be available until later versions as a number of noise cancellation algorithms and more refined active listening would be developed. Siri will also be optimized to Bluetooth 4 headsets that will create far more use cases in how it will detect questions from continuous speech. In the future later versions of Siri will be "Active" continuously adjusting to interjecting answers even when no direct question was asked (with in reason). This will make interaction far closer to an interaction with a friend than any device we have ever used.A New Ecosystem, Backend Cloud APIsOnce one really understands how people will use Siri it is not too hard to see that quite a number of very popular apps and sadly some business plans may become redundant or perhaps less useful. The new model may not be apps as much as structured Cloud APIs to deliver data to Siri. Over time it is perhaps easy to see the ecosystem that will develop around Siri and the APIs that are allowed to connect. I am not at all predicting the end of apps as we know it in any way of form. However I am predicting that we will see a Darwinian adaption to the new ecosystem Siri will create. It will be of very high importance to see this trend developing and adjust business models accordingly. Perhaps the opportunities that will be available for Siri backend cloud APIs may be as large as the opportunity the iTunes app store has created.Siri will be building on an ecosystem of Backend Cloud APIs. In its simplest form the API would declare the meaning of the data going in and out via ontologies that have been pre specified reachable by Siri on the Internet. Siri will than build a response on the Fly from the API data. The concept of ontologies-as-specification is the hallmark of Tom Gruber (http://tomgruber.org/) CTO and founder of Siri Inc and now at Apple working on Siri. Tom’s approach to the challenges of reaching out to the data on the Internet and getting back something useful is quite revolutionary in that it does not require a “Semantic Web” ecosystem. Through APIs and the land rush that I postulate will develop around this ecosystem getting at relevant data will become rapidly easier.It is important to understand that the APIs that Tom speaks to are backend Cloud APIs only reachable by the Siri engine via a request that is deemed to be relevant. I am not speaking to APIs that are specific to iOS and its interaction on the platform from an App to operating system interaction. I have little doubt that Apple will open the endpoint APIs to 3rd parties. However I am certain they will retain the right to have the base APIs and data sources under direct relationship control.If you are a developer, it would be very worth your time to understand ontologies and the semantic web. There is more detail to be found here: What do application developers need to know about Siri to interface with it?.Real Fears Of The Walled GardenApple always has been the example of the “Walled Garden”. This concept almost bankrupted the company prior to the return of Steve Jobs. However at the same time, it is the Walled Garden that has made Apple so successful. It is quite frustrating and perhaps almost Monopolistic at times and is feared for good reasons. We can reach as far back as the first Apple II all the way up to the iTunes store. Apple desires to own the garden, but at the same time they have invited everyone in to play. Apple has created uncountable wealth with in the Garden walls. Some may not like the way the Garden is run, but very few can argue with the success of 1000s of companies.The Walled Garden will however be one of the last really large blows to lesser competitors and the Smart Phone offerings they may have. To compete other companies would have to find equal or greater technology that Apple owns with Siri. And Apple has about a 40 year head start with how well Siri uses the DARPA research. Google of course is in the position to compete but thus far they have a wonderful voice recognition system and really great example results for typical “Voice” searches. There does not seem to be the deep dimension we find in Siri. But I am certain this will change soon with a similar offering on Android paired with Google's approach to the semantic web problem.It is also important to note that Apple has a Patent application that may limit how APIs connect to Siri and how competitors may be able to respond: Does Apple have patents that may show the future of Siri?Just A StartClearly this new way of interacting with a device will continue to evolve. As I mentioned, there is little doubt that all the existing interfaces and modes of accessing information, via apps and the web will not disappear. However, there is also little doubt that Siri will have a very meaningful impact on how we interact with our devices and how they will interact with us.This will all start with iPhone 4S but I see it moving rapidly to iPad 3 and in the home on Apple TV. Siri plus Bluetooth 4 and Bluetooth Low Energy (BLE) will have a profound interaction relationship. BLE devices like your front door lock could be controlled via Siri, "Siri, lock the front door" or "Unlock the front door when Sarah arrives". More details can be found here: What impact will the addition of Bluetooth 4.0 have on the iPhone 4S?.It Remains To Be SeenIt remains to be seen how well all of this research and technology really winds up working. I am certain there will be a number of rather large issues to start with. However clearly Apple is in many ways, "Betting the house" on this for at least the medium term. With 20/20 hindsight perhaps 5 years from now we will be able to judge the true impact of this technology. Will it be little more than a “Parlor Trick”? Or will it be the ultimate way for us to interact with our devices?

I'm a huge, huge fan of functional languages, especially strongly-typed ones.My particular interest is in long-term program maintenance. This is exactly the opposite of programming-hero bang-it-out, throw-it-out type programming that's so common for web development, but the kinds of systems I'm used to working with require lots of programmers working for a long time. I want to be able to specify interfaces very concretely, so that the semantics of any piece of code are rock-solid stable.Functional programming means that once you have an object, its value does not change out from underneath you. No matter what your threading model, no matter what other pieces of code are in the system, no matter how long it's been since you last looked, the thing will always be what it was. Race conditions disappear. Compilers can optimize with guarantees. Processing can be farmed out to multiple CPUs.Combined with strong typing, this makes interfaces that are a lot easier to understand. "This function combines one of these with one of those and gives you back one of the other." It can't modify the input so you don't need try to document state changes. An object can't become invalid while it's being worked on. A change to the definition of a function will cause compile-time errors, meaning that bugs will be spotted by the compiler rather than the unit test (which you forgot to write anyway).There are limitations and tradeoffs, of course. I/O under truly functional languages can be brain-bending, since it's inherently a stateful notion. Garbage collection is practically mandatory (well, not quite, but close), introducing overhead (sometimes heavy overhead). Even with FP and strong typing there are still things that have to be specified with code, so some of these benefits can be obtained via a model like Extreme Programming (using unit tests as a kind of ultra-powerful, but hard to read, specification language).Functional programs can be optimized like crazy, but that doesn't mean a compiler will do all of the possible optimizations. Optimization itself is a lot of work, both by the compiler-writer and in CPU cycles. For my undergrad thesis, my Haskell program ran 10,000 times slower than the corresponding program written in other languages. (And, worse, the others grew in linear fashion while the Haskell program was some higher-order polynomial for reasons we never figured out.)Still, the more work that gets done on compilers, the faster it will be. The larger the program, the more opportunities a compiler has, and I expect FPs will reach a point where they're faster than lower-level languages for the same reason that programs in C are often faster than programs in assembly: the automated optimization is better than what you can do by hand, even if in theory you could write the same yourself.Besides, with faster CPUs and more of them, speed is no longer of the essence. For complex programs, getting them written at all is more important than having the final product take 2 milliseconds versus 20 milliseconds.Sadly, I'm no longer familiar with the current state of the art in FPs. I don't even speak Clojure, though it's got some ideas that I was working on back in the 1990s. I work mostly in Java for a variety of other practical reasons: there are more Java programmers and it's more likely you'll be able to find somebody else to maintain the program. But with programs having cycles in years instead of decades, that constraint may be less important.

This is a very interesting question to explore, and in part it depends on what you mean by "learning human behavior/psychology."There is already a way for computers to "learn" facts and knowledge from language, and it is essentially composed of two steps: information extraction, and then integration of the information into a semantic web or graph of sorts. There are many existing techniques for both of these components of such a system, but, vaguely speaking, the idea is to be able to parse natural language into its meaningful components and then boil them down into simple statements like "x is a kind of y", "x is like y", "x is unlike y", and other basic units of meaning.These units of meaning can then be represented in huge databases full of the knowledge that we've summarized by parsing information from Quora. One could easily imagine building a database much like the OMCS (Open Mind Common Sense) Project developed at MIT (see citation 1). Once we've built what is essentially a mass of easily query-able Quora facts, we can build a system much like ConceptNet (see citation 2) which was (surprise, surprise) also developed at MIT and is, at the most basic level, a hypergraph that can support all sorts of inference and textual analysis tasks, including inferring the topics or mood of any text.In that way, we can say for sure that yes, we could learn things about human behavior and psychology; we could learn, for instance, whether a human is happy or sad about something given some language that they have produced, what they intend to address with their language, and other more subtle psycholinguistic details of a sinar nature. In other words: We can definitely use the language on Quora to learn about language use and the psychology behind various examples. For example, what sorts of language might someone use or what seemingly unrelated topics might they address if they were disappointed about something? Answering questions like this statistically has given us many opportunities to learn more about the human psyche.By the way, ConceptNet is built on top of OMCS, Wikipedia, WordNet, and and many more sources of information, in a similar way to how we might build such an engine on top of Quora.On the other hand, there are many other aspects to human behavior and psychology. Quora is a great resource for learning about how people write. However, could we also extract information about how people behave and think from the content of the Quora database rather than from its syntax?Surely there are many ways to go about doing this, but I think your approach would probably depend on which elements of this question are of particular interest to you.If you want your system to directly learn facts about psychology and behavior and be able to spout them off or answer questions about them, maybe you could build the same kind of knowledge web I mentioned above, but only from topics on quora that are specifically for the purpose of learning about human psychology. Then all of the facts in your own little version of ConceptNet would be about psychology and how humans behave.On the other hand, it seems more interesting to ask whether we could infer how humans work simply from how people interact on Quora (my guess is that this is closer to the idea behind your question). As you can see, there are many resources for learning from text, and I have no doubt that there are ways a system could be taught to predict human behavior from Quora interactions. However, keep in mind that Quora is a very limited set of human interactions. There are really only a few different actions that anyone can take on Quora, and while there are an infinite number of ways to write an answer or ask a question, the overarching purposes will always be the same. Because the range of interactions is somewhat small, it may be difficult for a system to accomplish much beyond answering simple questions such as "Given a question in topic x, what kinds of people are likely to respond?" or "Given a question in topic x, how long are answers likely to be from people with background y?"However, there are definitely opportunities for learning about human behavior. You could envision a system being able to make inferences about the following set of questions if we do the right kinds of statistical analyses and correlations:What sorts of questions are more likely to get responses?What kinds of topics are humans naturally more inclined towards, and how does this correlate with their previous activity and bio?What kinds of answers are people more likely to like or dislike, and how does this correlate with their previous activity and bio?In some set of people, what sorts of "relationships" (follows) are likely to form based on their collection of upvotes, follows, questions and answers?What kinds of interactions are likely to make someone more or less popular?This kind of learning system is absolutely achievable using the right kinds of causal and classification models. I suspect that, if I were to try to address them myself, I would begin by building a simple set of features for each user (credits, answers, upvotes, followed topics, etc.), along with graphs of user relationships (with an edge from A to B if A follows B), and then attempting to use these structures in different ways for whichever of these questions I would like to adsress.As an example, if I were to attempt to answer question 5, I would most likely create a scale of popularity based on credits and followers, and then attempt to build a logistic regression model that predicts a popularity rating on that scale, based on a feature set limited to statistics about the different sorts of interactions a given user has had with the Quora system (follows, upvotes, answers, etc.)In this way, you could foreseeably build a system that has some understanding of human relationships, interests and interactions, and Quora truly does become a hotbed of opportunities for artificial intelligence to learn about human behavior. Just keep in mind that the depth of your research may be limited by the fact that there are only a few ways for users to interact with Quora.Citations:Push Singh , Thomas Lin , Erik T. Mueller , Grace Lim , Travell Perkins , Wan Li Zhu, Open Mind Common Sense: Knowledge Acquisition from the General Public, On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002, p.1223-1237, October 30-November 01, 2002H. Liu , P. Singh, ConceptNet — A Practical Commonsense Reasoning Tool-Kit, BT Technology Journal, v.22 n.4, p.211-226, October 2004