Grad C: The twenty - ninth .
Grad A: OK .
Grad D: OK .
Grad C: That 's when I 'm meeting with Wolfgang Wahlster to sell him this idea .
Grad A: Mm - hmm . OK .
Grad C: OK ? Then I 'm also going to present a little talk at EML , about what we have done here and so of course , I 'm I 'm gonna start out with this slide , so the most relevant aspects of our stay here , and um , then I 'm asking them to imagine that they 're standing somewhere in Heidelberg and someone asks them in the morning The Cave Forty - Five is a is a well - known discotheque which is certainly not open at that that time . And so
Grad F: OK .
Grad C: they 're supposed to imagine that , you know , do they think the person wants to go there , or just know where it is ? Uh , which is probably not , uh , the case in that discotheque example , or in the Bavaria example , you just want to know where it is . And so forth . So basically we can make a point that here is ontological knowledge but if it 's nine nine PM in the evening then the discotheque question would be , for example , one that might ask for directions instead of just location . Um , and so forth and so forth . That 's sort of motivating it . Then what have we done so far ? We had our little bit of , um , um , SmartKom stuff , that we did , um , everth
Grad F: Oh , you 've got the parser done . Sorry .
Grad C: That 's the not the construction parser . That 's the , uh , tablet - based parser ,
Grad F: OK .
Grad A: Easy parser .
Grad F: OK .
Grad C: and the generation outputter .
Grad D: Halfway done ? Yeah .
Grad C: That 's done .
Grad A: Mmm .
Grad C: You have to change those strategies ,
Grad D: OK .
Grad C: right ? That 's , ten words ?
Grad D: Yeah . Well , i it , you know . Maybe twelve .
Grad C: Twelve ? OK . And , um , and Fey is doing the synthesis stuff as we speak . That 's all about that . Then I 'm going to talk about the data , you know these things about uh , actually I have an example , probably . Two s Can you hear that ? Or should I turn the l volume on .
Grad F: Mm - hmm .
Grad A: I could hear it .
Grad D: I I can hear it .
Grad F: I heard it .
Grad D: They might not hear it in the well maybe they will . I don't know .
Grad A: This was an actual , um , subject ? Ah .
Grad C: Mm - hmm .
Grad F: Sounds like Fey .
Grad A: Yeah .
Grad C: But they 're they 're mimicking the synthesis when they speak to the computer ,
Grad F: Oh , OK .
Grad C: the you can observe that all the time , they 're trying to match their prosody onto the machine .
Grad F: Oh really . Interesting . Oh , it 's pretty slow .
Grad C: Yeah , you have to Grad A: Wh
Grad F: The system breaking .
Grad A: What is the s ? Oh !
Grad C: OK . And so forth and so forth . Um , I will talk about our problems with the rephrasing , and how we solved it , and some preliminary observations , also , um , I 'm not gonna put in the figures from Liz , but I thought it would interesting to , uh , um , point out that it 's basically the same . Um , as in every human - human telephone conversation , and the human - computer telephone conversation is of course quite d quite different from , uh , some first , uh , observations . Then sort of feed you back to our original problem cuz , uh how to get there , what actually is happening there today , and then maybe talk about the big picture here , e tell a little bit as much as I can about the NTL story . I I wa I do wanna , um I 'm not quite sure about this , whether I should put this in , um , that , you know , you have these two sort of different ideas that are or two different camps of people envisioning how language understanding works , and then , talk a bit about the embodied and simulation approach favored here and as a prelude , I 'll talk about monkeys in Italy . And , um , Srini was gonna send me some slides but he didn't do it , so from but I have the paper , I can make a resume of that , and then I stole an X - schema from one of your talks I think .
Grad A: Oh . I was like , " where 'd you get that ? " OK .
Grad F: Yeah , that looks familiar .
Grad A: " Looks familiar . "
Grad C: I think that 's Bergen , Chang , something , or the other .
Grad A: Uh .
Professor E: Whatever .
Grad A: OK .
Grad C: Um , and that 's now I 'm not going to bring that . So that 's basically what I have , so far , and the rest is for airplanes . So X - schemas , then , I would like to do talk about the construction aspect and then at the end about our Bayes - net .
Grad A: Mm - hmm .
Grad C: End of story . Anything I forgot that we should mention ? Oh , maybe the FMRI stuff . Should I mention the fact that , um , we 're also actually started going to start to look at people 's brains in a more direct way ?
Professor E: You certainly can . I mean I y I you know , I don't know Grad A: You might just wanna like , tack that on , as a comment , to something .
Professor E: Right , um .
Grad C: " Future activities " something .
Professor E: Well , the time to mention it , if you mention it , is when you talk about mirror neurons , then you should talk about the more recent stuff , about the kicking
Grad C: Yeah .
Professor E: and , you know , the yeah , yeah and that the plan is to see to what extent the you 'll get the same phenomena with stories about this , so that Grad C: Mm - hmm .
Professor E: and that we 're planning to do this , um , which , we are . So that 's one thing . Um . Depends . I mean , there is a , um , whole language learning story , OK ?
Grad C: Yeah .
Professor E: which , uh , actually , i i even on your five - layer slide , you you 've got an old one that that leaves that off .
Grad C: Yeah , I I I do have it here .
Grad A: Hmm .
Professor E: Yeah .
Grad C: Um . And , of course , you know , the the big picture is this bit .
Professor E: Right .
Grad C: But , you know , it would But I don't think I I am capable of of do pulling this off and doing justice to the matter . I mean , there is interesting stuff in her terms of how language works , so the emergentism story would be nice to be you know , it would be nice to tell people how what 's happening there , plus how the , uh , language learning stuff works ,
Professor E: OK , so , so anyway , I I agree that 's not central .
Grad C: but Grad A: Mm - hmm .
Professor E: What you might wanna do is , um , and may not , but you might wanna this is rip off a bunch of the slides on the anal there the there we 've got various i generations of slides that show language analysis , and matching to the underlying image schemas , and , um , how the construction and simulation that ho that whole th
Grad C: Yeah , th that that 's c that comes up to the X - schema slide ,
Grad A: OK , right .
Grad C: so basically I 'm gonna steal that from Nancy ,
Grad A: OK , I can give you a more recent if you want Grad C: one of Nancy 's st
Grad A: well , that might have enough .
Grad C: Uh , I yeah , but I also have stuff you trash you left over ,
Professor E: OK .
Grad C: your quals and your triple - AI .
Professor E: The quals w the the the quals slides would be fine .
Grad A: Yeah .
Professor E: You could get it out of there , or some
Grad A: Which I can even email you then , you know , like there probably was a little few changes , not a big deal . Yeah , you could steal anything you want , I don't care . Which you 've already done , obviously . So . Sorry
Grad C: Well , I I don't feel bad about it at all
Grad A: No , you shouldn't .
Grad C: because because you are on the , uh , title .
Grad A: Oh , that 's great , that 's great .
Grad C: I mean on the the , you 're that 's see , that 's you .
Grad A: I 'm glad to see propagation .
Professor E: Yeah . Yeah .
Grad A: Mmm .
Grad C: Hmm ? Propagated ?
Grad A: Yes .
Grad C: I mean I might even mention that this work you 're doing is sort of also with the MPI in Leipzig , so .
Grad A: It 's it 's certainly related , um ,
Grad C: Because , um , EML is building up a huge thing in Leipzig .
Grad A: might wanna say . Is it ?
Grad C: So it It 's on biocomputation . Would Professor E: Yeah , it 's different , this is the , uh , DNA building , or someth the double helix building .
Grad C: Yeah .
Professor E: Yeah .
Grad A: Kind of a different level of analysis .
Professor E: The yeah it was it turns out that if if you have multiple billions of dollars , y you can do all sorts of weird things , and Grad D: Wait , they 're building a building in the shape of DNA ,
Grad A: What ?
Grad D: is that what you said ?
Professor E: Roughly , yeah .
Grad F: Oh ! Oh boy !
Grad A: O
Professor E: Including cr cross - bridges ,
Grad A: What ?
Professor E: and
Grad A: Oh my god !
Grad F: That 's brilliant ! Hhh .
Professor E: You d you really now I I spent the last time I was there I spent maybe two hours hearing this story which is , um Grad A: Of what
Grad D: Y You definitely wanna w don't wanna waste that money on research ,
Grad A: the building ?
Grad D: you know ?
Professor E: Right .
Grad D: That 's horrible .
Professor E: Right . Well , no , no , y i there 's infinite money . See you th you th you then fill it with researchers .
Grad A: And give them more money . They just want a fun place for them to to work .
Professor E: Right . Right .
Grad F: And everybody gets a trampoline in their office .
Grad C: Well , the the offices are actually a little the , think of um , ramps , coming out of the double helix and then you have these half - domes , glass half - domes , and the offices are in in the glass half - dome .
Grad A: Really ?
Professor E: Yeah .
Grad F: Alright , let 's stop talking about this .
Grad A: Does it exist yet ?
Professor E: Yeah .
Grad A: They are w now building it ?
Grad C: Uh , as a model .
Grad A: Hmm .
Grad C: But I th