diff --git "a/data/en-de/tst2018/IWSLT.TED.tst2018.en-de.en.xml" "b/data/en-de/tst2018/IWSLT.TED.tst2018.en-de.en.xml" new file mode 100644--- /dev/null +++ "b/data/en-de/tst2018/IWSLT.TED.tst2018.en-de.en.xml" @@ -0,0 +1,2061 @@ + + + + + And let me switch to English at this point . + So the rest of the lecture will be in English . + And at some point , we will provide some simultaneous translation , not quite yet because we want to give you a little bit of suspense while I 'm talking about other subjects . + But what is this all about is that we want to provide human-human interaction support . + Now what is human-human interaction like in the real world ? + When we give a lecture , or a seminar , just like you see it here on the picture then it is a presentation by one speaker , but it is at the end of the day still a communication between people . + People watch what's going on in the screen , they watch the speaker , the speaker can have eye contact and see the people in the audience and so people connect with each other and see whether the lecture actually gets across a certain point or certain ideas . + And even a lecture in a big hall like this one can observe the audience and know , I can see you in the back for example , I can see that you 're talking to your neighbor or I can see over there somebody is kind of picking their nose somebody else may be reading a newspaper or something . + And if I see that , I may know that this is perhaps too boring or too slow and I can accelerate my lecture or my presentation et cetera . + Or if I see people's faces being puzzled , maybe my explanation of an algorithm or something was not clear . + So we connect . + We can tell from the audience what idea is getting across . + And at the same time the audience can observe a lecturer . + So it is really a human-human communication process , even in something like a large lecture like this one . + Of course much more so if we're sitting in meetings . + If we're sitting in meetings and talking to other people then it 's a very vivid exchange . + People talk with each other by speaking , but they also point to things , they produce gesture , they present power-point slides , they may look at objects or look at notes on their desk et cetera , et cetera . + Now by contrast human-machine interaction is very impoverished and terrible and we all have seen it , and you've seen this video , so I don 't need to play it but it is the video we all love so much because we all can relate to the situation of that poor guy in a cubical stuck in front of the computer and this computer essentially demands full attention and on top of it is rather stupid and devoid of any connection or any understanding of the human context . + So we like to resolve the situation and get to communication of a computer or the ability of a computer to understand human interaction and human activity better so that the computer can serve human needs better than in this poor situation . + Now we have other communication problems , multilingual communication problems . + If you don't understand English for example it's a problem because if you only speak German and I speak English up here , then you may not understand this lecture . + What can we produce ? + But if you visit China it may be worse . + You may not be able to speak Chinese except some of our friends who speak are native Chinese speakers . + But if you don't speak the language , you may be lost in a foreign language both by what they say as well by what is printed in the real world . + So what we really need to do is look at the human-machine process but as we will see in a moment , also the human-human communication and see how machines can better help this process . + Now even if we start with a human-machine process , there are already things we can do that provide helpful assistance with modalities other than simply type text . + Consider for example the situation we all have with a car navigation system in a car . + You 're driving on the road and you want to find the go to a particular destination and admittedly what we often then do is we program the GPS while driving and of course you shouldn't be doing this because it is unsafe . + And that interfaces on these navigation systems are really , really terrible because you have to punch in one letter by letter and toggle them and enter them and then press a button you can program the navigation system . + Wouldn't it be nice if you could just simply say it ? + And says I 'd like to go to Karlsruhe Am Fasanengarten fünf and then the system programs the navigation and gets you there . + So this is one of the things where clearly today modern dialogue systems come in place where the recognition by voice can recognize the command or the request by a human user and then the navigation system provides the speech navigation information and provides the navigation direction just like before . + Now this simple question of how to make a query to a navigation system is already a challenge . + First of all , we have to deal with conversational or spontaneous speech . + People generally don 't speak very clear and clean sentences . + They will stutter , they will produce ill-formed sentences . + So instead of saying how can I go to the station , they may say something like , how can I , I need to go to the station . + So how does a computer deal with the uhs and with the sentence that s not well-formed ? + That's one of the challenges . + Another challenge in a dialogue system is of course that if the user enters a piece of information that's not sufficiently precise , like if I say I need to go to Beethovenstraße . + There are probably several dozen straßen in Germany . + And so making such a request with a piece of information that's not sufficient needs to be specified or further specified and in a so-called dialogue system you'd like to have a system that then can say or ask a question , well do you need to go to Beethovenstraße in Karlsruhe or what city ? + Or if there are several such streets or several such buildings , then again you need to be able to have a dialogue system . + Moreover very often we have in our mind imprecise information . + So you may for example know that it 's a square between Kaiserstraße and Durlacher Tor or Adenauerring and so on , but you don 't know exactly where like for example if you wanted to get to this lecture hall . + So you 'd like to be able to specify it little bit indirectly in such a way that you can still program the navigation system . + So this is what dialogue systems do and at our laboratory that's one of the issues where you have a human machine interaction around this problem . + So once you have that you can then ask a question , how do I get to the Placa Catalunya in Barcelona or in another city . + And the navigation system will then give you the necessary directions . + Another thing of human machine interaction that gets a little bit more elaborate is human robot interaction . + That's something again that we are doing in the context of the humanoid robot project from the Sonderforschungsbereich , the SFB , together with colleague Dillmann Rüdiger Dillmann and his team . + And you've heard some of the presentations that they've given where it 's an issue of interacting between a robot and a machine . + But here too first of all the interaction here is complicated by the fact that again it's spontaneous speech but in this particular case it's also not a close-speaking microphone but a microphone on a robot and a robot can move around . + So it's a challenging task how to do even this even though it's human machine interaction . + Now this is if you're interacting directly between a human and a machine . + There are various different sorts of interactions we might envision as well . + Suppose for example you now want to access or want to have the computer access human information that's available only in data sources like television radio , broadcast news and various other stored resources and then further if we want to have human-human interaction that is supported by a machine or by a computer service and we'd like to look at these two or three different forms of interaction as well . + So here is for example an a system that we developed in here as well . + It's a so-called video retrieval system . + And it's some piece of research that we continue doing even in a multilingual situation . + So you want to ask a question , is there any news fro arlsruhe or has there been any strike of the Deutsche Bahn lately , for example . + And you want the computer to figure out what has happened in the news during the last two , three days . + And give you the information that only pertains to that particular query . + -- of course the problem in this case is that the information is not stored in textual form so you cannot simply search or google for it but it is in video form or in audio form if it's TV or radio . + And so what we need to do first of all is to do automatic recognition of the images in the data as well as the audio in the data to understand what the content is and then be able to search for it and perhaps at the end of the day also provide a summary that answers the questions that you may have raised . + We have such a system , that works in English and German . + So we can in fact ask queries about English news as well as German news . + And it provides then news clips that pertain to a particular query . + That you may have raised . + So this is a system that we're developing at our laboratory here and as fact in the future going to be expanded and extended . + Now we 'd like to do also implicit services . + So this is -- this is this connection between human and , sorry , computer and data sources that contain human information . + Now let's suppose we want to have a service that supports directly human-human interaction . + The Chil project is aimed at that . + So what we're doing in Chil is supporting human-human interaction , it 's instead of putting the human in the loop of computers we 're putting the computer in the loop of humans . + Humansns should be free to interact with other humans and the computer should observe that interaction and then provide helpful assistance . . . . . . . . . . . . . . . . . . . + Around this idea we formed a so-called integrated project funded by the European Commission , it's one of the largest project funded under the sixth framework program by the European Commission in this area . + We have been coordinating this in Karlsruhe together with the ITB Fraunnhofer Gesellschaft . + And as you can see there have been a large number of laboratories participating , fifteen laboratories in nine countries of the world that have participated in this problem . + Now , what do we mean by human-human communication or computer support of human-human communication . + Let me give you a couple of examples . + First one , we could have , of course , a meeting and in this case having a robot in the meeting and rather than the humans directly talking to the robot you want the humans talking to other humans and the robot listening to the conversation and every once in a while getting up and doing something helpful . + So if the robot notices that the people are getting thirsty , it should go and get coffee if it's in the morning , and maybe beer in the evening and various other things that you might imagine doing like providing printed material of information and so forth . + Another problem is the so-called this what we call the connector . + This is the idea that we all have had this problem that we're attending a meeting and somebody's phone rings and not only is this annoying because the phone rings , but it's also disconnects the person from the meeting and then if the person says I can't talk right now , I'll call you back later and then they leave messages and then you call back your friends and then they're busy et cetera . + So it 's a rather annoying , useless exercise that that -- wastes time and is a problem . + We 'd like to change that and provide different kind of assistance to to have a computer service that connects two people when they're both free . + So if I have the human butler or human secretary he or she can tell when I 'm busy or free . + And then can put a call through or connect a call whenever it's appropriate . + Why can 't we have machines that do that service for us ? + Another thing that also happens very frequently when we're sitting together with other people we forget their name . + So I know I met you before , but I don 't remember your name and so it would be nice if I had a machine that whispered in my ear and said , well this is Paul and you met him a year ago at such a meeting and you discussed the following topic . + We can't have that , and so we are always embarrassed when , you know , somebody says , oh hello , how are you , and then you don't remember when you met them and who they were and it's something that we also experience and it gets worse when you get older . + And so it'd be nice to have human memory support of this kind . + Another idea and another service of human human-interaction is if people speak different languages . + First of all you may want to have what you're saying translated to another person . + But at the same time you may also want to listen to a conversation between two people and see whether you can have that conversation translated . + So it's nothing as worse than you're attending a foreign language meeting and the people are sitting together and then they say Alex Waibel and you heard your name but you don't know what they said about you . + So this is terrible and you'd like to know what was said . + And again it would be nice if there was computer services that do that for you . + Now let's quickly consider what it takes to build such services so that we can actually build machines that that do all that . + So this would be the dream , this would the ideal situation what we're after having truly helpful machines that understand our need and that understand in what situation we are . + We call that context aware computing so imagine you are in a particular context and the machine should understand that human context , where we are , it should understand we're now all in lecture hall , it should understand a lecture is going on it should understand that I 'm looking at a particular person or looking into the audience et cetera , et cetera . + To have all this consider the simple example , if I if I couldn't attend a meeting and you were in that meeting and I asked you afterwards , after that meeting , why did Joe get angry at Bob about the budget ? + It is a simple question that you as a human could easily answer if I asked that question to you after the meeting . + But imagine what it would take to have a computer that can answer that question . + What would that computer have to be able to do . + What technologies would it have to have ? + Well , it needs to first of all recognize that Joe was there and Bob was there . + So IT needs to be able to identify people recognizing their faces , recognizing their voice , recognizing who they are . + Needs to know that Joe's emotion was anger needs to be able to tell emotional outburst . + It needs to know that Joe was addressing Bob when he got angry . + Right ? + If I'm looking at you or you and I get very angry then I need to be somebody needs to be able to detect I looked at you . + You know that I'm looking at you right now . + And I can tell that you're looking at me . + So this remarkable ability of a human being to tell who we're looking at is truly stunning because you're all sitting back there . + She has her eyes closed for example , she's sleeping over there in her in her seat . + And I can see that from here . + And I can see that up here you're doing this with your arm . + So I can tell all these things about what you're doing in the audience . + And it's a remarkable ability because we are doing this all at a long distance . + And we can tell so much about the other person simply by looking at them . + So anger , focus of attention , who am I talking to , it's important to know that , right ? + If I'm have a truly context aware environment and I say something to Matthias here about we should make sure that we don't delete our files . + You don't want the computer now to listen and say delete our files and then delete all my files , right . + It should know that I was talking to Matthias about something completely different . + And it should not do that right now . + That's context aware computing , it needs to understand the context that we're in and why we're saying something that we're saying . + And then last not least a topic of discussion budget and why it's happening and what is the sequence of things that we 're talking about and so on . + So to build all this to build all this we have to have speech recognition . + We have to recognize speakers , speaker identification . + Wehh need to identify emotion , emotion recognition or emotion identification . + Genre recognition , telling whether people are negotiating , lecturing and so on . + Language identification , which language was spoken here . + Summaries , topics , handwriting recognition . + Visually telling identity of people by faces gestures , body languages , track , faces and gaze and pose et cetera facial expressions and focus of attention . + There 's a long list of things we need to be able to tell and process . + And these are all cognitive systems , type things that you have studied in class namely doing all of these different things . + We have studied for example speech recognition but you see from the slides that is only one out of many different communication modalities we use . + So we call that the who , what , where , why and how of human communication . + Why do we call it this way ? + Because if I say for example , who is there I need to identify the person and I can tell that by recognizing the face but I can also do it by recognizing the speaker by voice . + I can also tell by many other biometric markers who that person is . + So it 's multimodal , it 's usually a decision we make based on multiple pieces of information . + What happened , what was said where is the person , why and how are they interacting . + All of these things are important . + So in our laboratory here in Karlsruhe as well as at Carnegie Mellon as well as many other partners in the Chil project at IBM , at Irst in Italy , AIT in Greece and UPC in Spain . + They all build rooms like this that are equipped with many cameras and many microphones . + And they can record an event that is happening in this room and we have recordings of these events seminars and they are basically now available in a European organization that distributes data and it's become a benchmark based on which we can now test out these types of algorithms . + What is the data like ? + The data records seminars that are happening in our and in other laboratories within the Chil project . + And here you see one example of a seminar given in our laboratory . + And what interests us is to extract from it certain human communication information like who is there , what is this person pointing to , what does he say , to whom does he speak where is he going to , where is he , what is the environment , what is the what is the discourse situation et cetera , et cetera . + So how do we answer all this ? + We broke down in the Chil project all these questions into actually research agenda , into actual research problems and these research items like audiovisual person tracking , tracking hands and faces , animated social agents , far field speech recognition et cetera these are actual research projects that you can do a Diplomarbeit or a dissertation on . + Wach of them is so hard that we have a dozen different dissertations going on in different areas of this problem . + Now this is enormous and is of course not something that a single person can do . + We have many people working on these research problems also at the other laboratories that are participating in the Chil project . + So this is an effort by a large consortium of researchers and people . + Now these are some videos that I'm playing but we will actually see them in a moment in reality because we brought some of the demonstrations . + So what you see here for example on the upper right is face identification as being done in our laboratory when people come through the door a camera recognizes them and identifies who they are . + In the upper left you're seeing a person tracking algorithm . + The idea here is to always track the speaker in a seminar . + Down here you see focus of attention tracking , this is an intriguing thing as well because you want to know who somebody is talking to or who somebody is looking at . + So if I 'm looking over here , you wanna be able to know or derive that I'm talking in this direction or when I'm turning around and I'm looking in that direction . + So we do this by actually automatically visually processing people's face and head pose direction and then we put little arrows on their face to tell in which direction they 're actually looking and who they 're actually talking to . + So you all know the expression if looks could kill , wenn Blicke töten könnten . + So we could actually build systems that could do that , so be careful . + Just a joke . + We 're being recorded here so I don 't wanna be on record of having said something like that when it 's just a joke . + Anyways here is another another demonstration of something that combines these things . + Now if you 're looking at a particular light switch and says turn off that switch you have three pieces of information . + One is that I 'm saying something , the word switch and I say turn off that switch but I 'm also looking in that direction and I 'm pointing with my gesture . + We need all that information , we need to know where the tip of my finger is we need to know where my face is looking and what I'm saying and integrate these things in order to build such services . + So you see this here in this demonstration where in fact Kai who is sitting right over here is in fact turning on lights and turning them off and operating various things in our laboratory . + So I think there is some demonstration we could briefly interrupt and show . + I think we might wanna do that now . + So while they 're being started OK . + Brighton . + Obviously all of these different processing algorithms are also then combined in fuse . + So if you consider for example the problem of saying if we have a meeting and I want to know who the people in the meeting are you need to recognize their faces but you could also recognize their voice . + And obviously we can combine the two and not always can you know for sure because in an open meeting I may not say anything , then speaker identification is not helpful . + And I could in a meeting just do for example what our friends are doing here , is just putting their hand in front of their nose and then recognizing faces is difficult from a visual point of view . + So this opportunistic grabbing of information that is relevant is obviously one one problem . + Bei mir geht's nie so besonders gut , es hat immer Probleme mit der Glatze . + OK . + Ganz herzlichen Dank , auch hierfür . + So , die Frage stellt sich natürlich mit all den tollen technischen Sachen , die man hier machen kann . + All das funktioniert natürlich immer noch nicht perfekt und man sieht also wie aufwendig und wie schwierig das ist , etwas was der Mensch eigentlich mit mit überraschender Leichtigkeit als Kind schon lernt Personen zu identifizieren und so weiter , aber ich denke bei uns im Kopf laufen auch Lernalgorithmen ab die immer besser werden je älter man wird . + Und so ist das natürlich eine deutliche Herausforderung zu schauen , dass wir Algorithmen oder Programme haben , die das so ähnlich tun . + Lassen Sie mich noch eine weitere Sache hier zeigen . + Was nun interessant ist bei all diesen Herausforderungen , ist wie wir diese verschiedenen Verarbeitungsschritte zusammenziehen in weitere Systeme oder Komponenten , die auf mehreren solchen Informationsquellen aufsetzen . + Wenn ich zum Beispiel mit einer Kamera Bilder aufnehme , dann kann ich natürlich auch mit Mikrophonen den Sprecher versuchen zu identifizieren . + Ich kann Mikrophonen versuchen zum Beispiel , die Zähnezu beschreiben , es ist ja im Raum nicht nur ein Sprecher sondern das Telefon mag ja klingeln oder die Türe geht auf und zu , die TTüresschlägt, die TTürekann man auf und zugehen sehen . + Und all diese Information ist natürlich relevant um dann zum Beispiel Aktivitäten in einem Raum zu erkennen . + Nun auch das haben wir jetzt mit diesen Sachen machen können . + Das ist hier ein Video von einem activity analysis oder activity detection . + Was Sie hier sehen ist im Wesentlichen die Aktivitäten in mehrerer unserer Büros . + Then we switch to English so everyone can follow here . + So what you see here is the activity analysis that cameras and microphones perform in our offices in our building . + So several students agreed by the way , this was not done without their consent of course . + But they agreed that they had microphones and cameras in their office and these microphones and cameras would then detect whether people are busy , whether they 're holding a meeting , whether they 're doing desk work whether they're discussing with each other whether nobody's in the in the office and so forth . + So you see here for example someone at their desk , you see that the desk work is the most likely hypothesis at this point . + And then in a moment you'll see someone comes in the in the door and starts a discussion with a person and as you see , all the sudden , the likelihood of a meeting increases more than the desk work . + Likelihood as people are discussing . + And all of this is done by a combination of microphone and images that can be collected and detected here in the room . + So , of course , what do you need such things for ? + Having such devices that can tell can analyze activity can then be useful for example for robots when you're interacting with that robot or when you're simply speaking with other people and a robot or in meetings when you want to provide the helpful service . + Here's another integration of such capabilities into a robot that is being talked to by one of the researchers and the robot performs certain actions based on dialogue and recognition of the human pointing to certain objects for example and giving certain instructions . + Now , if we want to recognize speech one of the difficulties of speech recognition is enhanced by the fact that a a speech event such as a lecture is in fact very difficult to to recognize . + This is a database called the translingual English database TED or sometimes affectionately also referred to as the terrible English database because it was recorded at an international conference and everybody at that international conference spoke with a different accent . + And hence the sound quality is rather poor and the and the error rates and you can see the error rates are relatively high we can see that foreign accented speeches are really difficult particularly if the microphone situation is poor and the speaking style spontaneous . + So if you put this together , you see that speech recognition is actually particular difficult because of the level of spontaneity . + So if I speak for example a lecture , I speak that lecture spontaneously and in the midst of it I have hesitation uhs and uhms , ill-formed sentences . + And on top of it the topic of discussion is very special . + A lecture is always about a special technical topic and will not be covered by the typical vocabulary from broadcast news . + So hence one of the things to notice here is that if you have a broadcast news recognizer that recognizes the the news anchor in a broadcast news TV or radio program our error rates today are fairly good . + We can recognize TV programs with better than ten percent error rate , lower than ten percent error rate , maybe five percent error rate . + But if we go and record open meetings where anyone discusses with any other other person and we use microphones that are not close talking like the one I'm wearing here but maybe such tabletop microphone , the error rate is dramatically higher , as you can see . + In such a case your error rates might be as high as fifty percent . + So still there is a lot of open questions in speech recognition today in terms of how to do this successfully . + Now , why is it ? + Part of the reason is of course the special vocabularies in special lectures but the other one is that also lectures and spontaneous speech is rather ill-formed and sloppily spoken . + So if you have for example a recording of a meeting . + So this was from a real meeting at Carnegie Mellon where the speaker said I think you were saying that they tried to influence but if you do the recognition by a machine , you get rather poor results . + Now , if you give the same speaker who said that sentence in the meeting the same microphone and a manual transcript of what she said in the meeting and you ask that speaker to reread her own sentence , so you ask her to read the sentence she spoke in the meeting it sounds like this and you can see actually that the said automatically recognized sentence below is much better than above here . + The error rate over lots of data of such reread spontaneous discourse is only half of the error rate of the actual spontaneous discourse . + So reread speech and spontaneous speech are dramatically different in terms of difficulty in speech recognition , and the reason of course is that when people speak spontaneously they swallow a lot of words or they skip a lot of words . + Now distant microphones is another challenge . + If I speak into a close-speaking microphone a recognition both of speakers as well as speech is much easier than if I have a remote microphone on the table or on the wall . + Now , back to the question we raised initially . + If we have a system that tries to do all of this recognition of faces , recognition of focus of attention , recognition of speech , recognition of speaker et cetera , et cetera we need to make progress based on solid benchmark performance . + One way that is being done is by having these Olympic Games of speech recognition or visual perception et cetera . + And this is being done now in two workshops that take place every year where people get a unknown or a secret test sequence from an actual meeting . + And different groups then try to do the speech recognition or the focus of attention tracking and so on . + And the algorithms of different research teams is being evaluated and then at the workshop the results are being discussed and the winner of the contest is then of course shown and that has provided tremendous speed up in performance or improvements so in some sense that we now can actually answer these questions , who was there , where are they , where are they going to , with actual numbers and actual performance numbers where we know seventy-six percent of the time or eighty percent of the time we can tell where the person is or who the person is . + And we can also track progress and know how much better these algorithms are getting as we go . + Now many of these things are obviously difficult so each of these individual processing algorithms are very hard to develop and to get right and to get good performance and one of the things I 'd like to stress with this slide is if we're actually looking at open environments like lectures and seminars we're actually also looking at realistic real data . + Many of the databases in the past have been artificial . + -- people sit directly in front of the camera and then you get the face photographed or something . + But in this data that we're looking at it's much harder because people turn their head they put their hand in front of their faces they talk sloppily , it is real data as humans talk with other humans . + Now let 's turn back to the question of Chil services . + Whatat can we do with all of that ? ? ? ? ? ? + What kind of services can we build ? + And I think you will see that that is actually a lot of fun to build these interesting systems . + So we talked about the connector and for this I wanna show you a video . + OK , this is the way the world is now , but now let's see how it could be . + Also Sie sehen , you see how a connector service could help in social relationships . + Well let me turn to yet one more issue that's interesting us , which is of course also the information delivery . + So what you've seen here is human-human support or computer support for human-human interaction in a situation where it is basic devices that we have but we can also be more creative and come up with devices that can be delivered only in a very private way . + So if you want to for example have delivery of personalized information , we could for example have steerable projectors beaming information in front of someone . + Or this in an intriguing device , it's a heads-up display in glasses . + So you could for example if it 's embarrassing to be reminded of the name of someone whose name you forgot you could just have it discretely beamed into the glasses saying you know this is Muntz and Colds and he works for you and so you should remember his name . + Or you know this is a very good friend of mine , who you met just yesterday and your senility has caused you to forget . + So all of this would be really nice if it's done discretely , so having for example private information delivery in your glasses or a targeted audio device that beams an audio beam to your ear so that only you can hear it and we will show that to you in a moment in the context of the translation services . + Now yet one more thing I'd like to show you is something rather wild . + Let's suppose you're in a meeting and a phone call comes in and it is really urgent that you communicate with that person on the er end but you cannot speak because it disturbs the neighbors and it may be also a personal type thing you want to say . + Then wouldn't it be nice if you could speak over the telephone silently or quietly so that the other people don't hear it ? + So how could that be done ? + That should be like science fiction . + Well , we 're working on some electromyographic speech recognition , this is now actually work that is being taken over by Tanja Schultz who has just started this summer semester here at our faculty . + It is work where the idea is to recognize the muscle movement of the cheek as someone formulates sentences without speaking them out aloud . + So if I move my mouth I'm moving my mouth but if you're not a good lipreader you can't tell . + What I said and it's certainly not loud . + But with electromyographic signals we can actually recognize what the person has said and then produce a synthetic nsmission over the telephone line . + Something wrong with the video . + There was a problem with the video . + But you get the idea , he's moving his mouth and that motion is being recognized in terms of the words that were spoken and the words are being transmitted over the telephone channel in terms of voice so that the other person on the other end actually hears the voice of the sentence that was spoken . + So it is possible , potentially , in the future to be able to sit in a meeting and carry on a conversation without anyone else hearing it . + That will be terrible because then I will have here an audience full of people who talk to their friends and well whatever . + -- the next and last issue that I 'd like to show you is language support across languages . . . . . . . . . . . . . . + So whatat can we do if we want to bridge the linguistic divide ? ? ? ? ? ? ? ? ? ? ? + Now this is of course a very interesting issue today because what we have is in today's world many languages being spoken , people participate in different activities in trading and lecturing and in many they work with partners in other countries and obviously that's important because we do increasingly communicate and work with people in other countries but at the same time we have to then speak some language that we can't understand . + So the common solution today is of course everyone learns English and everybody speaks English to each other but that's a problem in another respect is the cultural diversity and then also the the detail and the goodness of communication can suffer and so what you 'd like to be able to do is have something that can bridge this language divide and allow people to communicate with each other in their own language without actually losing that language identity . + And the question is can we do that ? + Can we do this with technology ? + Now if we ask the question why is this hard ? + Why is in fact language translation or language communication difficult ? + Then it becomes clear that it is ambiguity that's causing it . + And we have already seen in the speech recognition lecture that speech recognition is difficult but translation is difficult as well and you see up here many of the typical jokes that people have been telling about illustrating how hard it is to recognize , to translate and how to process language . + So if you for example wanted to translate the spirit is willing but the flesh is weak it could be misunderstood or mistranslated as the vodka is good but the meat is rotten . + Syntactically time flies like an arrow has six different interpretations or six different parses . + Stunning because we would only think of one . + Phonetics can be highly ambiguous , give me a new display could be give me a nudist play . + It 's the same acoustic phonetic string but it 's a different word sequence . + And you've seen already that we're dealing with some of that obviously with statistical language model models or with statistical models that assign a likelihood to each of these different hypotheses and bringing them out as the better solution . + And so this is how we deal with it because each of these modules obviously would be compounding the errors if we 're stacking them behind one another . + And so a successful model always has to involve probabilistic uncertainty or deal with probabilistic with uncertainty in a probabilistic way . + And it has to work with a variety of hypotheses and pull out the correct one by using subsequent knowledge sources as it goes down the chain of processing . + Now having said that , obviously , that provides possibilities and I'd like to describe some of them in the remaining time that we have and also alert you to some of the scientific research challenges that are still ahead that we're working on . + First of all , conversational speech . + How do you translate a sentence like this ? + This is an actual recording we made in our laboratory of somebody speaking German in the context of a appointment scheduling . + The first thing that is noticeable is there are these uhs and uhm and Schmatzen et cetera in between , the recognizer has to deal with these sounds . + Second thing you will notice is there 's no punctuation because people don't say in the middle of the sentence comma , period question mark . + But they will simply speak continuously and naturally as they do . + Now if you do put punctuation in it artificially and you remove the uhs and uhms and you feed it to traditional , classical translation systems you'll see that the output sometimes still is garbage . + It is a highly disfluent thing that needs to be suitably interpreted and often what people wanted to say was actually rather simple and the way they expressed it was very complicated and confuse . + So this is the nature of human interaction and hence our solutions have to take take care or take account of that . + Just briefly we have a consortium for speech translation advanced research that was founded in nineteen ninety one . + It's a consortium that has now many partners around the world that are working on speech translation and speech translation now has become from we were the first laboratory in Germany to actually ever build a speech translator and show it to the public and our partner lab in Pittsburgh was the first one to do that in the US . + But now it is the largest funded research area in this whole area of speech and language translation speech translation , speech to speech translation . + So what do we need to do ? + First of all , we need to realize that there are certain challenges namely how do I deliver a translation capability how do I overcome performance limitations in face of noise , errors and disfluencies ? + And then how do I deal with large domains and scope and many different languages ? + Two approaches are have been proposed in the past , there is the so-called interlingua approach which which works by analyzing a sentence and decoding it in terms of some semantic representation and producing an output or a statistical translation approach where the mapping is done directly with a source channel model , very similar to the kind of models that we have seen in this lecture for speech recognition . + So in the history of speech translation work started in the late eighties , early nineties and lead up to this first demonstration of speech to speech translation systems . + They were rather limited in vocabulary and speaking style so that the next ten years were concerned with translating spontaneous speech , so that you could say a sentence fluently the way people say it but still with a limitation of domain . + So these were systems that would then recognize sentences that you speak into a system in a particular domain , so if it is for example at the doctor's office you can say I have a headache or something but you cannot discuss seventeenth century French literature . + So it would be something that you then do only in a particular domain for discussion with people in the field . + That was work done in the nineties all the way to two thousand , two thousand one and it is these kinds of systems that are now being expanded in actual fieldable systems that are being deployed either for tourists or humanitarian humanitarian services or situations or government peacekeeping police and military so these are all situations where a simple or limited domain is sufficient . + You don't want to discuss everything possible but you want to still be able to carry out a dialogue naturally . + These systems have become smaller and smaller , I can show that to you later if you come up here . + We have these systems now on PDAs and you can say a certain sentence for a tourist in one language and it comes out by speech in another . + Now we can combine all of this into what we call wearable language assistance . + So this would involve the navigation , information access document translation and dialogue translation , so again if a human is in a foreign situation , it is many challenges that come together , recognizing the road signs , recognizing the getting navigation information getting information about the locale and translating dialogue . + We have a video for that but I don't have time so let me skip this one and if we have time , I can show it to you later . + This is something that I had a chance to participate on . + Some of our systems in health care translation , so it's basically the laptops and PDAs that translate doctor-patient dialogues . + And we could actually use them in a exercise that took place in May this year where there was a coalition forces exercise done in villages and jungle of Thailand where the US government Singapore , Japan Thailand and some participation from Indonesia teamed up to provide health care for people in remote villages just on a single day , so it was advertised and thousand people would come in the morning and by the nd of the people had their teeth taken care of and got new eye glasses et cetera . + It was a remarkable exercise in very short time to bring health care to so many people and obviously in this situation language is a problem in how do you communicate between a local villager in Thailand and the and the doctor , American doctor or English speaking doctor and this is what was done here . + Another thing that's interesting is sign translation . + Here , too , we developed devices that work on PDAs with cameras on top where you can go to China and take a picture of a Chinese road sign and then see the image of the road sign or the image the program would extract the text from the road sign and do the OCR , take a character recognition and then translate that sentence into English and put it in the image so that you can tell what the road sign says . + I actually myself collected this database in China , we took thousands of pictures of road signs in China and when we came back we actually found some curious and funny examples where translation really would have been helpful like this one in the middle where you can see that the Chinese sign actually said no entry for tourists . + So here I was in China and couldn't read the sign -- that was forbidding me to enter . + Now the last challenge is domain unlimited speech translation . + These devices still are limited to particular domain . + You can say it anywhere you like but it's only health care or only tourism . + But what if I want to translate my lecture to you from English into Spanish ? + There's a number of applications like that , translation of radio broadcast , translation of lectures and speeches translational parliamentary speeches telephone conversations , meetings they're all domain unlimited , we cannot limit the systems here in domain . + And we have to make sure that a delivery is found that is suitable for the situation . + So if you want to have translation of this lecture I need to be able to do domain unlimited speech translation and you need to have it somehow delivered privately because if only you don't understand English you want to have your personal translator into Spanish or Chinese . + But everybody else wants to hear it in English , let's say and in this case you don't want to disturb everybody with a loudspeaker that speaks Chinese into the room . + But you want to have something selective . + Now can we do this ? + There's speech recognition for different genres , it turns out lectures if they are in general , as we've seen before , it 's very hard . + Word error rates , are still around thirty percent speaker independent meetings is very hard but if it's a domain if it's domain-unmlimited , however speaker adapted to a particular speaker we can actually get reasonable recognition error rates for this task . + So in one other EC project called TC Star we worked on this issue where the idea was to translate parliamentary speeches in the European Parliament from English into Spanish and German and so forth with rather surprisingly good success . + These are some translation results measured in terms of so-called Bleu-scores where translation is being done on the actual transcripts of these parliamentary speeches . + And a good performance could be obtained . + Now . + Here , something worthy to note is that these statistical systems that have been developed in this context are already substantially better than some of the commercial translation systems that you can buy outside . + OK . + Now , how good do these systems work ? + Let me get to that actually after we show you the actual demonstration . + Lecture translation is of course an extension of something like parliamentary speeches , parliamentary speeches are still a rather general topic of discussion . + But if I give a lecture on a particular technical topic it is of course much more specific . + And we want to show that to you here on that screen over there if you would just engage our lecture translator while I'm giving the lecture . + Then we should be able to see up there the automatic recognition of what I'm saying in the upper screen while at the screen below the automatic translation into Spanish . + Now notice that this is now no longer limited to a particular domain , but we have full translation of open domain speech the way I produce it here in front of you . + Now these lecture translation systems were applied to talks in the European Community or in the European Parliament but in order to apply them to this type of lecture we obviously had to introduce also special vocabularies and the system had to be adapted to the typical kind of lecture that you might see in a technical lecture at a university . + Now the university lectures that we're trying to translate here are obviously now done into Spanish , but we're working on versions in German and in Chinese . + And the goal is at some point to provide you as students automatic translation of lectures done here at the University of Karlsruhe or at the other universities in our Interact joined center . + So future semesters of this class may potentially get simultaneous translation services into multiple languages , either English or German and perhaps Chinese and find it perhaps a useful addition to the the lecture presentation . + Now how is this done ? + Again it's open domain , open vocabulary speech translation but we have to deal with spontaneous speech disfluencies . + And that can only be done by applying statistical learning algorithms much the way you have learnt in class in the speech recognition and language processing lectures of this of this class . + Now another thing we have to worry about is of course delivery . + For example right now I find it disturbing that you're all watching over there and I cannot show my slides , no one is paying attention to my lecture but everybody is paying attention to that text . + And that's obviously not a good idea for a lecture when the whole point of the lecture is to transmit an idea and not to wow you or to impress you with particular displays on another screen . + So how can we do that ? + We already mentioned the targeted audio device . + That is a device that is a audio speaker that produces a rather straight or narrow targeted beam of audio in a particular direction that it's pointing to . + Like the Spanish translation of this lecture right now you should be hearing out of this loudspeaker . + I cannot hear anything here . + So I'm not sure if it's working . + Is it working ? + Can you hear it ? + So it is really a remarkable piece of technology that was developed by Daimler Chrysler in the context of the Chil project which delivers a beam of audio only in a particular direction in the audience or in the room . + I cannot hear it for example right now . + But as the loudspeaker comes around and gets to you you will hear in your ear their talking and giving you the Spanish translation of this lecture directly to you . + So you can imagine because if the loudspeaker goes away you don't hear it you'd hear just a regular lecture so in future it may be possible to have the Spanish section over here , the Chinese section over here , the Germans here and the English speakers here . + And you all get an individual acoustic presentation of the lecture as the loudspeaker is beaming into different parts of that room . + So that's exciting . + We can actually do simultaneous translation into different languages in an audience and have several people in the audience hear the lecture in their own language without actually speaking the or understanding the lecture of the lecturer . + The other possibility is of course these translation goggles , these again are goggles with the translation text or the text of the translation being displayed into your personal goggles and you put them on and while you're listening to a lecture in one language , you see translations much like captions in the movie in your own goggles to follow the lecture . + We have such a device , we didn't bring it today but we're experimenting with these types of devices as well . + Well last not least , of course one of the things we can also do is to try to imagine a world in which you produce speech in a foreign language without speaking it . + So one set of experimentations that we have done is to combine it for example with the ENG recognition , so if you could recognize speech just by moving your lips then maybe we can also produce output in another language . + So in the future maybe you can travel to another country move your lips in German and out comes Chinese and if you believe that at some point it could be perhaps implanted in your cheek or an earring or something then you might be able to simply turn your mouth into foreign language mode and produce speech in another language . + So this in another one from this is from discovery channel , by the way . + OK . + So this was very entertaining when they came and visited us there's is a couple of TV shows like that , there was one also done by German channel but I don't have time to show them to you all . + Let me leave you with a last thought that I think is also exciting and interesting and opens up possibilities for lots of interesting research . + Much of the research in some sense we're at interesting time where this type of translation across language boundaries is starting to work . + It's starting to work in a number of different situations that people encounter if they're going to foreign countries . + And we can now actually even get to domain unlimited speech and translate it . + So this is obviously remarkable and wonderful because we can really begin to communicate freely with people speaking different languages . + However there's one big challenge still remaining which is the large number of languages in the world . + Most of the translation systems and systems that you've seen here were all developed in basically about five languages . + And these languages are either very populous many people living in those countries very rich countries that can afford big research programs or considered dangerous , so research is being being done and so there's only four five or six languages that are being being researched Chinese for example , Spanish , English German , Japanese , Korean , French in those languages there's very active and vibrant language and speech processing programs under way where these types of technologies become reality . + What about the rest of the world ? + There are six thousand languages in the world and with five languages or ten languages we're not going to cover much of that wealth of languages . + So one important research direction also that we're doing at both Karlsruhe as well as Carnegie Mellon is to look at this long tail of language . + How can we in fact take these technologies and lower the cost and lower the barrier of entry and essentially develop translation and speech processing technology faster for these other languages . + And there are a number of ideas that can be proposed to this . + Several research themes , several we have a couple of PHD theses and Diplomarbeiten that are addressing this problem of how to lower the cost of of developing systems in those languages and how to in fact produce technology with much much fewer resources in those other languages . + So unfortunately we don't have time to go into all of these and again the whole goal of this lecture was not to give you a detail of all the possible research directions that come from all of the things we've seen today but to give you an impression of what wealth of possibilities there are if we think about these cognitively aware and cognitive processing systems that begin to process and observe and interpret the world that we're living in as well as the interaction between human beings . + So there is a large number of really interesting potential services that become possible if we have such systems around us . + So needless to say , there is lots of possibilities for Diplomarbeiten , Studienarbeiten , dissertations and all of this each one of these corners of the things we touched on today really provides a number of potential research projects to do . + So I hope that one or the other people among you might be interested in the future to work with us . + If you 're interested in any of these projects come talk to us so that we can can so that you can participate in these interesting activities . + Before I go let me point out that there is some information material that you're welcome to take along . + We have on Thursday a so-called Chil technology day . + Many of the systems you've seen here today will be demonstrated to visitors from industry as well as visitors from the European Community this Thursday in the EATB as well as in our building at Fasanengarten and I think there should be a program here or programs , yes , we have leaflets of programs that you can get information on this Chil technology day as well information about our Interact center if you might be interested in one of the exchanges . + So with that I hope we have given you a little bit of overview I hope you will do well in the Kognitive-Systeme-Klausur . + My advice to you for the Klausur is do the Übungen , do the homeworks . + If you haven't done them yet start them now because there is a high correlation between failure and not doing the Übungen . + So those people who do the Übungen do well in the Klausur and those who don't don't . + So please , please , please do the Übungen do the exercises . + And I hope that you'll all manage to do well in the final exam . + Thanks very much . + + + Okay , thank you very much . + So , I'm going to present some work that I did on porting phoneme based speech recognition systems to new languages supported by articulatory feature models . + So just another to motivate the work in porting to new languages in general . + If we look at the languages that exist in the world today there are about five to seven thousand different languages which are still living and are being used in today's world and that is , of course , a large number and one of the interesting facts about that large number is that the vast majority of these languages are only spoken by a very small population . + If you look at the languages with one million speakers or above . + They are spoken by about ninety-six percent of the population . + So , roughly three hundred fifty to four hundred fifty languages are spoken by ninety-six percent of the population . + All the other remaining ninety-five percent of the languages are only spoken by six percent of the population . + Currently we are experiencing some of the fact that languages are frequently dying . + It is a trend that has started in the past , so over the past two hundred years linguists were able to show that languages have started to die but it seems that this general trend is increasing and linguists estimate a very pessimistic estimates that within a few generations up to ninety percent of all of the living languages today might have died out on the technical side , if we look at automatic speech recognition systems or natural language processing systems in general they only exist with only a fraction of these languages in the world . + So common saying nowadays is languages which were addressed are rich with a large number of speakers are dangerous . + So I put it as politically irrelevant , which is a little bit nice , I would say but these are mainly the languages that natural language processing systems have been developed for and interestingly if you look at the work of linguists linguists themselves from a non-technical point of view have also mainly worked on the main languages but very little work has been done on exploring many of the minority languages that exist in the in the world . + So , when we look at the technical side of training an ASR system training it requires large amount of datas . + Statistical methods just try to process as much preferably manually annotated audio data in order to gather necessary statistics to estimate the statistical models . + And , at the same time , ASR system now for actually a couple of years performed so well that they are being used in real-life applications . + If you look at this project , the industry partners are starting to use ASR systems even now in their systems and are thinking about how to use them . + If you look at the market there are many products now out there that make use of automatic speech recognition system and nowadays also in combination with translation systems but these systems only exist for the large languages in the world . + So , what we see here is that we are actually in danger of creating a digital divide just like that say the access to the Internet that is not available to everybody might create a digital divide on the information access size side , here we are in danger of creating a digital divide when it comes to accessing different or other languages in an automatic way . + For example , if you look at translation systems , we are currently running into the danger that translation systems are only available for the major languages and one of the reasons why languages are actually dying out at such a rapid rate is the fact that many speakers switch to languages which seem to them more advantageous so have economic advantages , advantages of political or social status and these are not only languages which are spoken in one remote village in the jungle , these are actually well known languages just as Gaelic or even nowadays Irish is considered to be on the path to extinction because people switch to English because we think it advantages to them . + So , the idea is it would be nice to keep up the language diversity in the world to have many different languages just similar , let's say , to biological diversity in order to have an healthy environment where languages can evolve and evolve well and , with them , the cultures of the people , which are closely linked to languages , can also evolve very well . + So , if we look at the way we said we need large amounts of annotated audio data in order to train the acoustic models of ASR systems if we want to address all languages in the world , or at least very many languages in the world this traditional approach most likely is not possible . + It is too expensive too time consuming when it comes to developing these systems . + So people now for quite a long time have started to look at how can we equate ASR systems in new languages in a cheaper way which can be applied to many more languages than it is done today . + So , one of the work by Tanya and Alex was the use of multilingual acoustic models to order to address this task of porting speech recognition systems to new languages with possibly little overhead or in a very fast and cheap way and cheap in that sense that we don't need much time and money and they define multilingual automatic speech recognition systems and systems that are able to recognize many languages simultaneously that was seen during training and then , as a next step these acoustic models could then be applied to new languages and one of the techniques developed by Tanya is this technique multilingual mix where you train acoustic models on multiple languages and you share the acoustic models across languages based on phoneme identity . + So , the idea behind that is , you have an annotation scheme , for example Ipa which notes all phonemes in the world in different languages in the same way and you can say that two phonemes in different languages which are represented by the same symbol sort of almost sound similar . + So you can train common models with them by sharing the training data from all the languages . + And the hope is if you have such an multilingual model if you include many languages in the training of course the well known ones that you already have ASR systems for that you then get an acoustic model which almost or even completely covers the acoustics of a new language . + In reality , there is still a clear drop in performance if you do multilingual modeling on the training language , and , also , if you apply it to a new language but , it is a really good starting point for initializing a acoustic model in a new language that you then can adapt with only few adaptation material in order to get a usable ASR system . + So , instead of collecting large amounts of data you now only need to collect fewer amounts of data in order to reach a similar performance . + And , in that way , that is the way multilingual acoustic modeling can be used for porting speech recognition systems to new languages with less effort . + So , this picture just illustrates the ML-Mix notion . + So , on the left side you have the traditional monolingual recognizer with the models for every language . + They have separate models here . + This is the model for the middle state of an M phoneme , so all the four languages have their separate models and then for ML-Mix , you pull the training data from all languages , and you train one one single Gaussian-Mixture-Models for these languages because you claim that an German M , Japanese M , etcetera they all basically sound the same . + Or at least similar enough so that they can be modeled by one single model . + So , also , in the past people have started to look at acoustic models which are different from phonemes . + So , and because people felt that phonemes are not necessarily possible to really capture all the effects that you have in speech when it comes to acoustic modeling especially researchers felt that for spontaneous speech the strict phoneme sequence that you use in order to describe the pronunciation of words does not account for all effects that you have in spontaneous speech such as elision of phonemes or the fact that phonemes who consist of different articulatory features not every articulatory feature is reached at the same level of accuracy , depending on whether you speak sloppy or depending on the context that a phoneme may occur . + So , one of alternative models that people have looked at is the use of articulatory features . + So , an articulatory feature as we are uses in this work is sort of a description of the articulatory targets that are being reached by the articulators during the articulation process . + So , for every phoneme , for example Ipa describes certain targets , so whether a sound is voiced or unvoiced , whether it is a vowel or consonant whether it is for example a plosive or not a plosive whether the dorsum of the tongue reaches a certain position during articulation . + These are the kind of articulatory features that we use when we hear talk of them , so place and manner of articulation . + And , Florian-Metze has done work on that in the past , where he showed in the monolingual case , so if you work on a well-known languages and you combine phonemes and models for these articulatory features and he showed improvements when doing this kind of combination . + And , in order to do that you need models for your articulatory features and , what he used were binary features . + So , for every articulatory feature that you define you train two models , one for detecting its absence , and one for detecting its presence . + So , for the feature voiceness , you have one model says whether sound is voiced and you have another model that says whether feature is unvoiced and we used Gaussian-Mixture-Models for that , with one hundred twenty-eight Gaussian components . + So , if you want to do frame-wise classification , you can easily build a Naive-Bayesian classifier using these two models but when incorporating that now in continuous speech recognition Florian used a stream-based setup where you combine the phoneme models and the articulatory feature models at the stage where you calculate the emission probability . + So here just is an overview of the Ipa table . + So , what you can clearly see now , for example that every phoneme in that table is sort of described as a combination of different articulatory features . + So , a phoneme actually is only a shortened for such a bundle of articulatory features , such as it is a fricative , it is labiodental and voiced , for example . + That would give you that would these bundle or vector of articulatory features you would then abbreviate as a phoneme . + And , when it comes to the stream based setup this figure illustrates the stream based setup . + So stream zero usually is our phoneme models just like you have in the phoneme based recognition and when you now want to calculate the emission probability of a state in your HMM it is not just anymore the emission probability from the Gaussian-Mixture-Model of the phoneme model but we do in the log probability domain a linear combination of this phoneme model with all the corresponding articulatory feature models that correspond to this phoneme . + So , let's say if you are calculating the score for a P then you would take the phoneme model score for the P then you would say it is a plosive , so you take the phoneme model for plosive and what is it ? + It is unvoiced , so you take the articulatory feature model for unvoiced and then you would calculate the scores , you sum them up and you assign them weights . + So , it is a log-linear combination , so you just don't sum up the values , but for numerical reasons and also for reasons how value are able to detect a certain feature you give different weights to the single probabilities or log probabilities in the sum . + So , since you need these weights what you need is actually a good way of selecting these weights in a good manner and in the past I've worked with two methods one is a heuristic simple heuristic , and the other one is a discriminative training method for finding these feature weights . + I'll explain them a little bit more in detail later . + So , what we also did in the past was work on examining whether articulatory features can also be modeled in a multilingual way and can be applied in a cross-lingual way and what we found out is that articulatory features actually can quite robustly be recognized across languages . + So , if you take a model for voiceness and it was trained in English and you apply it to German you are actually pretty much able to detect in German voice sound and distinguish them from unvoiced sound using this model that was only trained on English . + And , also , what you can do is just as you do it for phonemes you can train multilingual articulatory feature models by pooling the training data from all the different languages . + In her work , Tanya has introduced a measure called the share factor with which she measures that when you have an multilingual model , and you apply it to a new language you measure how well do you already cover the phonemes of the new target language by this multilingual model . + So , how many phonemes do they have in common and when you do the same for the articulatory features what you find out is that the share factor for articulatory features actually is higher than for phonemes , so the overlap between features in different languages seems to be in general higher than for phonemes . + So that makes them very interesting for multilingual and cross-lingual application because you are able to cover many of the features in the target language without seeing the target language . + And , in the past , so what we've looked at is at combining cross-lingual and multilingual articulatory features with monolingual phoneme models . + So , we always took phoneme models which were trained on the target language and we combined them with articulatory features from many languages or with multilingual articulatory features and then we tested on the training language of the phoneme models and we found improvements . + So , the question now that we ask ourselves is what if we do have the phoneme model also to be multilingual model or monolingual model which is different from the testing language . + So , if you have a new language which we have not seen , neither in the articulatory feature models nor the phoneme models and we now combine phoneme models multilingual ones , monolingual ones with articulatory features and applies them to the new language will we see improvements over just using phonemes ? + So , as I said before , we need to select stream rates and we have two ways of doing that . + One one thing we used was a heuristic . + So , that was a simple one . + We just selected a fixed stream rate for every articulatory feature that we would add to the stream based setup and then the weight for the phoneme based models would simply be the weight that makes them sum up to one and then we looked at the classification accuracies of the articulatory feature models and we just started to add them one by one in the order of their classification accuracy and we test the word error rate on a development set until you reach a maximum performance and then that is your setup which you apply later to the evaluation set . + The other way was actually a way of training the weights using a method called discriminative model combination . + It is a something developed by Peter-Beyerlein for actually the same stream based setup that we use . + So , what he actually did is he for example used it for training the the weight of different language models . + Which are also combined in a log-linear way in with the models from the acoustic model and just like that is exactly the same setup that we now have with the phoneme models and the articulatory feature models . + So , we used that in order to discriminatively train the weights that we have for the stream based setup and what this DMC does , it just or it implements a gradient descent on a smoothed word error rate function . + And , the way that the word error rate function is made smooth so that you can actually do a gradient descent is done in such a way that it needs an approximation of the probability of the whole hypothesis space and since that of course in reality is not possible we used as an approximation for that an N-best list . + So the experiments in this work were conducted on the Globalphone corpus , or languages from the Globalphone corpus . + Globalphone collected by Tanya and under Tanya's supervision is a corpus of read newspaper articles from many languages , I think eighteen and number is still growing , so that might already be outnumbered that number and these articles are all collected in a very similar manner , or basically in the same manner like close talking microphones , same recording quality newspaper articles are read by native speakers , normally within the country where they live and since it is such a uniform collection for many , many languages for an LVCSR task it is very well suited for doing research when porting to new languages or when comparing the performance among languages or doing multilingual modeling . + So , for our experiments , we selected the languages German , English , Russian and Spanish and we had mainly three sets from the corpus , one for training one for development work such as finding the correct stream rates and language model weights , etcetera and then once we found the optimal combination , we did the evaluation on a separate , held-out set . + So , this just gives an overview about the size for the four different languages , of the training , development and evaluation set . + So , in order to get , of course , feeling for how well your porting to your new language works , and how well the different languages perform as a baseline we trained monolingual recognizers on the languages that we selected and and just a standard setup as you know it with MFCC front end . + Left to right continuous HMM , we have context independent and context dependent models the context dependent models have three thousand models , and are phonetically tied using a classification and regression tree and then we also trained a multilingual model on the languages English , Russian and Spanish . + And we also of course trained the corresponding articulatory feature detectors for the languages English , Russian and Spanish and from that you can already guess German was our main target language , so we pretended that we don't know anything about German and did the porting to German and then you can compare against what a full-blown German system , if you had enough training material , actually would look like . + So , this just gives an overview of these baseline systems for the context independent and context dependent case for the different evaluation for the development and evaluation set and you can see that the numbers vary from language to language which usually hints at the difference in difficulty of the different languages when creating a speech recognition system for them and what you probably will notice right away Russian sort of sticks out having very high word error rates and the reason for Russian is Russian is from a linguistic point of view very complex . + First , it is highly inflecting and second , which really makes it difficult for N-gram language modeling . + It has a very loose word order . + Basically everything is possible . + The way you change words just give different intonations or different connotations to the different sentences , but you can very freely arrange the words , which leads to the fact that the Russian language model has a very high perplexity one thousand and higher and that is why we have such high word error rates for Russian and that is still an unsolved problem , but a different research problem for now . + So , if you now train a multilingual model on the languages English , Russian and Spanish get these numbers , and what you will notice is that these numbers are sort of somewhat lower than the monolingual numbers and the reason for that is even though a phoneme that is noted by the same Ipa symbol sounds very similar in the different languages it is in fact not completely the same but might have different variations . + Also , this multilingual model that covers three languages has the same amount of models as recognizer for only one language in the monolingual case . + So , if you do the experiments and train this multilingual model with let's say nine thousand models you will see that these numbers actually go down somewhat . + You don't completely reach the monolingual word error rates , but it is an indication that if you have the same amount of models for more than one language you lose somewhat . + You're not able to capture the context dependency in the context dependent tree as good as if you are only working on one language . + So , the first experiment was just a monolingual porting . + So we took the English recognizer and applied it to the German set and if you just take the phoneme models you see these numbers . + So , these are word error rates , and they are comparatively high because the English acoustic is different from the German acoustic and the word error rates , if you don't , and have any German data , and you just apply the English acoustic model to German is comparatively high . + So , when you now start to add articulatory feature models to these phoneme models . + You see that you get a drop in word error rate and , what is important to note , so we used both the heuristic and the discriminative model combination and what we did is that we actually determined the weights for the combination , the stream based setup on the English development set . + We then applied that to German . + So these numbers haven't seen any German data not even the German development set for finding the correct stream based rates and so it is actually interesting to note that these weights that we find on the English set actually seemed to somehow do something good on the German set , so they are sort of not completely language independent but they generalize to a new language in a reasonable way . + What we can also see is that when we add only the English articulatory features with the DMC we get a lower word error rate as if we use all articulatory features from the DMC . + And this must come from the fact that there is actually a mismatch between the set way determine the stream based rates and a set and the set that it tests on . + We will later see that if you actually calculate your stream based rates on the target language , you will get better numbers . + So , now instead of using the English phoneme based model and applying it to German we use now the multilingual model and apply that to German and what you can see is now that the word error rates start to drop . + So , as we know from Tanya's work if you have a multilingual model that has seen data from many languages you benefit from it when you apply it to a new language . + You are better able to capture it and now we started to add the articulatory feature models , first only the English ones then the multilingual ones at the end all monolingual ones and what you can see is that the word error rates in general drop again for the DMC . + You see , the problem that the phoneme based models and the English articulatory feature models don't hurt , but also don't give you a large gain but , if you lose all the models , you actually start to see some moderate gains . + They are not huge , but they are consistent under all different combi nations of adding articulatory feature models and calculating the stream based weights , so there is a clearly visible trend . + So , what we now did is we started to use adaptation material in order to also adapt the phoneme based model . + So , we pretended that we had fifteen minutes of adaptation data in the German language and we started to adapt the phoneme models with these fifteen minutes and you can see that the word error rate already goes down significantly and now when we add the articulatory features again , the word error rate goes down and , as we have seen , adding all articulatory features seem to be the best so , we did it this time with all the articulatory features and use the DMC in order to find the stream base and stream weights in the setup . + So in order to conclude this work already gave an indication that articulatory features are suited for supporting porting phoneme based models to new languages . + And , if you have stream weights that are estimated on the development set of a different language and then you test on they still give you improvements when you test on the new languages , so they seem to generalize somewhat across languages . + And , if you then combine the adapted phoneme models , so you adapt the phoneme models to German and you combine them with all articulatory features you also see improvements , not just when only adding unmatched phoneme models . + So , for future work , one thing that is still missing is we've been adapting the phoneme models , but so far we have not adapted the articulatory feature models . + So that would be one of the future experiments to do , what happens if you now also adapt the articulatory feature models will you seen will you see higher gains from adding the articulatory feature models . + + + Hi . + So , for those of you who don't know me , I'm Kevin-Kilgour from Karlsruhe and I'll be talking to you today about language model adaptation in particular , using interlinked semantic data . + The just a quick overview of what we actually need language models for you probably already all know this , but language models are a mathematical representation of natural language and we need them whenever machines encounter natural language . + The this this language model here was built in particular for automatic speech recognition . + A common phenomenon when building language models is that you can train a language model in one domain and it is very good in that domain . + But if you try and use it for a different domain , it just feels and if you build a language model that is general enough for all domains , it is just not as good in one particular domain . + So , to do that people have been trying to adapt the language models by taking the output so the ASR system transcribes some text . + You take this output , you analyze it and you adapt your language model depending upon this output . + It is in this field for my adaptive language model . + Comes in , and I want to propose a a suggested suggest a a language model to fit this need . + Now the goals I set for myself was I wanted a language model that is domain independent . + I don't want to have to build it to a particular domain that should be usable straightaway in any domain should be able to generalize whenever you are getting to the specifics of something , like in the previous slide it started off with actually sorry . + It started off with the teacher thing good morning class and then start talking about history so , at first the language model might detect , ah , we're in a class environment but , when more information comes in , it will specialize to , ah , we're in a history environment . + We need to be prepared for words that have to do with history . + So , that is the generalization and specialization capabilities and , it would also be nice for further processing if we could get some semantic information out of it as well . + The this is just a a rough overview of how such a language model could look . + Like we've got ASR system down at the bottom left and to build the language model we need data . + We always require data . + And I'll go into that in a second . + Okay , take from stuff out of your data , and you build lots of language models and , then , while decoding you have to find some way of mixing these language models depending upon what you've detected . + Let's go into the requirements on the data sources . + A large amounts of text . + You always need lots of text and it has to cover multiple domains and you need to have enough text to be able to build a language model for the domain if you only have a couple hundred words , or even a couple thousand , you can't build a good enough language model . + So each individual domain also has to have a lot of text . + And your data source should be able to you should be able to extract domains from your data source and , also associate text with one data source that I found is Open-Directory project which is a large directory of websites and links , a bit like Yahoo , not the search engine but the directory where you can click on on topics and go deeper down into it . + It contains over four million links and it is freely available and the links have been sorted out into almost six hundred thousand categories which we can use as concepts . + If you're utilizing the category hierarchy , you can extract texts and associate them with the categories and also the concepts because of our requirement that we need a lot of text only about ninety thousand of those categories contain , well , usable as concepts . + And , to get that much text what you can do is you can follow down the hierarchy and then constantly add more data . + So , if this is how your hierarchy looks like and you want to build a language model that programming you recursively add all the texts and the whole sub-tree . + This a more a better look at one entry . + Each entry in the Open-Directory project contains links to websites associated with that concept or category and links to sub-concepts and they in themselves also contain lots of links . + So whenever you're building it whenever you're building your or whenever you're associating a text with your concepts this is a schematic view of the Open-Directory project . + And instead of just taking one node and associating the links with it , you follow the whole sub-tree down . + So , the higher up you are the larger your language model but also the more general it is and the lower down the more specialized it becomes . + Okay . + Once you've found out what texts and what concepts you want to associate with each other be the mechanism for choosing which language model you want to use when . + To do that , you look at you look at your whole system and you find at some point you'll have some text and we need to adapt to this text . + The I decided to build attribute vectors from the text associated with each concept just using simple TFIDFs and storing them as sparse vectors . + In a selector component . + The language models you also build from the same text . + So , you have language models , and each language model has a is associated with a concept and an attribute vector . + Well , now how you've got a query to this selector component , which will be a sequence of words you build an attribute vector out of it and compare it with the attribute vectors already in the selector and then you can find which language models closest or most relevant for your current text . + Here is the TFIDFs , again and the metric I used to measure this closeness was just a simple cosine metric which was fast . + Because in principle all we have to do is do the dot product of two sparse vectors and you can do that quite fast if you've normalized them both . + So , you return the top however many concepts you want . + The only new limit is how many you can work with and you use these similarity measurements . + Introduce them as weights whenever your data interpolate them in the in the ASR system . + So , just a quick example for what the cosine metric does . + You choose those language models that are closest vector wise or angle wise . + So here is an example . + And , this is in the output from that selector component when it is queried with a sentence detected by just a standard , ordinary language model and it goes into the selector and it becomes those language models that it found to be best . + And you can see , here it found quite specific ones but it also found a more general one covering the whole topic . + And it also returned weights . + And these weights are used to interpolate the language models on the fly whenever you're running your your ASR system . + So so far , we've kept up our promise of being domain independent haven't used any domain knowledge who shows that it can generalize , and has specialization capabilities . + And this slide here also returns semantic information . + We get that pretty much for free . + Okay , that is the how it then works to guess our ASR system . + So far we've just got a language model and we haven't integrated it into anything . + At Karlsruhe , we use a Janus language model Janus recognition toolkit . + And it is decoder is Ibis decoder . + And , interesting for language models is that bottom , right hand corner . + I augmented it by adding a selector component between the the linguistic knowledge source and then a set of one language model lots of language models . + This component is in here and this component communicates with the previously built selector component and queries the selector with a particular word history that normally whenever we talk about word history with language models we are thinking of the past two words , the past five words , perhaps . + But here with just the past two words I'm sending the the past whole hypothesis of the previous sentence perhaps even of the previous five or six or ten sentences . + Because you want to get a the bigger picture . + And , for testing purposes is also interesting to use a base standard language model do a first pass and then use that decoded hypothesis to adapt the language model for a second pass . + And just a quick word about the base language model used I use two different base language models for different tests . + In one case I just took my whole data source and built a language model out of it a simple language model that is domain independent . + And because I evaluated this on the TC-Star data I also used the handmade language model that we built for the TC-Star evaluation that was domain dependent . + It was optimized for that domain . + So let's have some some more parameters . + We can't include all ninety thousand concepts . + That is not reasonable . + And we also want to evaluate was my idea of including all the texts in the sub-tree a good idea ? + Am I just adding junk to it ? + Need to test to make sure that my statement there was correct all that I have to go over word history to adapt to and how we should adapt ? + What interpolation weights to use ? + Especially interpolation weights between the base language model and the adaptive part . + So the the selector will return these concept language models , and these have to be interpolated and for more general parts , we also interpolate it with the base language model we also need to know what parameters to use . + Hm . + No . + Yes . + Did this come out ? + Okay , now everything has gone black . + You know what ? + Oh . + That turns on ? + I've got the PDF yes . + PDF file . + I still got battery , I just don't have a display . + Maybe just turn it turn it ? + Close it and reopen it ? + Sorry for the delay . + He did it intentionally . + Yeah . + Yeah . + I did I did that deliberately . + It what is the name ? + Name of the presentation . + And it should just be pres . + Pres or pres-X ? + X is the old one . + Pres is the one with the new logos . + Yeah . + Yeah . + Yeah . + Yeah . + Mhm . + Hm . + Okay . + While that is booting up , perhaps I can just tell you a bit about the evaluation data that I tested my language model on . + But , I tested it on the TC-Star development data which I split . + I used the first part for my own development , and the second part for my evaluation . + -- do we have something ? + Hm . + I actually had a second laptop . + It yeah perfect example of the oh just ignore all the videos . + Yeah . + Slide eighteen or nineteen no , keep going . + At top right hand corner . + Okay . + Okay . + Yeah . + One more . + Perhaps one more . + Okay , again , one more . + Okay , one more . + Okay . + Okay . + Well okay . + Thank you . + Sorry about the technical difficulties . + Okay , back to the presentation . + Now as I was saying before you can't load ninety thousand language models with our current computers . + In actual fact , at about one thousand was the limit and even that required over twenty gigabytes of ram and some utterances to decode them . + So , to reduce I just did some quick heuristics to reduce the amount of concepts loaded . + And I only use concepts from those two nodes , society government and regional Europe and even here , because there were ten thousand concepts here I sorted them by size and removed the top twenty and then used the largest remaining and how many of the largest remaining you can see in the next slide . + Hm . + Go forward . + I built several language models one using only ten extra concept language models overlapped to a thousand extra concept language models and compared these to see how adding more concepts helped . + Also , in this test I used the texts of the whole sub-tree and the history was just as I mentioned before as a base language model went through one pass and that was used to adapt to for the second pass . + The interpolation weights are for now set at fifty fifty . + And I used two different types of base language models . + So , if we go on we can see the results of that test as mentioned , this was tested on this was tested on the TC-Star development set . + This is just parameter tuning , so it only used the first part of that set you can see using this ODPLM , which is the more general base language model built using just my whole domain independent data set . + It got a word error rate of twenty-one point five percent and by the time I added one thousand concept language models I had reduced that to twenty point five percent . + So if go on to the next slide this method improves domain independent language models , so I already have won that point . + No we can go on to the next test which was to evaluate how using this whole sub-tree of text . + Did that help ? + Or was that a bad idea ? + All the other parameters are kept the same , and I kept the largest language model the the one that used the top one thousand concepts . + Now , here next slide . + Yeah . + Just to illustrate in one of these tests , the one with the one thousand X I only use the individual text of the node and in the standard one just the adaptive one thousand I used the whole sub-tree so if we go on to the next slide using just the text of the node was atrocious . + It just performed awful . + Whereas using the whole sub-tree text , we were able to get enough text to build a decent language model . + And just in case anybody was claiming that by choosing those two nodes to select my concept language models from that I helped it along . + I took all the text in all the concept language models and built a language model out of that and interpolated it again fifty-fifty with the base language model and tested this ODPLM mixed language model and it also wasn't as good as wasn't even as good as ODPLM language model was to begin with . + So , here we can see that using the selective method and interpolating based on the selector's weights actually did increase the performance . + We can go on to the next slide . + Okay . + The next thing to evaluate would be which history to adapt to ? + That so far it is just used the base language model , made the hypothesis , and adapted to that . + Now we'll keep that test and we'll also evaluate how it performs if we use the last hypothesis . + The well , the hypothesis decoded in in the previous step or for the previous utterance or for the previous however many utterances . + Now , we can go on to the next slide . + And here we can see that well adapting to the baseline hypothesis which is whenever there is a plus-HB , that means hypothesis as a baseline . + That performs better than adapting to ah . + Thank you . + That performs better than adapting to the previous hypothesis which is whenever there is an H-one . + And unfortunately it didn't this one here didn't perform better than just the baseline . + So we need to tune it a bit more . + We go on to the next slide . + We can see a similar situation here . + Adding the adaptive one hundred or one thousand language model with different history length is this here is without the base language model . + This is one pass . + And , computing the perplexity it goes down in this one but hardly goes down using the language model previously built by hand . + So , we can improve on our on a domain independent language model so far , but not very much on a domain dependent language model . + Now , if we go on to the next slide ? + Perhaps we're just giving this domain independent part too much weight . + So I tried increasing the weight of the optimized domain dependent language model , to see if we can get some to see if we can get some improvements that way . + And on the next slide , again , keeping I kept all the parameters the same as in the previous test and we're back to using the two pass method using the hypothesis of the base language model to adapt to . + Here you can see that whenever you really increase the weight and really turn it down you can get a slight improvement and it appears that this language model is already as adapted to the domain as it can get , pretty much . + So adding for some more adaptive parts to it didn't improve the score that much . + But after having evaluated these parameters I did run can we go on to the next slide ? + I did run an evaluation on the remaining data in the development set using a different weight and again this is a domain dependent language model . + I'm trying to see if I can improve on the domain dependent language model . + And , if we can see the scores oh go back . + Can we go back a bit ? + Okay . + Thank you . + We can see the scores , and , unfortunately it hasn't improved on the domain dependent language model just yet , but this is still just the first ration of it . + So , can we go on to the next slide ? + Mhm . + So , in conclusion that the good news is we have been able to improve on domain independent language models increasing the score of of one of them by one absolute percent and we found that using the two pass method is is so far the best method . + I would like to do more tests on the history . + It just should be noted that it is quite slow right now . + Decoding that test set increased the decoding time from by six hours using just the base language model to about nineteen hours using well , I think one thousand concept language models . + So you can't just test something you have to choose your test carefully . + We found better interpolation weights and we haven't used them yet for anything , but it does give you some semantic tags for utterances and also weights to those tags . + So other development set , we did get an improvement over the domain dependent language model , but that didn't result in an improvement on the evaluation set . + We go on to the next slide . + This is just a first draft of the language model there is lots of work that can still be done on it . + In particular well , speeding it up and what would be very interesting is a dynamic vocabulary . + So you can dynamically adjust your based on the concepts you find . + I'm very interested in how that will turn out . + And it doesn't have to be in automatic speech recognition . + You can also use it in machine translation . + Especially if you were to build build it from concepts where you had the same concept in two different languages . + So thanks for for staying with me through all the technical problems . + And thank you for your attention . + Are there any questions ? + + + Okay , thank you . + Good afternoon . + I'll be presenting my work on on big corpora and show how we filtered noise data from the Giga corpus and how we could speed up the processing time . + So this will be shown on two aspects , filtering the first aspect is the filtering and then we go to parallelizing the phrase scoring . + So , first of all the parallel corpora we all know as in machine translation they are very important and not only in machine translation but also in other NLP tasks . + And they can be manually created like the EPPS corpus or UN corpus . + And these kind of corpora have the better quality and but they are very restricted in terms of size and types . + But we can also automatically collect the data from the web and this kind of corpora have have high availability , but they have restrictions in the quality . + And the Giga corpus is one of these of these web crawled corpora was collected by Chris Callison-Burch in Two-Thousand-Nine and is still too noisy even after some heuristic cleaning by the author . + And just for comparison I put the number of sentences in the Giga corpus which is twenty-two point five million sentences and you can compare to the EPPS corpus which was collected on fourteen years of European Parliament proceedings which is like five percent of this size . + And what are the problems we face with the Giga corpus ? + Yeah , one of the problems we face is as you see here , junk portions in the in the corpus . + We can also see broken lines or broken sentences with scores sentence alignment errors like you can see here . + Or even some pairs from other languages . + And due to its size it might also take even days to finish training . + So the first acts we we treated this data is by filtering it from noise . + And we have several approaches for that . + So , we tried to automatically denoise this data using only lexical features . + And for that we created a training set and a test set from clean data available from previous evaluations namely the NC dev Two-Thousand-Seven and NC devtest Two-Thousand-Seven , for people who are have worked already in the evaluation , they know these datasets . + And so , to create the false examples we switch it thirty per cent of the of the source side switched positions for the source side so that they form false examples . + And we also needed lexical dictionaries and recreated them from the clean data EPPS and NC . + So the first approach , we call it naive approach , so we just thought that a lexical score alone would be sufficient to distinguish good pairs and bad pairs and this turns out not to work as we expected . + So the scoring formula is takes into consideration the lexical scores and by the constant which is multiplied outside the multiplication we give more chance to the longer sentences to pass the filter if if they could . + And with this approach we got very bad F-score . + Like fifty-eight percent . + And then we moved to discriminative approaches . + So for discriminative approach we have two classes . + Either we reject a pair which is with value zero , or we keep the pair value one . + And the features we used are the difference in number of words between source side and target side . + And we expect that the lower the number the lower the difference the better the the better the correspondence between source and target the IBM one score and we expect that the higher the better . + And the number of unaligned words between source and target and we expect for this that the more unaligned words the worse pair . + And the maximum number of words a given word is aligned , too , which is called the fertility . + And the maximum the fertility that should be the worse the the worse the pair . + And the first approach we tried is regression . + And by a linear combination of of scores of the features we optimized the lambdas using the Powell search against the sum of squared errors and we got an F-score of ninety per cent . + It is bad yeah . + And the next approach we tried is the logistic regression . + And we optimized it with the BFGS algorithm . + And to maximize the likelihood to the training data . + And we got much better in recall and much better on precision as well which gave us ninety-four or almost ninety-five percent of F-score . + We also tried the maximum in entropy classifier trained with the Mega-m package . + And we it did slightly better on precision , but worse on recall and then it gave a worse F-score . + The last technique we tried also is the SVM classifier which was trained by the SVM light package . + And it gave much better on precision and much better recall . + And this gave us ninety-seven percent of F-score . + And the results . + From the twenty-two point five million sentences we selected sixteen point eight and which lead us to throw like twenty-two percent of the corpus . + And we used these training data in our systems for the two last evaluations WMT and IWSLT . + And you can see the gain we got for French English . + In WMT it is around point seven for on development and test sets . + And in IWSLT it is even better . + And it is around one Bleu point for both Dev and and test . + That's for the filtering part . + Now let's move to the parallelizing part and as mentioned in the morning by Alexander the phrase scoring is or the the standard phrase scoring is just one step in building the translation model . + It comes after extracting the phrases and in which we calculate the corresponding probabilities to phrases like the one shown here . + And for that we need to count the source and target sentences . + And therefore we need the similar pairs or similar sentences to be together in order to count the number of occurrences . + And then we need a sorted list of the extracted of the extracted phrases . + Moses does it by the standard sort . + Unix command . + And here are a sample of times according to the corpora . + You can see that it can go until several days for for the big corpus which contains all the data . + So we implemented two different approaches one for shared memory architectures in which we used the STXXL library which is a an external memory container . + And and the process is is as follows . + So , we have an SMP machine , which has multiple cores , so for every core we have a thread and every thread does the processing locally , which is the sort , I mean , by processing the sort . + So every threat sorts its local data and then the aggregation or the merging is done globally which is the calculating or computing of the of the corresponding probabilities . + So , once for target and once for source . + And afterwards we tried a hybrid approach a hybrid approach and for that we used DEM-sort , which is distributed external memory sort algorithm , which is itself based on the STXXL containers and and we used also the MPI library . + And and the process is as follows , so for every node or for every process it has some local data , so it sorts and then it aggregates locally , but before doing the aggregation locally we need to ensure that every process has the right range of data . + For that we have an all to all operation after immediately after the sort . + And the problem here that in the aggregation operation some nodes could just finish way faster than the others , so the variation in time is too high . + So for that we might need some between the nodes . + I show here a comparison to the previously mentioned Moses times and our times . + So it cuts the time to the half at least with sixteen cores . + And finally a comparison between all the implemented methods so the distributed , unbalanced could cut the time until like ninety percent of speed-up . + And even we got more speed-up by balancing the load between the nodes . + And that's the last point I want to talk about . + + +http://www.ted.com/talks/trevor_timm_how_free_is_our_freedom_of_the_press +TED Talk Subtitles and Transcript: In the US, the press has a right to publish secret information the public needs to know, protected by the First Amendment. Government surveillance has made it increasingly more dangerous for whistleblowers, the source of virtually every important story about national security since 9/11, to share information. In this concise, informative talk, Freedom of the Press Foundation co-founder and TED Fellow Trevor Timm traces the recent history of government action against individuals who expose crime and injustice and advocates for technology that can help them do it safely and anonymously. +talks, Internet, TED Fellows, corruption, crime, government +2507 +Trevor Timm: How free is our freedom of the press? + + + So this is James Risen. + You may know him as the Pulitzer Prize-winning reporter for The New York Times. + Long before anybody knew Edward Snowden's name, Risen wrote a book in which he famously exposed that the NSA was illegally wiretapping the phone calls of Americans. + But it's another chapter in that book that may have an even more lasting impact. + In it, he describes a catastrophic US intelligence operation in which the CIA quite literally handed over blueprints of a nuclear bomb to Iran. + If that sounds crazy, go read it. + It's an incredible story. + But you know who didn't like that chapter? + The US government. + For nearly a decade afterwards, Risen was the subject of a US government investigation in which prosecutors demanded that he testify against one of his alleged sources. + And along the way, he became the face for the US government's recent pattern of prosecuting whistleblowers and spying on journalists. + You see, under the First Amendment, the press has the right to publish secret information in the public interest. + But it's impossible to exercise that right if the media can't also gather that news and protect the identities of the brave men and women who get it to them. + So when the government came knocking, Risen did what many brave reporters have done before him: he refused and said he'd rather go to jail. + So from 2007 to 2015, Risen lived under the specter of going to federal prison. + That is, until just days before the trial, when a curious thing happened. + Suddenly, after years of claiming it was vital to their case, the government dropped their demands to Risen altogether. + It turns out, in the age of electronic surveillance, there are very few places reporters and sources can hide. + And instead of trying and failing to have Risen testify, they could have his digital trail testify against him instead. + So completely in secret and without his consent, prosecutors got Risen's phone records. + They got his email records, his financial and banking information, his credit reports, even travel records with a list of flights he had taken. + And it was among this information that they used to convict Jeffrey Sterling, Risen's alleged source and CIA whistleblower. + Sadly, this is only one case of many. + President Obama ran on a promise to protect whistleblowers, and instead, his Justice Department has prosecuted more than all other administrations combined. + Now, you can see how this could be a problem, especially because the government considers so much of what it does secret. + Since 9/11, virtually every important story about national security has been the result of a whistleblower coming to a journalist. + So we risk seeing the press unable to do their job that the First Amendment is supposed to protect because of the government's expanded ability to spy on everyone. + But just as technology has allowed the government to circumvent reporters' rights, the press can also use technology to protect their sources even better than before. + And they can start from the moment they begin speaking with them, rather than on the witness stand after the fact. + Communications software now exists that wasn't available when Risen was writing his book, and is much more surveillance-resistant than regular emails or phone calls. + For example, one such tool is SecureDrop, an open-source whistleblower submission system that was originally created by the late Internet luminary Aaron Swartz, and is now developed at the non-profit where I work, Freedom of the Press Foundation. + Instead of sending an email, you go to a news organization's website, like this one here on The Washington Post. + From there, you can upload a document or send information much like you would on any other contact form. + It'll then be encrypted and stored on a server that only the news organization has access to. + So the government can no longer secretly demand the information, and much of the information they would demand wouldn't be available in the first place. + SecureDrop, though, is really only a small part of the puzzle for protecting press freedom in the 21st century. + Unfortunately, governments all over the world are constantly developing new spying techniques that put us all at risk. + And it's up to us going forward to make sure that it's not just the tech-savvy whistleblowers, like Edward Snowden, who have an avenue for exposing wrongdoing. + It's just as vital that we protect the next veteran's health care whistleblower alerting us to overcrowded hospitals, or the next environmental worker sounding the alarm about Flint's dirty water, or a Wall Street insider warning us of the next financial crisis. + After all, these tools weren't just built to help the brave men and women who expose crimes, but are meant to protect all of our rights under the Constitution. + Thank you. + + +http://www.ted.com/talks/robert_palmer_the_panama_papers_exposed_a_huge_global_problem_what_s_next +TED Talk Subtitles and Transcript: On April 3, 2016 we saw the largest data leak in history. The Panama Papers exposed rich and powerful people hiding vast amounts of money in offshore accounts. But what does it all mean? We called Robert Palmer of Global Witness to find out. +talks, activism, big problems, business, corruption, economics, global issues, government, identity, inequality, investment, law, money, news, poverty +2478 +Robert Palmer: The Panama Papers exposed a huge global problem. What's next? + + + [On April 3, 2016 we saw the largest data leak in history.] [The Panama Papers exposed rich and powerful people] [hiding vast amounts of money in offshore accounts.] [What does this mean?] [We called Robert Palmer of Global Witness to explain.] This week, there have been a whole slew and deluge of stories coming out from the leak of 11 million documents from a Panamanian-based law firm called Mossack Fonseca. + The release of these papers from Panama lifts the veil on a tiny piece of the secretive offshore world. + We get an insight into how clients and banks and lawyers go to companies like Mossack Fonseca and say, "OK, we want an anonymous company, can you give us one?" + So you actually get to see the emails, you get to see the exchanges of messages, you get to see the mechanics of how this works, how this operates. + Now, this has already started to have pretty immediate repercussions. + The Prime Minister of Iceland has resigned. + We've also had news that an ally of the brutal Syrian dictator Bashar Al-Assad has also got offshore companies. + There's been allegations of a $2 billion money trail that leads back to President Vladimir Putin of Russia via his close childhood friend, who happens to be a top cellist. + And there will be a lot of rich individuals out there and others who will be nervous about the next set of stories and the next set of leaked documents. + Now, this sounds like the plot of a spy thriller or a John Grisham novel. + It seems very distant from you, me, ordinary people. + Why should we care about this? + But the truth is that if rich and powerful individuals are able to keep their money offshore and not pay the taxes that they should, it means that there is less money for vital public services like healthcare, education, roads. + And that affects all of us. + Now, for my organization Global Witness, this exposé has been phenomenal. + We have the world's media and political leaders talking about how individuals can use offshore secrecy to hide and disguise their assets -- something we have been talking about and exposing for a decade. + Now, I think a lot of people find this entire world baffling and confusing, and hard to understand how this sort of offshore world works. + I like to think of it a bit like a Russian doll. + So you can have one company stacked inside another company, stacked inside another company, making it almost impossible to really understand who is behind these structures. + It can be very difficult for law enforcement or tax authorities, journalists, civil society to really understand what's going on. + I also think it's interesting that there's been less coverage of this issue in the United States. + And that's perhaps because some prominent US people just haven't figured in this exposé, in this scandal. + Now, that's not because there are no rich Americans who are stashing their assets offshore. + It's just because of the way in which offshore works, Mossack Fonseca has fewer American clients. + I think if we saw leaks from the Cayman Islands or even from Delaware or Wyoming or Nevada, you would see many more cases and examples linking back to Americans. + In fact, in a number of US states you need less information, you need to provide less information to get a company than you do to get a library card. + That sort of secrecy in America has allowed employees of school districts to rip off schoolchildren. + It has allowed scammers to rip off vulnerable investors. + This is the sort of behavior that affects all of us. + Now, at Global Witness, we wanted to see what this actually looked like in practice. + How does this actually work? + So what we did is we sent in an undercover investigator to 13 Manhattan law firms. + Our investigator posed as an African minister who wanted to move suspect funds into the United States to buy a house, a yacht, a jet. + Now, what was truly shocking was that all but one of those lawyers provided our investigator with suggestions on how to move those suspect funds. + These were all preliminary meetings, and none of the lawyers took us on as a client and of course no money moved hands, but it really shows the problem with the system. + It's also important to not just think about this as individual cases. + This is not just about an individual lawyer who's spoken to our undercover investigator and provided suggestions. + It's not just about a particular senior politician who's been caught up in a scandal. + This is about how a system works, that entrenches corruption, tax evasion, poverty and instability. + And in order to tackle this, we need to change the game. + We need to change the rules of the game to make this sort of behavior harder. + This may seem like doom and gloom, like there's nothing we can do about it, like nothing has ever changed, like there will always be rich and powerful individuals. + But as a natural optimist, I do see that we are starting to get some change. + Over the last couple of years, we've seen a real push towards greater transparency when it comes to company ownership. + This issue was put on the political agenda by the UK Prime Minister David Cameron at a big G8 Summit that was held in Northern Ireland in 2013. + And since then, the European Union is going to be creating central registers at a national level of who really owns and controls companies across Europe. + One of the things that is sad is that, actually, the US is lagging behind. + There's bipartisan legislation that had been introduced in the House and the Senate, but it isn't making as much progress as we'd like to see. + So we'd really want to see the Panama leaks, this huge peek into the offshore world, be used as a way of opening up in the US and around the world. + For us at Global Witness, this is a moment for change. + We need ordinary people to get angry at the way in which people can hide their identity behind secret companies. + We need business leaders to stand up and say, "Secrecy like this is not good for business." + We need political leaders to recognize the problem, and to commit to changing the law to open up this sort of secrecy. + Together, we can end the secrecy that is currently allowing tax evasion, corruption, money laundering to flourish. + + +http://www.ted.com/talks/joe_gebbia_how_airbnb_designs_for_trust +TED Talk Subtitles and Transcript: Joe Gebbia, the co-founder of Airbnb, bet his whole company on the belief that people can trust each other enough to stay in one another's homes. How did he overcome the stranger-danger bias? Through good design. Now, 123 million hosted nights later, Gebbia sets out his dream for a culture of sharing in which design helps foster community and connection instead of isolation and separation. +talks, behavioral economics, business, collaboration, community, culture, design, economics, entrepreneur, future, innovation, potential, privacy, product design, relationships, social change, technology, urban planning +2447 +Joe Gebbia: How Airbnb designs for trust + + + I want to tell you the story about the time I almost got kidnapped in the trunk of a red Mazda Miata. + It's the day after graduating from design school and I'm having a yard sale. + And this guy pulls up in this red Mazda and he starts looking through my stuff. + And he buys a piece of art that I made. + And it turns out he's alone in town for the night, driving cross-country on a road trip before he goes into the Peace Corps. + So I invite him out for a beer and he tells me all about his passion for making a difference in the world. + Now it's starting to get late, and I'm getting pretty tired. + As I motion for the tab, I make the mistake of asking him, "So where are you staying tonight?" + And he makes it worse by saying, "Actually, I don't have a place." + And I'm thinking, "Oh, man!" + What do you do? + We've all been there, right? + Do I offer to host this guy? + But, I just met him -- I mean, he says he's going to the Peace Corps, but I don't really know if he's going to the Peace Corps and I don't want to end up kidnapped in the trunk of a Miata. + That's a small trunk! + So then I hear myself saying, "Hey, I have an airbed you can stay on in my living room." + And the voice in my head goes, "Wait, what?" + That night, I'm laying in bed, I'm staring at the ceiling and thinking, "Oh my god, what have I done? + There's a complete stranger sleeping in my living room. + What if he's psychotic?" + My anxiety grows so much, I leap out of bed, I sneak on my tiptoes to the door, and I lock the bedroom door. + It turns out he was not psychotic. + We've kept in touch ever since. + And the piece of art he bought at the yard sale is hanging in his classroom; he's a teacher now. + This was my first hosting experience, and it completely changed my perspective. + Maybe the people that my childhood taught me to label as strangers were actually friends waiting to be discovered. + The idea of hosting people on airbeds gradually became natural to me and when I moved to San Francisco, I brought the airbed with me. + So now it's two years later. + I'm unemployed, I'm almost broke, my roommate moves out, and then the rent goes up. + And then I learn there's a design conference coming to town, and all the hotels are sold out. + And I've always believed that turning fear into fun is the gift of creativity. + So here's what I pitch my best friend and my new roommate Brian Chesky: "Brian, thought of a way to make a few bucks -- turning our place into 'designers bed and breakfast,' offering young designers who come to town a place to crash, complete with wireless Internet, a small desk space, sleeping mat, and breakfast each morning. + Ha!" + We built a basic website and Airbed and Breakfast was born. + Three lucky guests got to stay on a 20-dollar airbed on the hardwood floor. + But they loved it, and so did we. + I swear, the ham and Swiss cheese omelets we made tasted totally different because we made them for our guests. + We took them on adventures around the city, and when we said goodbye to the last guest, the door latch clicked, Brian and I just stared at each other. + Did we just discover it was possible to make friends while also making rent? + The wheels had started to turn. + My old roommate, Nate Blecharczyk, joined as engineering co-founder. + And we buckled down to see if we could turn this into a business. + Here's what we pitched investors: "We want to build a website where people publicly post pictures of their most intimate spaces, their bedrooms, the bathrooms -- the kinds of rooms you usually keep closed when people come over. + And then, over the Internet, they're going to invite complete strangers to come sleep in their homes. + It's going to be huge!" + We sat back, and we waited for the rocket ship to blast off. + It did not. + No one in their right minds would invest in a service that allows strangers to sleep in people's homes. + Why? + Because we've all been taught as kids, strangers equal danger. + Now, when you're faced with a problem, you fall back on what you know, and all we really knew was design. + In art school, you learn that design is much more than the look and feel of something -- it's the whole experience. + We learned to do that for objects, but here, we were aiming to build Olympic trust between people who had never met. + Could design make that happen? + Is it possible to design for trust? + I want to give you a sense of the flavor of trust that we were aiming to achieve. + I've got a 30-second experiment that will push you past your comfort zone. + If you're up for it, give me a thumbs-up. + OK, I need you to take out your phones. + Now that you have your phone out, I'd like you to unlock your phone. + Now hand your unlocked phone to the person on your left. + That tiny sense of panic you're feeling right now -- is exactly how hosts feel the first time they open their home. + Because the only thing more personal than your phone is your home. + People don't just see your messages, they see your bedroom, your kitchen, your toilet. + Now, how does it feel holding someone's unlocked phone? + Most of us feel really responsible. + That's how most guests feel when they stay in a home. + And it's because of this that our company can even exist. + By the way, who's holding Al Gore's phone? + Would you tell Twitter he's running for President? + OK, you can hand your phones back now. + So now that you've experienced the kind of trust challenge we were facing, I'd love to share a few discoveries we've made along the way. + What if we changed one small thing about the design of that experiment? + What if your neighbor had introduced themselves first, with their name, where they're from, the name of their kids or their dog? + Imagine that they had 150 reviews of people saying, "They're great at holding unlocked phones!" + Now how would you feel about handing your phone over? + a well-designed reputation system is key for building trust. + And we didn't actually get it right the first time. + It's hard for people to leave bad reviews. + Eventually, we learned to wait until both guests and hosts left the review before we reveal them. + Now, here's a discovery we made just last week. + We did a joint study with Stanford, where we looked at people's willingness to trust someone based on how similar they are in age, location and geography. + The research showed, not surprisingly, we prefer people who are like us. + The more different somebody is, the less we trust them. + Now, that's a natural social bias. + But what's interesting is what happens when you add reputation into the mix, in this case, with reviews. + Now, if you've got less than three reviews, nothing changes. + But if you've got more than 10, everything changes. + High reputation beats high similarity. + The right design can actually help us overcome one of our most deeply rooted biases. + Now we also learned that building the right amount of trust takes the right amount of disclosure. + This is what happens when a guest first messages a host. + If you share too little, like, "Yo," acceptance rates go down. + And if you share too much, like, "I'm having issues with my mother," acceptance rates also go down. + But there's a zone that's just right, like, "Love the artwork in your place. Coming for vacation with my family." + So how do we design for just the right amount of disclosure? + We use the size of the box to suggest the right length, and we guide them with prompts to encourage sharing. + We bet our whole company on the hope that, with the right design, people would be willing to overcome the stranger-danger bias. + What we didn't realize is just how many people were ready and waiting to put the bias aside. + This is a graph that shows our rate of adoption. + There's three things happening here. + The first, an unbelievable amount of luck. + The second is the efforts of our team. + And third is the existence of a previously unsatisfied need. + Now, things have been going pretty well. + Obviously, there are times when things don't work out. + Guests have thrown unauthorized parties and trashed homes. + Hosts have left guests stranded in the rain. + In the early days, I was customer service, and those calls came right to my cell phone. + I was at the front lines of trust breaking. + And there's nothing worse than those calls, it hurts to even think about them. + And the disappointment in the sound of someone's voice was and, I would say, still is our single greatest motivator to keep improving. + Thankfully, out of the 123 million nights we've ever hosted, less than a fraction of a percent have been problematic. + Turns out, people are justified in their trust. + And when trust works out right, it can be absolutely magical. + We had a guest stay with a host in Uruguay, and he suffered a heart attack. + The host rushed him to the hospital. + They donated their own blood for his operation. + Let me read you his review. + "Excellent house for sedentary travelers prone to myocardial infarctions. + The area is beautiful and has direct access to the best hospitals. + Javier and Alejandra instantly become guardian angels who will save your life without even knowing you. + They will rush you to the hospital in their own car while you're dying and stay in the waiting room while the doctors give you a bypass. + They don't want you to feel lonely, they bring you books to read. + And they let you stay at their house extra nights without charging you. + Highly recommended!" + Of course, not every stay is like that. + But this connection beyond the transaction is exactly what the sharing economy is aiming for. + Now, when I heard that term, I have to admit, it tripped me up. + How do sharing and transactions go together? + So let's be clear; it is about commerce. + But if you just called it the rental economy, it would be incomplete. + The sharing economy is commerce with the promise of human connection. + People share a part of themselves, and that changes everything. + You know how most travel today is, like, I think of it like fast food -- it's efficient and consistent, at the cost of local and authentic. + What if travel were like a magnificent buffet of local experiences? + What if anywhere you visited, there was a central marketplace of locals offering to get you thoroughly drunk on a pub crawl in neighborhoods you didn't even know existed. + Or learning to cook from the chef of a five-star restaurant? + Today, homes are designed around the idea of privacy and separation. + What if homes were designed to be shared from the ground up? + What would that look like? + What if cities embraced a culture of sharing? + I see a future of shared cities that bring us community and connection instead of isolation and separation. + In South Korea, in the city of Seoul, they've actually even started this. + They've repurposed hundreds of government parking spots to be shared by residents. + They're connecting students who need a place to live with empty-nesters who have extra rooms. + And they've started an incubator to help fund the next generation Tonight, just on our service, 785,000 people in 191 countries will either stay in a stranger's home or welcome one into theirs. + Clearly, it's not as crazy as we were taught. + We didn't invent anything new. + Hospitality has been around forever. + There's been many other websites like ours. + So, why did ours eventually take off? + Luck and timing aside, I've learned that you can take the components of trust, and you can design for that. + Design can overcome our most deeply rooted stranger-danger bias. + And that's amazing to me. + It blows my mind. + I think about this every time I see a red Miata go by. + Now, we know design won't solve all the world's problems. + But if it can help out with this one, if it can make a dent in this, it makes me wonder, what else can we design for next? + Thank you. + + +http://www.ted.com/talks/dalia_mogahed_what_do_you_think_when_you_look_at_me +TED Talk Subtitles and Transcript: When you look at Muslim scholar Dalia Mogahed, what do you see: a woman of faith? a scholar, a mom, a sister? or an oppressed, brainwashed, potential terrorist? In this personal, powerful talk, Mogahed asks us, in this polarizing time, to fight negative perceptions of her faith in the media -- and to choose empathy over prejudice. +talks, Islam, United States, culture, faith, politics +2442 +Dalia Mogahed: What do you think when you look at me? + + + What do you think when you look at me? + A woman of faith? An expert? + Maybe even a sister. + Or oppressed, brainwashed, a terrorist. + Or just an airport security line delay. + That one's actually true. + If some of your perceptions were negative, I don't really blame you. + That's just how the media has been portraying people who look like me. + One study found that 80 percent of news coverage about Islam and Muslims is negative. + And studies show that Americans say that most don't know a Muslim. + I guess people don't talk to their Uber drivers. + Well, for those of you who have never met a Muslim, it's great to meet you. + Let me tell you who I am. + I'm a mom, a coffee lover -- double espresso, cream on the side. + I'm an introvert. + I'm a wannabe fitness fanatic. + And I'm a practicing, spiritual Muslim. + But not like Lady Gaga says, because baby, I wasn't born this way. + It was a choice. + When I was 17, I decided to come out. + No, not as a gay person like some of my friends, but as a Muslim, and decided to start wearing the hijab, my head covering. + My feminist friends were aghast: "Why are you oppressing yourself?" + The funny thing was, it was actually at that time a feminist declaration of independence from the pressure I felt as a 17-year-old, to conform to a perfect and unattainable standard of beauty. + I didn't just passively accept the faith of my parents. + I wrestled with the Quran. + I read and reflected and questioned and doubted and, ultimately, believed. + My relationship with God -- it was not love at first sight. + It was a trust and a slow surrender that deepened with every reading of the Quran. + Its rhythmic beauty sometimes moves me to tears. + I see myself in it. I feel that God knows me. + Have you ever felt like someone sees you, completely understands you and yet loves you anyway? + That's how it feels. + And so later, I got married, and like all good Egyptians, started my career as an engineer. + I later had a child, after getting married, and I was living essentially the Egyptian-American dream. + And then that terrible morning of September, 2001. + I think a lot of you probably remember exactly where you were that morning. + I was sitting in my kitchen finishing breakfast, and I look up on the screen and see the words "Breaking News." + There was smoke, airplanes flying into buildings, people jumping out of buildings. + What was this? + An accident? + A malfunction? + My shock quickly turned to outrage. + Who would do this? + And I switch the channel and I hear, "... Muslim terrorist ...," "... in the name of Islam ...," "... Middle-Eastern descent ...," "... jihad ...," "... we should bomb Mecca." + Oh my God. + Not only had my country been attacked, but in a flash, somebody else's actions had turned me from a citizen to a suspect. + That same day, we had to drive across Middle America to move to a new city to start grad school. + And I remember sitting in the passenger seat as we drove in silence, crouched as low as I could go in my seat, for the first time in my life, afraid for anyone to know I was a Muslim. + We moved into our apartment that night in a new town in what felt like a completely different world. + And then I was hearing and seeing and reading warnings from national Muslim organizations saying things like, "Be alert," "Be aware," "Stay in well-lit areas," "Don't congregate." + I stayed inside all week. + And then it was Friday that same week, the day that Muslims congregate for worship. + And again the warnings were, "Don't go that first Friday, it could be a target." + And I was watching the news, wall-to-wall coverage. + Emotions were so raw, understandably, and I was also hearing about attacks on Muslims, or people who were perceived to be Muslim, being pulled out and beaten in the street. + Mosques were actually firebombed. + And I thought, we should just stay home. + And yet, something didn't feel right. + Because those people who attacked our country attacked our country. + I get it that people were angry at the terrorists. + Guess what? So was I. + And so to have to explain yourself all the time isn't easy. + I don't mind questions. I love questions. + It's the accusations that are tough. + Today we hear people actually saying things like, "There's a problem in this country, and it's called Muslims. + When are we going to get rid of them?" + So, some people want to ban Muslims and close down mosques. + They talk about my community kind of like we're a tumor in the body of America. + And the only question is, are we malignant or benign? + You know, a malignant tumor you extract altogether, and a benign tumor you just keep under surveillance. + The choices don't make sense, because it's the wrong question. + Muslims, like all other Americans, aren't a tumor in the body of America, we're a vital organ. + Thank you. + Muslims are inventors and teachers, first responders and Olympic athletes. + Now, is closing down mosques going to make America safer? + It might free up some parking spots, but it will not end terrorism. + Going to a mosque regularly is actually linked to having more tolerant views of people of other faiths and greater civic engagement. + And as one police chief in the Washington, DC area recently told me, people don't actually get radicalized at mosques. + They get radicalized in their basement or bedroom, in front of a computer. + And what you find about the radicalization process is it starts online, is the person gets cut off from their community, from even their family, so that the extremist group can brainwash them into believing that they, the terrorists, are the true Muslims, and everyone else who abhors their behavior and ideology are sellouts or apostates. + So if we want to prevent radicalization, we have to keep people going to the mosque. + Now, some will still argue Islam is a violent religion. + After all, a group like ISIS bases its brutality on the Quran. + Now, as a Muslim, as a mother, as a human being, I think we need to do everything we can to stop a group like ISIS. + But we would be giving in to their narrative if we cast them as representatives of a faith of 1.6 billion people. + Thank you. + ISIS has as much to do with Islam as the Ku Klux Klan has to do with Christianity. + Both groups claim to base their ideology on their holy book. + But when you look at them, they're not motivated by what they read in their holy book. + It's their brutality that makes them read these things into the scripture. + Recently, a prominent imam told me a story that really took me aback. + He said that a girl came to him because she was thinking of going to join ISIS. + And I was really surprised and asked him, had she been in contact with a radical religious leader? + And he said the problem was quite the opposite, that every cleric that she had talked to had shut her down and said that her rage, her sense of injustice in the world, was just going to get her in trouble. + And so with nowhere to channel and make sense of this anger, she was a prime target to be exploited by extremists promising her a solution. + What this imam did was to connect her back to God and to her community. + He didn't shame her for her rage -- instead, he gave her constructive ways to make real change in the world. + What she learned at that mosque prevented her from going to join ISIS. + I've told you a little bit about how Islamophobia affects me and my family. + But how does it impact ordinary Americans? + How does it impact everyone else? + How does consuming fear 24 hours a day affect the health of our democracy, the health of our free thought? + Well, one study -- actually, several studies in neuroscience -- show that when we're afraid, at least three things happen. + We become more accepting of authoritarianism, conformity and prejudice. + One study showed that when subjects were exposed to news stories that were negative about Muslims, they became more accepting of military attacks on Muslim countries and policies that curtail the rights of American Muslims. + Now, this isn't just academic. + When you look at when anti-Muslim sentiment spiked between 2001 and 2013, it happened three times, but it wasn't around terrorist attacks. + It was in the run up to the Iraq War and during two election cycles. + So Islamophobia isn't just the natural response to Muslim terrorism as I would have expected. + It can actually be a tool of public manipulation, eroding the very foundation of a free society, which is rational and well-informed citizens. + Muslims are like canaries in the coal mine. + We might be the first to feel it, but the toxic air of fear is harming us all. + And assigning collective guilt isn't just about having to explain yourself all the time. + Deah and his wife Yusor were a young married couple living in Chapel Hill, North Carolina, where they both went to school. + Deah was an athlete. + He was in dental school, talented, promising ... + And his sister would tell me that he was the sweetest, most generous human being she knew. + She was visiting him there and he showed her his resume, and she was amazed. + She said, "When did my baby brother become such an accomplished young man?" + Just a few weeks after Suzanne's visit to her brother and his new wife, their neighbor, Craig Stephen Hicks, murdered them, as well as Yusor's sister, Razan, who was visiting for the afternoon, in their apartment, execution style, after posting anti-Muslim statements on his Facebook page. + He shot Deah eight times. + So bigotry isn't just immoral, it can even be lethal. + So, back to my story. + What happened after 9/11? + Did we go to the mosque or did we play it safe and stay home? + Well, we talked it over, and it might seem like a small decision, but to us, it was about what kind of America we wanted to leave for our kids: one that would control us by fear or one where we were practicing our religion freely. + So we decided to go to the mosque. + And we put my son in his car seat, buckled him in, and we drove silently, intensely, to the mosque. + I took him out, I took off my shoes, I walked into the prayer hall and what I saw made me stop. + The place was completely full. + And then the imam made an announcement, thanking and welcoming our guests, because half the congregation were Christians, Jews, Buddhists, atheists, people of faith and no faith, who had come not to attack us, but to stand in solidarity with us. + I just break down at this time. + These people were there because they chose courage and compassion over panic and prejudice. + What will you choose? + What will you choose at this time of fear and bigotry? + Will you play it safe? + Or will you join those who say we are better than that? + Thank you. + Thank you so much. + Helen Walters: So Dalia, you seem to have struck a chord. + But I wonder, what would you say to those who might argue that you're giving a TED Talk, you're clearly a deep thinker, you work at a fancy think tank, you're an exception, you're not the rule. + What would you say to those people? + Dalia Mogahed: I would say, don't let this stage distract you, I'm completely ordinary. + I'm not an exception. + My story is not unusual. + I am as ordinary as they come. + When you look at Muslims around the world -- and I've done this, I've done the largest study ever done on Muslims around the world -- people want ordinary things. + They want prosperity for their family, they want jobs and they want to live in peace. + So I am not in any way an exception. + When you meet people who seem like an exception to the rule, oftentimes it's that the rule is broken, not that they're an exception to it. + HW: Thank you so much. Dalia Mogahed. + + +http://www.ted.com/talks/raffaello_d_andrea_meet_the_dazzling_flying_machines_of_the_future +TED Talk Subtitles and Transcript: When you hear the word "drone," you probably think of something either very useful or very scary. But could they have aesthetic value? Autonomous systems expert Raffaello D'Andrea develops flying machines, and his latest projects are pushing the boundaries of autonomous flight -- from a flying wing that can hover and recover from disturbance to an eight-propeller craft that's ambivalent to orientation ... to a swarm of tiny coordinated micro-quadcopters. Prepare to be dazzled by a dreamy, swirling array of flying machines as they dance like fireflies above the TED stage. +talks, beauty, creativity, demo, design, drones, flight, future, invention, technology +2440 +Raffaello D'Andrea: Meet the dazzling flying machines of the future + + + What started as a platform for hobbyists is poised to become a multibillion-dollar industry. + Inspection, environmental monitoring, photography and film and journalism: these are some of the potential applications for commercial drones, and their enablers are the capabilities being developed at research facilities around the world. + For example, before aerial package delivery entered our social consciousness, an autonomous fleet of flying machines built a six-meter-tall tower composed of 1,500 bricks in front of a live audience at the FRAC Centre in France, and several years ago, they started to fly with ropes. + By tethering flying machines, they can achieve high speeds and accelerations in very tight spaces. + They can also autonomously build tensile structures. + Skills learned include how to carry loads, how to cope with disturbances, and in general, how to interact with the physical world. + Today we want to show you some new projects that we've been working on. + Their aim is to push the boundary of what can be achieved with autonomous flight. + Now, for a system to function autonomously, it must collectively know the location of its mobile objects in space. + Back at our lab at ETH Zurich, we often use external cameras to locate objects, which then allows us to focus our efforts on the rapid development of highly dynamic tasks. + For the demos you will see today, however, we will use new localization technology developed by Verity Studios, a spin-off from our lab. + There are no external cameras. + Each flying machine uses onboard sensors to determine its location in space and onboard computation to determine what its actions should be. + The only external commands are high-level ones such as "take off" and "land." + This is a so-called tail-sitter. + It's an aircraft that tries to have its cake and eat it. + Like other fixed-wing aircraft, it is efficient in forward flight, much more so than helicopters and variations thereof. + Unlike most other fixed-wing aircraft, however, it is capable of hovering, which has huge advantages for takeoff, landing and general versatility. + There is no free lunch, unfortunately. + One of the limitations with tail-sitters is that they're susceptible to disturbances such as wind gusts. + We're developing new control architectures and algorithms that address this limitation. + The idea is for the aircraft to recover no matter what state it finds itself in, and through practice, improve its performance over time. + OK. + When doing research, we often ask ourselves fundamental abstract questions that try to get at the heart of a matter. + For example, one such question would be, what is the minimum number of moving parts needed for controlled flight? + Now, there are practical reasons why you may want to know the answer to such a question. + Helicopters, for example, are affectionately known as machines with a thousand moving parts all conspiring to do you bodily harm. + It turns out that decades ago, skilled pilots were able to fly remote-controlled aircraft that had only two moving parts: a propeller and a tail rudder. + We recently discovered that it could be done with just one. + This is the monospinner, the world's mechanically simplest controllable flying machine, invented just a few months ago. + It has only one moving part, a propeller. + It has no flaps, no hinges, no ailerons, no other actuators, no other control surfaces, just a simple propeller. + Even though it's mechanically simple, there's a lot going on in its little electronic brain to allow it to fly in a stable fashion and to move anywhere it wants in space. + Even so, it doesn't yet have the sophisticated algorithms of the tail-sitter, which means that in order to get it to fly, I have to throw it just right. + And because the probability of me throwing it just right is very low, given everybody watching me, what we're going to do instead is show you a video that we shot last night. + If the monospinner is an exercise in frugality, this machine here, the omnicopter, with its eight propellers, is an exercise in excess. + What can you do with all this surplus? + The thing to notice is that it is highly symmetric. + As a result, it is ambivalent to orientation. + This gives it an extraordinary capability. + It can move anywhere it wants in space irrespective of where it is facing and even of how it is rotating. + It has its own complexities, mainly having to do with the interacting flows from its eight propellers. + Some of this can be modeled, while the rest can be learned on the fly. + Let's take a look. + If flying machines are going to enter part of our daily lives, they will need to become extremely safe and reliable. + This machine over here is actually two separate two-propeller flying machines. + This one wants to spin clockwise. + This other one wants to spin counterclockwise. + When you put them together, they behave like one high-performance quadrocopter. + If anything goes wrong, however -- a motor fails, a propeller fails, electronics, even a battery pack -- the machine can still fly, albeit in a degraded fashion. + We're going to demonstrate this to you now by disabling one of its halves. + This last demonstration is an exploration of synthetic swarms. + The large number of autonomous, coordinated entities offers a new palette for aesthetic expression. + We've taken commercially available micro quadcopters, each weighing less than a slice of bread, by the way, and outfitted them with our localization technology and custom algorithms. + Because each unit knows where it is in space and is self-controlled, there is really no limit to their number. + Hopefully, these demonstrations will motivate you to dream up new revolutionary roles for flying machines. + That ultrasafe one over there for example has aspirations to become a flying lampshade on Broadway. + The reality is that it is difficult to predict the impact of nascent technology. + And for folks like us, the real reward is the journey and the act of creation. + It's a continual reminder of how wonderful and magical the universe we live in is, that it allows creative, clever creatures to sculpt it in such spectacular ways. + The fact that this technology has such huge commercial and economic potential is just icing on the cake. + Thank you. + + +http://www.ted.com/talks/allan_adams_what_the_discovery_of_gravitational_waves_means +TED Talk Subtitles and Transcript: More than a billion years ago, two black holes in a distant galaxy locked into a spiral, falling inexorably toward each other, and collided. "All that energy was pumped into the fabric of time and space itself," says theoretical physicist Allan Adams, "making the universe explode in roiling waves of gravity." About 25 years ago, a group of scientists built a giant laser detector called LIGO to search for these kinds of waves, which had been predicted but never observed. In this mind-bending talk, Adams breaks down what happened when, in September 2015, LIGO detected an unthinkably small anomaly, leading to one of the most exciting discoveries in the history of physics. +talks, astronomy, cosmos, curiosity, exploration, nature, physics, science, space, technology, universe +2439 +Allan Adams: What the discovery of gravitational waves means + + + 1.3 billion years ago, in a distant, distant galaxy, two black holes locked into a spiral, converting three Suns' worth of stuff into pure energy in a tenth of a second. + For that brief moment in time, the glow was brighter than all the stars in all the galaxies in all of the known Universe. + It was a very big bang. + But they didn't release their energy in light. + I mean, you know, they're black holes. + All that energy was pumped into the fabric of space and time itself, making the Universe explode in gravitational waves. + Let me give you a sense of the timescale at work here. + 1.3 billion years ago, Earth had just managed to evolve multicellular life. + Since then, Earth has made and evolved corals, fish, plants, dinosaurs, people and even -- God save us -- the Internet. + And about 25 years ago, a particularly audacious set of people -- Rai Weiss at MIT, Kip Thorne and Ronald Drever at Caltech -- to build a giant laser detector with which to search for the gravitational waves from things like colliding black holes. + Now, most people thought they were nuts. + But enough people realized that they were brilliant nuts that the US National Science Foundation decided to fund their crazy idea. + So after decades of development, construction and imagination and a breathtaking amount of hard work, they built their detector, called LIGO: The Laser Interferometer Gravitational-Wave Observatory. + For the last several years, LIGO's been undergoing a huge expansion in its accuracy, a tremendous improvement in its detection ability. + It's now called Advanced LIGO as a result. + In early September of 2015, LIGO turned on for a final test run while they sorted out a few lingering details. + And on September 14 of 2015, just days after the detector had gone live, the gravitational waves from those colliding black holes passed through the Earth. + And they passed through you and me. + And they passed through the detector. + Scott Hughes: There's two moments in my life more emotionally intense than that. + One is the birth of my daughter. + The other is when I had to say goodbye to my father when he was terminally ill. + You know, it was the payoff of my career, basically. + Everything I'd been working on -- it's no longer science fiction! Allan Adams: So that's my very good friend and collaborator, Scott Hughes, a theoretical physicist at MIT, who has been studying gravitational waves from black holes and the signals that they could impart on observatories like LIGO, So let me take a moment to tell you what I mean by a gravitational wave. + A gravitational wave is a ripple in the shape of space and time. + As the wave passes by, it stretches space and everything in it in one direction, and compresses it in the other. + This has led to countless instructors of general relativity doing a really silly dance to demonstrate in their classes on general relativity. + "It stretches and expands, it stretches and expands." + So the trouble with gravitational waves is that they're very weak; they're preposterously weak. + For example, the waves that hit us on September 14 -- and yes, every single one of you stretched and compressed under the action of that wave -- when the waves hit, they stretched the average person by one part in 10 to the 21. + That's a decimal place, 20 zeroes, That's why everyone thought the LIGO people were nuts. + Even with a laser detector five kilometers long -- and that's already crazy -- they would have to measure the length of those detectors to less than one thousandth of the radius of the nucleus of an atom. + And that's preposterous. + So towards the end of his classic text on gravity, described the hunt for gravitational waves as follows: He said, "The technical difficulties to be surmounted in constructing such detectors are enormous. + But physicists are ingenious, and with the support of a broad lay public, all obstacles will surely be overcome." + Thorne published that in 1973, 42 years before he succeeded. + Now, coming back to LIGO, Scott likes to say that LIGO acts like an ear more than it does like an eye. + I want to explain what that means. + Visible light has a wavelength, a size, that's much smaller than the things around you, the features on people's faces, the size of your cell phone. + And that's really useful, because it lets you make an image or a map of the things around you, by looking at the light coming from different spots in the scene about you. + Sound is different. + Audible sound has a wavelength that can be up to 50 feet long. + And that makes it really difficult -- in fact, in practical purposes, impossible -- to make an image of something you really care about. + Your child's face. + Instead, we use sound to listen for features like pitch and tone and rhythm and volume to infer a story behind the sounds. + That's Alice talking. + That's Bob interrupting. + Silly Bob. + So, the same is true of gravitational waves. + We can't use them to make simple images of things out in the Universe. + But by listening to changes in the amplitude and frequency of those waves, we can hear the story that those waves are telling. + And at least for LIGO, the frequencies that it can hear are in the audio band. + So if we convert the wave patterns into pressure waves and air, into sound, we can literally hear the Universe speaking to us. + For example, listening to gravity, just in this way, can tell us a lot about the collision of two black holes, something my colleague Scott has spent an awful lot of time thinking about. + SH: If the two black holes are non-spinning, you get a very simple chirp: whoop! + If the two bodies are spinning very rapidly, I have that same chirp, but with a modulation on top of it, so it kind of goes: whir, whir, whir! + It's sort of the vocabulary of spin imprinted on this waveform. + AA: So on September 14, 2015, a date that's definitely going to live in my memory, LIGO heard this: [Whirring sound] So if you know how to listen, that is the sound of -- SH: ... two black holes, each of about 30 solar masses, that were whirling around at a rate comparable to what goes on in your blender. + AA: It's worth pausing here to think about what that means. + Two black holes, the densest thing in the Universe, one with a mass of 29 Suns and one with a mass of 36 Suns, whirling around each other 100 times per second before they collide. + Just imagine the power of that. + It's fantastic. + And we know it because we heard it. + That's the lasting importance of LIGO. + It's an entirely new way to observe the Universe that we've never had before. + It's a way that lets us hear the Universe and hear the invisible. + And there's a lot out there that we can't see -- in practice or even in principle. + So supernova, for example: I would love to know why very massive stars explode in supernovae. + They're very useful; we've learned a lot about the Universe from them. + The problem is, all the interesting physics happens in the core, and the core is hidden behind thousands of kilometers of iron and carbon and silicon. + We'll never see through it, it's opaque to light. + Gravitational waves go through iron as if it were glass -- The Big Bang: I would love to be able to explore the first few moments of the Universe, but we'll never see them, because the Big Bang itself is obscured by its own afterglow. + With gravitational waves, we should be able to see all the way back to the beginning. + Perhaps most importantly, I'm positive that there are things out there that we've never seen that we may never be able to see and that we haven't even imagined -- things that we'll only discover by listening. + And in fact, even in that very first event, LIGO found things that we didn't expect. + Here's my colleague and one of the key members of the LIGO collaboration, Matt Evans, my colleague at MIT, addressing exactly that: Matt Evans: The kinds of stars which produce the black holes that we observed here are the dinosaurs of the Universe. + They're these massive things that are old, from prehistoric times, and the black holes are kind of like the dinosaur bones with which we do this archeology. + So it lets us really get a whole nother angle on what's out there in the Universe and how the stars came to be, and in the end, of course, how we came to be out of this whole mess. + AA: Our challenge now is to be as audacious as possible. + Thanks to LIGO, we know how to build exquisite detectors that can listen to the Universe, to the rustle and the chirp of the cosmos. + Our job is to dream up and build new observatories -- a whole new generation of observatories -- on the ground, in space. + I mean, what could be more glorious than listening to the Big Bang itself? + Our job now is to dream big. + Dream with us. + Thank you. + + +http://www.ted.com/talks/shonda_rhimes_my_year_of_saying_yes_to_everything +TED Talk Subtitles and Transcript: Shonda Rhimes, the titan behind Grey's Anatomy, Scandal and How to Get Away With Murder, is responsible for some 70 hours of television per season, and she loves to work. "When I am hard at work, when I am deep in it, there is no other feeling," she says. She has a name for this feeling: The hum. The hum is a drug, the hum is music, the hum is God's whisper in her ear. But what happens when it stops? Is she anything besides the hum? In this moving talk, join Rhimes on a journey through her "year of yes" and find out how she got her hum back. +talks, children, creativity, culture, decision-making, family, identity, motivation, parenting, personal growth, television, work, work-life balance, writing +2438 +Shonda Rhimes: My year of saying yes to everything + + + So a while ago, I tried an experiment. + For one year, I would say yes to all the things that scared me. + Anything that made me nervous, took me out of my comfort zone, I forced myself to say yes to. + Did I want to speak in public? + No, but yes. + Did I want to be on live TV? + No, but yes. + Did I want to try acting? + No, no, no, but yes, yes, yes. + And a crazy thing happened: the very act of doing the thing that scared me made it not scary. + My fear of public speaking, my social anxiety, poof, gone. + It's amazing, the power of one word. + "Yes" changed my life. + "Yes" changed me. + But there was one particular yes that affected my life in the most profound way, in a way I never imagined, and it started with a question from my toddler. + I have these three amazing daughters, Harper, Beckett and Emerson, and Emerson is a toddler who inexplicably refers to everyone as "honey." + as though she's a Southern waitress. + "Honey, I'm gonna need some milk for my sippy cup." + The Southern waitress asked me to play with her one evening when I was on my way somewhere, and I said, "Yes." + And that yes was the beginning of a new way of life for my family. + I made a vow that from now on, every time one of my children asks me to play, no matter what I'm doing or where I'm going, I say yes, every single time. + Almost. I'm not perfect at it, but I try hard to practice it. + And it's had a magical effect on me, on my children, on our family. + But it's also had a stunning side effect, and it wasn't until recently that I fully understood it, that I understood that saying yes to playing with my children likely saved my career. + See, I have what most people would call a dream job. + I'm a writer. I imagine. I make stuff up for a living. + Dream job. + No. + I'm a titan. + Dream job. + I create television. I executive produce television. + I make television, a great deal of television. + In one way or another, this TV season, I'm responsible for bringing about 70 hours of programming to the world. + Four television programs, 70 hours of TV -- Three shows in production at a time, sometimes four. + Each show creates hundreds of jobs that didn't exist before. + The budget for one episode of network television can be anywhere from three to six million dollars. + Let's just say five. + A new episode made every nine days times four shows, so every nine days that's 20 million dollars worth of television, four television programs, 70 hours of TV, three shows in production at a time, sometimes four, 16 episodes going on at all times: 24 episodes of "Grey's," 21 episodes of "Scandal," 15 episodes of "How To Get Away With Murder," 10 episodes of "The Catch," that's 70 hours of TV, + that's 350 million dollars for a season. + In America, my television shows are back to back to back on Thursday night. + Around the world, my shows air in 256 territories in 67 languages for an audience of 30 million people. + My brain is global, and 45 hours of that 70 hours of TV are shows I personally created and not just produced, so on top of everything else, I need to find time, real quiet, creative time, to gather my fans around the campfire and tell my stories. + Four television programs, 70 hours of TV, three shows in production at a time, sometimes four, 350 million dollars, campfires burning all over the world. + You know who else is doing that? + Nobody, so like I said, I'm a titan. + Dream job. + Now, I don't tell you this to impress you. + I tell you this because I know what you think of when you hear the word "writer." + I tell you this so that all of you out there who work so hard, whether you run a company or a country or a classroom or a store or a home, take me seriously when I talk about working, so you'll get that I don't peck at a computer and imagine all day, so you'll hear me when I say that I understand that a dream job is not about dreaming. + It's all job, all work, all reality, all blood, all sweat, no tears. + I work a lot, very hard, and I love it. + When I'm hard at work, when I'm deep in it, there is no other feeling. + For me, my work is at all times building a nation out of thin air. + It is manning the troops. It is painting a canvas. + It is hitting every high note. It is running a marathon. + It is being Beyoncé. + And it is all of those things at the same time. + I love working. + It is creative and mechanical and exhausting and exhilarating and hilarious and disturbing and clinical and maternal and cruel and judicious, and what makes it all so good is the hum. + There is some kind of shift inside me when the work gets good. + A hum begins in my brain, and it grows and it grows and that hum sounds like the open road, and I could drive it forever. + And a lot of people, when I try to explain the hum, they assume that I'm talking about the writing, that my writing brings me joy. + And don't get me wrong, it does. + But the hum -- it wasn't until I started making television that I started working, working and making and building and creating and collaborating, that I discovered this thing, this buzz, this rush, this hum. + The hum is more than writing. + The hum is action and activity. The hum is a drug. + The hum is music. The hum is light and air. + The hum is God's whisper right in my ear. + And when you have a hum like that, you can't help but strive for greatness. + That feeling, you can't help but strive for greatness at any cost. + That's called the hum. + Or, maybe it's called being a workaholic. + Maybe it's called genius. + Maybe it's called ego. + Maybe it's just fear of failure. + I don't know. + I just know that I'm not built for failure, and I just know that I love the hum. + I just know that I want to tell you I'm a titan, and I know that I don't want to question it. + But here's the thing: the more successful I become, the more shows, the more episodes, the more barriers broken, the more work there is to do, the more balls in the air, the more eyes on me, the more history stares, the more expectations there are. + The more I work to be successful, the more I need to work. + And what did I say about work? + I love working, right? + The nation I'm building, the marathon I'm running, the troops, the canvas, the high note, the hum, the hum, the hum. + I like that hum. I love that hum. + I need that hum. I am that hum. + Am I nothing but that hum? + And then the hum stopped. + Overworked, overused, overdone, burned out. + The hum stopped. + Now, my three daughters are used to the truth that their mother is a single working titan. + Harper tells people, "My mom won't be there, but you can text my nanny." + And Emerson says, "Honey, I'm wanting to go to ShondaLand." + They're children of a titan. + They're baby titans. + They were 12, 3, and 1 when the hum stopped. + The hum of the engine died. + I stopped loving work. I couldn't restart the engine. + The hum would not come back. + My hum was broken. + I was doing the same things I always did, all the same titan work, 15-hour days, working straight through the weekends, no regrets, never surrender, a titan never sleeps, a titan never quits, full hearts, clear eyes, yada, whatever. + But there was no hum. + Inside me was silence. + Four television programs, 70 hours of TV, three shows in production at a time, sometimes four. + Four television programs, 70 hours of TV, three shows in production at a time ... + I was the perfect titan. + I was a titan you could take home to your mother. + All the colors were the same, and I was no longer having any fun. + And it was my life. + It was all I did. + I was the hum, and the hum was me. + So what do you do when the thing you do, the work you love, starts to taste like dust? + Now, I know somebody's out there thinking, "Cry me a river, stupid writer titan lady." + But you know, you do, if you make, if you work, if you love what you do, being a teacher, being a banker, being a mother, being a painter, being Bill Gates, if you simply love another person and that gives you the hum, if you know the hum, if you know what the hum feels like, if you have been to the hum, when the hum stops, who are you? + What are you? + What am I? + Am I still a titan? + If the song of my heart ceases to play, can I survive in the silence? + And then my Southern waitress toddler asks me a question. + I'm on my way out the door, I'm late, and she says, "Momma, wanna play?" + And I'm just about to say no, when I realize two things. + One, I'm supposed to say yes to everything, and two, my Southern waitress didn't call me "honey." + She's not calling everyone "honey" anymore. + When did that happen? + I'm missing it, being a titan and mourning my hum, and here she is changing right before my eyes. + And so she says, "Momma, wanna play?" + And I say, "Yes." + There's nothing special about it. + We play, and we're joined by her sisters, and there's a lot of laughing, and I give a dramatic reading from the book Everybody Poops. + Nothing out of the ordinary. + And yet, it is extraordinary, because in my pain and my panic, in the homelessness of my humlessness, I have nothing to do but pay attention. + I focus. + I am still. + The nation I'm building, the marathon I'm running, the troops, the canvas, the high note does not exist. + All that exists are sticky fingers and gooey kisses and tiny voices and crayons and that song about letting go of whatever it is that Frozen girl needs to let go of. + It's all peace and simplicity. + The air is so rare in this place for me that I can barely breathe. + I can barely believe I'm breathing. + Play is the opposite of work. + And I am happy. + Something in me loosens. + A door in my brain swings open, and a rush of energy comes. + And it's not instantaneous, but it happens, it does happen. + I feel it. + A hum creeps back. + Not at full volume, barely there, it's quiet, and I have to stay very still to hear it, but it is there. + Not the hum, but a hum. + And now I feel like I know a very magical secret. + Well, let's not get carried away. + It's just love. That's all it is. + No magic. No secret. It's just love. + It's just something we forgot. + The hum, the work hum, the hum of the titan, that's just a replacement. + If I have to ask you who I am, if I have to tell you who I am, if I describe myself in terms of shows and hours of television and how globally badass my brain is, I have forgotten what the real hum is. + The hum is not power and the hum is not work-specific. + The hum is joy-specific. + The real hum is love-specific. + The hum is the electricity that comes from being excited by life. + The real hum is confidence and peace. + The real hum ignores the stare of history, and the balls in the air, and the expectation, and the pressure. + The real hum is singular and original. + The real hum is God's whisper in my ear, but maybe God was whispering the wrong words, because which one of the gods was telling me I was the titan? + It's just love. + We could all use a little more love, a lot more love. + Any time my child asks me to play, I will say yes. + I make it a firm rule for one reason, to give myself permission, to free me from all of my workaholic guilt. + It's a law, so I don't have a choice, and I don't have a choice, not if I want to feel the hum. + I wish it were that easy, but I'm not good at playing. + I'm not interested in doing it the way I'm interested in doing work. + The truth is incredibly humbling and humiliating to face. + I don't like playing. + I work all the time because I like working. + I like working more than I like being at home. + Facing that fact is incredibly difficult to handle, because what kind of person likes working more than being at home? + Well, me. + I mean, let's be honest, I call myself a titan. + I've got issues. + And one of those issues isn't that I am too relaxed. + We run around the yard, up and back and up and back. + We have 30-second dance parties. + We sing show tunes. We play with balls. + I blow bubbles and they pop them. + And I feel stiff and delirious and confused most of the time. + I itch for my cell phone always. + But it is OK. + My tiny humans show me how to live and the hum of the universe fills me up. + I play and I play until I begin to wonder why we ever stop playing in the first place. + You can do it too, say yes every time your child asks you to play. + Are you thinking that maybe I'm an idiot in diamond shoes? + You're right, but you can still do this. + You have time. + You know why? Because you're not Rihanna and you're not a Muppet. + Your child does not think you're that interesting. + You only need 15 minutes. + My two- and four-year-old only ever want to play with me for about 15 minutes or so before they think to themselves they want to do something else. + It's an amazing 15 minutes, but it's 15 minutes. + If I'm not a ladybug or a piece of candy, I'm invisible after 15 minutes. + And my 13-year-old, if I can get a 13-year-old to talk to me for 15 minutes I'm Parent of the Year. + 15 minutes is all you need. + I can totally pull off 15 minutes of uninterrupted time on my worst day. + Uninterrupted is the key. + No cell phone, no laundry, no anything. + You have a busy life. You have to get dinner on the table. + You have to force them to bathe. But you can do 15 minutes. + My kids are my happy place, they're my world, but it doesn't have to be your kids, the fuel that feeds your hum, the place where life feels more good than not good. + It's not about playing with your kids, it's about joy. + It's about playing in general. + Give yourself the 15 minutes. + Find what makes you feel good. + Just figure it out and play in that arena. + I'm not perfect at it. In fact, I fail as often as I succeed, seeing friends, reading books, staring into space. + "Wanna play?" starts to become shorthand for indulging myself in ways I'd given up on right around the time I got my first TV show, right around the time I became a titan-in-training, right around the time I started competing with myself for ways unknown. + 15 minutes? What could be wrong with giving myself my full attention for 15 minutes? + Turns out, nothing. + The very act of not working has made it possible for the hum to return, as if the hum's engine could only refuel while I was away. + Work doesn't work without play. + It takes a little time, but after a few months, one day the floodgates open and there's a rush, and I find myself standing in my office filled with an unfamiliar melody, full on groove inside me, and around me, and it sends me spinning with ideas, and the humming road is open, and I can drive it and drive it, and I love working again. + But now, I like that hum, but I don't love that hum. + I don't need that hum. + I am not that hum. That hum is not me, not anymore. + I am bubbles and sticky fingers and dinners with friends. + I am that hum. + Life's hum. + Love's hum. + Work's hum is still a piece of me, it is just no longer all of me, and I am so grateful. + And I don't give a crap about being a titan, because I have never once seen a titan play Red Rover, Red Rover. + I said yes to less work and more play, and somehow I still run my world. + My brain is still global. My campfires still burn. + The more I play, the happier I am, and the happier my kids are. + The more I play, the more I feel like a good mother. + The more I play, the freer my mind becomes. + The more I play, the better I work. + The more I play, the more I feel the hum, the nation I'm building, the marathon I'm running, the troops, the canvas, the high note, the hum, the hum, the other hum, the real hum, life's hum. + The more I feel that hum, the more this strange, quivering, uncocooned, awkward, brand new, alive non-titan feels like me. + The more I feel that hum, the more I know who I am. + I'm a writer, I make stuff up, I imagine. + That part of the job, that's living the dream. + That's the dream of the job. + Because a dream job should be a little bit dreamy. + I said yes to less work and more play. + Titans need not apply. + Wanna play? + Thank you. + + +http://www.ted.com/talks/jocelyne_bloch_the_brain_may_be_able_to_repair_itself_with_help +TED Talk Subtitles and Transcript: Through treating everything from strokes to car accident traumas, neurosurgeon Jocelyne Bloch knows the brain's inability to repair itself all too well. But now, she suggests, she and her colleagues may have found the key to neural repair: Doublecortin-positive cells. Similar to stem cells, they are extremely adaptable and, when extracted from a brain, cultured and then re-injected in a lesioned area of the same brain, they can help repair and rebuild it. "With a little help," Bloch says, "the brain may be able to help itself." +talks, Surgery, brain, health, medical research, medicine, mind, neuroscience, science +2429 +Jocelyne Bloch: The brain may be able to repair itself -- with help + + + So I'm a neurosurgeon. + And like most of my colleagues, I have to deal, every day, with human tragedies. + I realize how your life can change from one second to the other after a major stroke or after a car accident. + And what is very frustrating for us neurosurgeons is to realize that unlike other organs of the body, the brain has very little ability for self-repair. + And after a major injury of your central nervous system, the patients often remain with a severe handicap. + And that's probably the reason why I've chosen to be a functional neurosurgeon. + What is a functional neurosurgeon? + It's a doctor who is trying to improve a neurological function through different surgical strategies. + You've certainly heard of one of the famous ones called deep brain stimulation, where you implant an electrode in the depths of the brain in order to modulate a circuit of neurons to improve a neurological function. + It's really an amazing technology in that it has improved the destiny of patients with Parkinson's disease, with severe tremor, with severe pain. + However, neuromodulation does not mean neuro-repair. + And the dream of functional neurosurgeons is to repair the brain. + I think that we are approaching this dream. + And I would like to show you that we are very close to this. + And that with a little bit of help, the brain is able to help itself. + So the story started 15 years ago. + At that time, I was a chief resident working days and nights in the emergency room. + I often had to take care of patients with head trauma. + You have to imagine that when a patient comes in with a severe head trauma, his brain is swelling and he's increasing his intracranial pressure. + And in order to save his life, you have to decrease this intracranial pressure. + you sometimes have to remove a piece of swollen brain. + So instead of throwing away these pieces of swollen brain, we decided with Jean-François Brunet, who is a colleague of mine, a biologist, to study them. + What do I mean by that? + We wanted to grow cells from these pieces of tissue. + It's not an easy task. + Growing cells from a piece of tissue is a bit the same as growing very small children out from their family. + So you need to find the right nutrients, the warmth, the humidity and all the nice environments to make them thrive. + So that's exactly what we had to do with these cells. + And after many attempts, Jean-François did it. + And that's what he saw under his microscope. + And that was, for us, a major surprise. + Why? + Because this looks exactly the same as a stem cell culture, with large green cells surrounding small, immature cells. + And you may remember from biology class that stem cells are immature cells, able to turn into any type of cell of the body. + The adult brain has stem cells, but they're very rare and they're located in deep and small niches in the depths of the brain. + So it was surprising to get this kind of stem cell culture from the superficial part of swollen brain we had in the operating theater. + And there was another intriguing observation: Regular stem cells are very active cells -- cells that divide, divide, divide very quickly. + And they never die, they're immortal cells. + But these cells behave differently. + They divide slowly, and after a few weeks of culture, they even died. + So we were in front of a strange new cell population that looked like stem cells but behaved differently. + And it took us a long time to understand where they came from. + They come from these cells. + These blue and red cells are called doublecortin-positive cells. + All of you have them in your brain. + They represent four percent of your cortical brain cells. + They have a very important role during the development stage. + When you were fetuses, they helped your brain to fold itself. + But why do they stay in your head? + This, we don't know. + We think that they may participate in brain repair because we find them in higher concentration close to brain lesions. + But it's not so sure. + But there is one clear thing -- that from these cells, we got our stem cell culture. + And we were in front of a potential new source of cells to repair the brain. + And we had to prove this. + we decided to design an experimental paradigm. + The idea was to biopsy a piece of brain in a non-eloquent area of the brain, and then to culture the cells exactly the way Jean-François did it in his lab. + And then label them, to put color in them in order to be able to track them in the brain. + And the last step was to re-implant them in the same individual. + We call these autologous grafts -- autografts. + So the first question we had, "What will happen if we re-implant these cells in a normal brain, and what will happen if we re-implant the same cells in a lesioned brain?" + Thanks to the help of professor Eric Rouiller, we worked with monkeys. + So in the first-case scenario, we re-implanted the cells in the normal brain and what we saw is that they completely disappeared after a few weeks, as if they were taken from the brain, they go back home, the space is already busy, they are not needed there, so they disappear. + In the second-case scenario, we performed the lesion, we re-implanted exactly the same cells, and in this case, the cells remained -- and they became mature neurons. + And that's the image of what we could observe under the microscope. + Those are the cells that were re-implanted. + And the proof they carry, these little spots, those are the cells that we've labeled in vitro, when they were in culture. + But we could not stop here, of course. + Do these cells also help a monkey to recover after a lesion? + So for that, we trained monkeys to perform a manual dexterity task. + They had to retrieve food pellets from a tray. + They were very good at it. + And when they had reached a plateau of performance, we did a lesion in the motor cortex corresponding to the hand motion. + So the monkeys were plegic, they could not move their hand anymore. + And exactly the same as humans would do, they spontaneously recovered to a certain extent, exactly the same as after a stroke. + Patients are completely plegic, and then they try to recover due to a brain plasticity mechanism, they recover to a certain extent, exactly the same for the monkey. + So when we were sure that the monkey had reached his plateau of spontaneous recovery, we implanted his own cells. + So on the left side, you see the monkey that has spontaneously recovered. + He's at about 40 to 50 percent of his previous performance before the lesion. + He's not so accurate, not so quick. + And look now, when we re-impant the cells: Two months after re-implantation, the same individual. + It was also very exciting results for us, I tell you. + Since that time, we've understood much more about these cells. + We know that we can cryopreserve them, we can use them later on. + We know that we can apply them in other neuropathological models, like Parkinson's disease, for example. + But our dream is still to implant them in humans. + And I really hope that I'll be able to show you soon that the human brain is giving us the tools to repair itself. + Thank you. + Bruno Giussani: Jocelyne, this is amazing, and I'm sure that right now, there are several dozen people in the audience, possibly even a majority, who are thinking, "I know somebody who can use this." + I do, in any case. + And of course the question is, what are the biggest obstacles before you can go into human clinical trials? + Jocelyne Bloch: The biggest obstacles are regulations. So, from these exciting results, you need to fill out about two kilograms of papers and forms to be able to go through these kind of trials. + BG: Which is understandable, the brain is delicate, etc. + JB: Yes, it is, but it takes a long time and a lot of patience and almost a professional team to do it, you know? + BG: If you project yourself -- having done the research and having tried to get permission to start the trials, if you project yourself out in time, how many years before somebody gets into a hospital and this therapy is available? + JB: So, it's very difficult to say. + It depends, first, on the approval of the trial. + Will the regulation allow us to do it soon? + And then, you have to perform this kind of study in a small group of patients. + So it takes, already, a long time to select the patients, do the treatment and evaluate if it's useful to do this kind of treatment. + And then you have to deploy this to a multicentric trial. + You have to really prove first that it's useful before offering this treatment up for everybody. + BG: And safe, of course. JB: Of course. + BG: Jocelyne, thank you for coming to TED and sharing this. + BG: Thank you. + + +http://www.ted.com/talks/yanis_varoufakis_capitalism_will_eat_democracy_unless_we_speak_up +TED Talk Subtitles and Transcript: Have you wondered why politicians aren't what they used to be, why governments seem unable to solve real problems? Economist Yanis Varoufakis, the former Minister of Finance for Greece, says that it's because you can be in politics today but not be in power -- because real power now belongs to those who control the economy. He believes that the mega-rich and corporations are cannibalizing the political sphere, causing financial crisis. In this talk, hear his dream for a world in which capital and labor no longer struggle against each other, "one that is simultaneously libertarian, Marxist and Keynesian." +talks, Europe, United States, activism, big problems, business, capitalism, democracy, economics, finance, global issues, government, investment, leadership, money, politics, society +2413 +Yanis Varoufakis: Capitalism will eat democracy -- unless we speak up + + + Democracy. + In the West, we make a colossal mistake taking it for granted. + We see democracy not as the most fragile of flowers that it really is, but we see it as part of our society's furniture. + We tend to think of it as an intransigent given. + We mistakenly believe that capitalism begets inevitably democracy. + It doesn't. + Singapore's Lee Kuan Yew and his great imitators in Beijing that it is perfectly possible to have a flourishing capitalism, spectacular growth, while politics remains democracy-free. + Indeed, democracy is receding in our neck of the woods, here in Europe. + Earlier this year, while I was representing Greece -- the newly elected Greek government -- in the Eurogroup as its Finance Minister, I was told in no uncertain terms that our nation's democratic process -- our elections -- could not be allowed to interfere with economic policies that were being implemented in Greece. + At that moment, I felt that there could be no greater vindication of Lee Kuan Yew, or the Chinese Communist Party, indeed of some recalcitrant friends of mine who kept telling me that democracy would be banned if it ever threatened to change anything. + Tonight, here, I want to present to you an economic case for an authentic democracy. + I want to ask you to join me in believing again that Lee Kuan Yew, the Chinese Communist Party and indeed the Eurogroup are wrong in believing that we can dispense with democracy -- that we need an authentic, boisterous democracy. + And without democracy, our societies will be nastier, our future bleak and our great, new technologies wasted. + Speaking of waste, allow me to point out an interesting paradox that is threatening our economies as we speak. + I call it the twin peaks paradox. + One peak you understand -- you know it, you recognize it -- is the mountain of debts that has been casting a long shadow over the United States, Europe, the whole world. + We all recognize the mountain of debts. + But few people discern its twin. + A mountain of idle cash belonging to rich savers and to corporations, too terrified to invest it into the productive activities that can generate the incomes from which you can extinguish the mountain of debts and which can produce all those things that humanity desperately needs, like green energy. + Now let me give you two numbers. + Over the last three months, in the United States, in Britain and in the Eurozone, we have invested, collectively, 3.4 trillion dollars on all the wealth-producing goods -- things like industrial plants, machinery, office blocks, schools, roads, railways, machinery, and so on and so forth. + $3.4 trillion sounds like a lot of money until you compare it to the $5.1 trillion that has been slushing around in the same countries, in our financial institutions, doing absolutely nothing during the same period except inflating stock exchanges and bidding up house prices. + So a mountain of debt and a mountain of idle cash form twin peaks, failing to cancel each other out through the normal operation of the markets. + The result is stagnant wages, more than a quarter of 25- to 54-year-olds in America, in Japan and in Europe And consequently, low aggregate demand, which in a never-ending cycle, reinforces the pessimism of the investors, who, fearing low demand, reproduce it by not investing -- exactly like Oedipus' father, who, terrified by the prophecy of the oracle that his son would grow up to kill him, + unwittingly engineered the conditions that ensured that Oedipus, his son, would kill him. + This is my quarrel with capitalism. + Its gross wastefulness, all this idle cash, should be energized to improve lives, to develop human talents, and indeed to finance all these technologies, green technologies, which are absolutely essential for saving planet Earth. + Am I right in believing that democracy might be the answer? + I believe so, but before we move on, what do we mean by democracy? + Aristotle defined democracy as the constitution in which the free and the poor, being in the majority, control government. + Now, of course Athenian democracy excluded too many. + Women, migrants and, of course, the slaves. + But it would be a mistake to dismiss the significance of ancient Athenian democracy on the basis of whom it excluded. + What was more pertinent, and continues to be so about ancient Athenian democracy, was the inclusion of the working poor, who not only acquired the right to free speech, but more importantly, crucially, they acquired the rights to political judgments that were afforded equal weight in the decision-making concerning matters of state. + Now, of course, Athenian democracy didn't last long. + Like a candle that burns brightly, it burned out quickly. + And indeed, our liberal democracies today do not have their roots in ancient Athens. + They have their roots in the Magna Carta, in the 1688 Glorious Revolution, indeed in the American constitution. + Whereas Athenian democracy was focusing on the masterless citizen and empowering the working poor, our liberal democracies are founded on the Magna Carta tradition, which was, after all, a charter for masters. + And indeed, liberal democracy only surfaced when it was possible to separate fully the political sphere from the economic sphere, so as to confine the democratic process fully in the political sphere, leaving the economic sphere -- the corporate world, if you want -- as a democracy-free zone. + Now, in our democracies today, this separation of the economic from the political sphere, it gave rise to an inexorable, epic struggle between the two, with the economic sphere colonizing the political sphere, eating into its power. + Have you wondered why politicians are not what they used to be? + It's not because their DNA has degenerated. + It is rather because one can be in government today and not in power, because power has migrated from the political to the economic sphere, which is separate. + I spoke about my quarrel with capitalism. + If you think about it, it is a little bit like a population of predators, that are so successful in decimating the prey that they must feed on, that in the end they starve. + Similarly, the economic sphere has been colonizing and cannibalizing the political sphere to such an extent that it is undermining itself, Corporate power is increasing, political goods are devaluing, inequality is rising, aggregate demand is falling and CEOs of corporations are too scared to invest the cash of their corporations. + So the more capitalism succeeds in taking the demos out of democracy, the taller the twin peaks and the greater the waste of human resources and humanity's wealth. + Clearly, if this is right, we must reunite the political and economic spheres and better do it with a demos being in control, like in ancient Athens except without the slaves or the exclusion of women and migrants. + Now, this is not an original idea. + The Marxist left had that idea 100 years ago and it didn't go very well, did it? + The lesson that we learned from the Soviet debacle is that only by a miracle will the working poor be reempowered, as they were in ancient Athens, without creating new forms of brutality and waste. + But there is a solution: eliminate the working poor. + Capitalism's doing it by replacing low-wage workers with automata, androids, robots. + The problem is that as long as the economic and the political spheres are separate, automation makes the twin peaks taller, the waste loftier and the social conflicts deeper, including -- soon, I believe -- in places like China. + So we need to reconfigure, we need to reunite the economic and the political spheres, but we'd better do it by democratizing the reunified sphere, lest we end up with a surveillance-mad hyperautocracy that makes The Matrix, the movie, look like a documentary. + So the question is not whether capitalism will survive the technological innovations it is spawning. + The more interesting question is whether capitalism will be succeeded by something resembling a Matrix dystopia or something much closer to a Star Trek-like society, where machines serve the humans and the humans expend their energies exploring the universe and indulging in long debates about the meaning of life in some ancient, Athenian-like, high tech agora. + I think we can afford to be optimistic. + But what would it take, what would it look like to have this Star Trek-like utopia, instead of the Matrix-like dystopia? + In practical terms, allow me to share just briefly, a couple of examples. + At the level of the enterprise, imagine a capital market, where you earn capital as you work, and where your capital follows you from one job to another, from one company to another, and the company -- whichever one you happen to work at at that time -- is solely owned by those who happen to work in it at that moment. + Then all income stems from capital, from profits, and the very concept of wage labor becomes obsolete. + No more separation between those who own but do not work in the company and those who work but do not own the company; no more tug-of-war between capital and labor; no great gap between investment and saving; indeed, no towering twin peaks. + At the level of the global political economy, imagine for a moment that our national currencies have a free-floating exchange rate, with a universal, global, digital currency, one that is issued by the International Monetary Fund, on behalf of all humanity. + And imagine further that all international trade is denominated in this currency -- let's call it "the cosmos," in units of cosmos -- with every government agreeing to be paying into a common fund a sum of cosmos units proportional to the country's trade deficit, or indeed to a country's trade surplus. + And imagine that that fund is utilized to invest in green technologies, especially in parts of the world where investment funding is scarce. + This is not a new idea. + It's what, effectively, John Maynard Keynes proposed in 1944 at the Bretton Woods Conference. + The problem is that back then, they didn't have the technology to implement it. + Now we do, especially in the context of a reunified political-economic sphere. + The world that I am describing to you is simultaneously libertarian, in that it prioritizes empowered individuals, Marxist, since it will have confined to the dustbin of history the division between capital and labor, and Keynesian, global Keynesian. + But above all else, it is a world in which we will be able to imagine an authentic democracy. + Will such a world dawn? + Or shall we descend into a Matrix-like dystopia? + The answer lies in the political choice that we shall be making collectively. + It is our choice, and we'd better make it democratically. + Thank you. + Bruno Giussani: Yanis ... + It was you who described yourself in your bios as a libertarian Marxist. + What is the relevance of Marx's analysis today? + Yanis Varoufakis: Well, if there was any relevance in what I just said, then Marx is relevant. + Because the whole point of reunifying the political and economic is -- if we don't do it, then technological innovation is going to create such a massive fall in aggregate demand, what Larry Summers refers to as secular stagnation. + With this crisis migrating from one part of the world, as it is now, it will destabilize not only our democracies, but even the emerging world that is not that keen on liberal democracy. + So if this analysis holds water, then Marx is absolutely relevant. + But so is Hayek, that's why I'm a libertarian Marxist, and so is Keynes, so that's why I'm totally confused. + BG: Indeed, and possibly we are too, now. + YV: If you are not confused, you are not thinking, OK? + BG: That's a very, very Greek philosopher kind of thing to say -- YV: That was Einstein, actually -- BG: During your talk you mentioned Singapore and China, and last night at the speaker dinner, you expressed a pretty strong opinion about how the West looks at China. + Would you like to share that? + YV: Well, there's a great degree of hypocrisy. + In our liberal democracies, we have a semblance of democracy. + It's because we have confined, as I was saying in my talk, democracy to the political sphere, while leaving the one sphere where all the action is -- the economic sphere -- a completely democracy-free zone. + In a sense, if I am allowed to be provocative, China today is closer to Britain in the 19th century. + Because remember, we tend to associate liberalism with democracy -- that's a mistake, historically. + Liberalism, liberal, it's like John Stuart Mill. + John Stuart Mill was particularly skeptical about the democratic process. + So what you are seeing now in China is a very similar process to the one that we had in Britain during the Industrial Revolution, especially the transition from the first to the second. + And to be castigating China for doing that which the West did in the 19th century, smacks of hypocrisy. + BG: I am sure that many people here are wondering about your experience as the Finance Minister of Greece earlier this year. + YV: I knew this was coming. + BG: Yes. + BG: Six months after, how do you look back at the first half of the year? + YV: Extremely exciting, from a personal point of view, and very disappointing, because we had an opportunity to reboot the Eurozone. + Not just Greece, the Eurozone. + To move away from the complacency and the constant denial that there was a massive -- and there is a massive architectural fault line going through the Eurozone, which is threatening, massively, the whole of the European Union process. + We had an opportunity on the basis of the Greek program -- was the first program to manifest that denial -- to put it right. + And, unfortunately, the powers in the Eurozone, in the Eurogroup, chose to maintain denial. + But you know what happens. + This is the experience of the Soviet Union. + When you try to keep alive an economic system that architecturally cannot survive, through political will and through authoritarianism, you may succeed in prolonging it, but when change happens it happens very abruptly and catastrophically. + BG: What kind of change are you foreseeing? + YV: Well, there's no doubt that if we don't change the architecture of the Eurozone, the Eurozone has no future. + BG: Did you make any mistakes when you were Finance Minister? + YV: Every day. + BG: For example? YV: Anybody who looks back -- No, but seriously. + If there's any Minister of Finance, or of anything else for that matter, who tells you after six months in a job, especially in such a stressful situation, that they have made no mistake, they're dangerous people. + Of course I made mistakes. + The greatest mistake was to sign the application for the extension of a loan agreement in the end of February. + I was imagining that there was a genuine interest on the side of the creditors to find common ground. + And there wasn't. + They were simply interested in crushing our government, just because they did not want to have to deal with the architectural fault lines that were running through the Eurozone. + And because they didn't want to admit that for five years they were implementing a catastrophic program in Greece. + We lost one-third of our nominal GDP. + This is worse than the Great Depression. + And no one has come clean from the troika of lenders that have been imposing this policy to say, "This was a colossal mistake." + BG: Despite all this, and despite the aggressiveness of the discussion, you seem to be remaining quite pro-European. + YV: Absolutely. + Look, my criticism of the European Union and the Eurozone comes from a person who lives and breathes Europe. + My greatest fear is that the Eurozone will not survive. + Because if it doesn't, the centrifugal forces that will be unleashed and they will destroy the European Union. + And that will be catastrophic not just for Europe but for the whole global economy. + We are probably the largest economy in the world. + And if we allow ourselves to fall into a route of the postmodern 1930's, which seems to me to be what we are doing, then that will be detrimental to the future of Europeans and non-Europeans alike. + BG: We definitely hope you are wrong on that point. + Yanis, thank you for coming to TED. + YV: Thank you. + + +http://www.ted.com/talks/sebastian_wernicke_how_to_use_data_to_make_a_hit_tv_show +TED Talk Subtitles and Transcript: Does collecting more data lead to better decision-making? Competitive, data-savvy companies like Amazon, Google and Netflix have learned that data analysis alone doesn't always produce optimum results. In this talk, data scientist Sebastian Wernicke breaks down what goes wrong when we make decisions based purely on data -- and suggests a brainier way to use it. +talks, TEDx, algorithm, brain, data, decision-making, intelligence, media, technology +2403 +Sebastian Wernicke: How to use data to make a hit TV show + + + Roy Price is a man that most of you have probably never heard about, even though he may have been responsible for 22 somewhat mediocre minutes of your life on April 19, 2013. + He may have also been responsible for 22 very entertaining minutes, but not very many of you. + And all of that goes back to a decision that Roy had to make about three years ago. + So you see, Roy Price is a senior executive with Amazon Studios. + That's the TV production company of Amazon. + He's 47 years old, slim, spiky hair, describes himself on Twitter as "movies, TV, technology, tacos." + And Roy Price has a very responsible job, because it's his responsibility to pick the shows, the original content that Amazon is going to make. + And of course that's a highly competitive space. + I mean, there are so many TV shows already out there, that Roy can't just choose any show. + He has to find shows that are really, really great. + So in other words, he has to find shows that are on the very right end of this curve here. + So this curve here is the rating distribution of about 2,500 TV shows on the website IMDB, and the rating goes from one to 10, and the height here shows you how many shows get that rating. + So if your show gets a rating of nine points or higher, that's a winner. + Then you have a top two percent show. + That's shows like "Breaking Bad," "Game of Thrones," "The Wire," so all of these shows that are addictive, whereafter you've watched a season, your brain is basically like, "Where can I get more of these episodes?" + That kind of show. + On the left side, just for clarity, here on that end, you have a show called "Toddlers and Tiaras" -- -- which should tell you enough about what's going on on that end of the curve. + Now, Roy Price is not worried about getting on the left end of the curve, because I think you would have to have some serious brainpower to undercut "Toddlers and Tiaras." + So what he's worried about is this middle bulge here, the bulge of average TV, you know, those shows that aren't really good or really bad, they don't really get you excited. + So he needs to make sure that he's really on the right end of this. + So the pressure is on, and of course it's also the first time that Amazon is even doing something like this, so Roy Price does not want to take any chances. + He wants to engineer success. + He needs a guaranteed success, and so what he does is, he holds a competition. + So he takes a bunch of ideas for TV shows, and from those ideas, through an evaluation, they select eight candidates for TV shows, and then he just makes the first episode of each one of these shows and puts them online for free for everyone to watch. + And so when Amazon is giving out free stuff, you're going to take it, right? + So millions of viewers are watching those episodes. + What they don't realize is that, while they're watching their shows, actually, they are being watched. + They are being watched by Roy Price and his team, who record everything. + They record when somebody presses play, when somebody presses pause, what parts they skip, what parts they watch again. + So they collect millions of data points, because they want to have those data points to then decide which show they should make. + And sure enough, so they collect all the data, they do all the data crunching, and an answer emerges, and the answer is, "Amazon should do a sitcom about four Republican US Senators." + They did that show. + So does anyone know the name of the show? + Yes, "Alpha House," but it seems like not too many of you here remember that show, actually, because it didn't turn out that great. + It's actually just an average show, actually -- literally, in fact, because the average of this curve here is at 7.4, and "Alpha House" lands at 7.5, so a slightly above average show, but certainly not what Roy Price and his team were aiming for. + Meanwhile, however, at about the same time, at another company, another executive did manage to land a top show using data analysis, and his name is Ted, Ted Sarandos, who is the Chief Content Officer of Netflix, and just like Roy, he's on a constant mission to find that great TV show, and he uses data as well to do that, except he does it a little bit differently. + So instead of holding a competition, what he did -- and his team of course -- was they looked at all the data they already had about Netflix viewers, you know, the ratings they give their shows, the viewing histories, what shows people like, and so on. + And then they use that data to discover all of these little bits and pieces about the audience: what kinds of shows they like, what kind of producers, what kind of actors. + And once they had all of these pieces together, they took a leap of faith, and they decided to license not a sitcom about four Senators but a drama series about a single Senator. + You guys know the show? + Yes, "House of Cards," and Netflix of course, nailed it with that show, at least for the first two seasons. + "House of Cards" gets a 9.1 rating on this curve, so it's exactly where they wanted it to be. + Now, the question of course is, what happened here? + So you have two very competitive, data-savvy companies. + They connect all of these millions of data points, and then it works beautifully for one of them, and it doesn't work for the other one. + So why? + Because logic kind of tells you that this should be working all the time. + I mean, if you're collecting millions of data points on a decision you're going to make, then you should be able to make a pretty good decision. + You have 200 years of statistics to rely on. + You're amplifying it with very powerful computers. + The least you could expect is good TV, right? + And if data analysis does not work that way, then it actually gets a little scary, because we live in a time where we're turning to data more and more to make very serious decisions that go far beyond TV. + Does anyone here know the company Multi-Health Systems? + No one. OK, that's good actually. + OK, so Multi-Health Systems is a software company, and I hope that nobody here in this room ever comes into contact with that software, because if you do, it means you're in prison. + If someone here in the US is in prison, and they apply for parole, then it's very likely that data analysis software from that company will be used in determining whether to grant that parole. + So it's the same principle as Amazon and Netflix, but now instead of deciding whether a TV show is going to be good or bad, you're deciding whether a person is going to be good or bad. + And mediocre TV, 22 minutes, that can be pretty bad, but more years in prison, I guess, even worse. + And unfortunately, there is actually some evidence that this data analysis, despite having lots of data, does not always produce optimum results. + And that's not because a company like Multi-Health Systems doesn't know what to do with data. + Even the most data-savvy companies get it wrong. + Yes, even Google gets it wrong sometimes. + In 2009, Google announced that they were able, with data analysis, to predict outbreaks of influenza, the nasty kind of flu, by doing data analysis on their Google searches. + And it worked beautifully, and it made a big splash in the news, including the pinnacle of scientific success: a publication in the journal "Nature." + It worked beautifully for year after year after year, until one year it failed. + And nobody could even tell exactly why. + It just didn't work that year, and of course that again made big news, of a publication from the journal "Nature." + So even the most data-savvy companies, Amazon and Google, they sometimes get it wrong. + And despite all those failures, data is moving rapidly into real-life decision-making -- into the workplace, law enforcement, medicine. + So we should better make sure that data is helping. + Now, personally I've seen a lot of this struggle with data myself, because I work in computational genetics, which is also a field where lots of very smart people are using unimaginable amounts of data to make pretty serious decisions like deciding on a cancer therapy or developing a drug. + And over the years, I've noticed a sort of pattern or kind of rule, if you will, about the difference between successful decision-making with data and unsuccessful decision-making, and I find this a pattern worth sharing, and it goes something like this. + So whenever you're solving a complex problem, you're doing essentially two things. + The first one is, you take that problem apart into its bits and pieces so that you can deeply analyze those bits and pieces, You put all of these bits and pieces back together again to come to your conclusion. + And sometimes you have to do it over again, but it's always those two things: taking apart and putting back together again. + And now the crucial thing is that data and data analysis is only good for the first part. + Data and data analysis, no matter how powerful, can only help you taking a problem apart and understanding its pieces. + It's not suited to put those pieces back together again and then to come to a conclusion. + There's another tool that can do that, and we all have it, and that tool is the brain. + If there's one thing a brain is good at, it's taking bits and pieces back together again, even when you have incomplete information, and coming to a good conclusion, especially if it's the brain of an expert. + And that's why I believe that Netflix was so successful, because they used data and brains where they belong in the process. + They use data to first understand lots of pieces about their audience that they otherwise wouldn't have been able to understand at that depth, but then the decision to take all these bits and pieces and put them back together again and make a show like "House of Cards," that was nowhere in the data. + Ted Sarandos and his team made that decision to license that show, which also meant, by the way, that they were taking a pretty big personal risk with that decision. + And Amazon, on the other hand, they did it the wrong way around. + They used data all the way to drive their decision-making, first when they held their competition of TV ideas, then when they selected "Alpha House" to make as a show. + Which of course was a very safe decision for them, because they could always point at the data, saying, "This is what the data tells us." + But it didn't lead to the exceptional results that they were hoping for. + So data is of course a massively useful tool to make better decisions, but I believe that things go wrong when data is starting to drive those decisions. + No matter how powerful, data is just a tool, and to keep that in mind, I find this device here quite useful. + Many of you will ... + Before there was data, this was the decision-making device to use. + Many of you will know this. + This toy here is called the Magic 8 Ball, and it's really amazing, because if you have a decision to make, a yes or no question, all you have to do is you shake the ball, and then you get an answer -- "Most Likely" -- right here in this window in real time. + I'll have it out later for tech demos. + Now, the thing is, of course -- so I've made some decisions in my life where, in hindsight, I should have just listened to the ball. + But, you know, of course, if you have the data available, you want to replace this with something much more sophisticated, like data analysis to come to a better decision. + But that does not change the basic setup. + So the ball may get smarter and smarter and smarter, but I believe it's still on us to make the decisions if we want to achieve something extraordinary, on the right end of the curve. + And I find that a very encouraging message, in fact, that even in the face of huge amounts of data, it still pays off to make decisions, to be an expert in what you're doing and take risks. + Because in the end, it's not data, it's risks that will land you on the right end of the curve. + Thank you. + + +