Grad E: As usual .
Grad B: Yes . Whew ! I almost forgot about the meeting . I woke up twenty minutes ago , thinking , what did I forget ?
Grad D: It 's great how the br brain sort of does that .
Grad E: Something 's not right here .
Grad B: Internal alarms .
Grad D: OK . So the news for me is A , my forthcoming travel plans
Grad B: Yes .
Grad D: in two weeks from today ? Yeah ? More or less ? I 'll be off to Sicily and Germany for a couple , three days .
Grad B: Now what are y what are you doing there ? I forgot ?
Grad D: OK , I 'm flying to Sicily basically to drop off Simon there with his grandparents . And then I 'm flying to Germany t to go to a MOKU - Treffen which is the meeting of all the module - responsible people in SmartKom ,
Grad B: Mmm .
Grad D: and , represent ICI and myself I guess there . And um . That 's the mmm actual reason . And then I 'm also going up to EML for a day , and then I 'm going to meet the very big boss , Wolfgang Walster , in Saarbruecken and the System system integration people in Kaiserslautern and then I 'm flying back via Sicily pick up my son come back here on the fourth of July . And uh .
Grad E: What a great time to be coming back to the
Grad B: God bless America .
Grad E: You 'll see maybe see the fireworks from your plane coming in .
Grad D: And I 'm sure all the the people at the airport will be happy to work on that day .
Grad E: Yeah . You 'll get even better service than usual .
Grad B: Wait , aren't you flying on Lufthansa though ?
Grad D: Mm - hmm . Alitalia .
Grad B: Oh . Well then the you know , it 's not a big deal . Once you get to the United States it 'll be a problem , but
Grad D: Yeah . And um , that 's that bit of news , and the other bit of news is we had you know , uh , I was visited by my German project manager who A , did like what we did what we 're doing here , and B , is planning to come here either three weeks in July or three weeks in August , to actually work .
Grad B: On ?
Grad D: With us .
Grad B: Oh .
Grad D: And we sat around and we talked and he came up we came up with a pretty strange idea . And that 's what I 'm gonna lay on you now . And um , maybe it might be ultimately the most interesting thing for Eva because she has been known to complain about the fact that the stuff we do here is not weird enough .
Grad C: OK .
Grad D: So this is so weird it should even make you happy .
Grad C: Uh . {comment} OK .
Grad E: Oh great .
Grad D: Imagine if you will , that we have a system that does all that understanding that we want it to do based on utterances .
Grad B: Mm - hmm .
Grad D: It should be possible to make that system produce questions . So if you have the knowledge of how to interpret " where is X ? " under given conditions , situational , user , discourse and ontological conditions , you should also be able to make that same system ask " where is X ? "
Grad E: Mm - hmm .
Grad D: in a sper certain way , based on certain intentions . So in instead of just being able to observe phenomenon , um , and , guess the intention we might be able just to sort of give it an intention , and make it produce an utterance .
Grad E: Hmm .
Grad B: Well , like in AI they generally do the take in , and then they also do the generation phase , like Nancy 's thing . Or uh , you remember , in the the hand thing in one - eighty - two , like not only was it able to recognize but it was also to generate based upon situations . You mean that sort of thing ?
Grad D: Absolutely .
Grad B: OK .
Grad D: And once you 've done that what we can do is have the system ask itself . And answer , understand the answer , ask something else , and enter a dialogue with itself . So the the ba basic the same idea as having two chess computers play against each other .
Grad E: Except this smacks a little bit more of a schizophrenic computer than AI .
Grad D: Yeah you c if you want , you can have two parallel machines um , asking each other . What would that give us ? Would A be something completely weird and strange , and B , i if you look at all the factors , we will never observe people let 's say , in wheelchairs under you know , in under all conditions ,
Grad E: That 's good .
Grad D: you know , when they say " X " , and there is a ride at the goal , and the parking is good , we can never collect enough data . It 's it 's it 's not possible .
Grad E: Mm - hmm . Right , right .
Grad D: But maybe one could do some learning . If you get the system to speak to itself , you may find n break downs and errors and you may be able to learn . And make it more robust , maybe learn new things . And um , so there 's no no end of potential things one could get out of it , if that works . And he would like to actually work on that with us .
Grad B: Well then , he probably should be coming back a year from now .
Grad D: So Yeah , I w See the the generation bit , making the system generate generate something , {comment} is shouldn't be too hard .
Grad B: Well , once the system understands things .
Grad E: Yeah . No problem .
Grad B: I just don't think I think we 're probably a year away from getting the system to understand things .
Grad D: Yeah . Well , if we can get it to understand one thing , like our " where is " run through we can also , maybe , e make it say , or ask " where is X ? " Or not .
Grad E: Mmm , I don't know . e I 'm sort of have the impression that getting it to say the right thing in the right circumstances is much more difficult than getting it to understand something given the circumstances and so on , you know , I mean just cuz it 's sort of harder to learn to speak correctly in a foreign language , rather than learning to understand it . Right ? I mean
Grad D :
Grad E: just the fact that we 'll get The point is that getting it to understand one construction doesn't mean that it will n always know exactly when it 's correct to use that construction . Right ?
Grad D: It 's it 's uh Well , I 've I 've done generation and language production research for fo four four and a half years . And so it 's it 's you 're right , it 's not the same as the understanding . It 's in some ways easier and some ways harder . nuh ?
Grad E: Yeah .
Grad D: But , um , I think it 'd be fun to look at it , or into that question .
Grad E: Nnn , yeah .
Grad D: It 's a pretty strange idea . And so that 's that 's But Grad B: The basic idea I guess would be to give allow the system to have intentions , basically ? Cuz that 's basically what needs to be added to the system for it .
Grad D: Well , look at th eee , I think even think even What it would be the the prior intention . So let 's uh uh , let 's say we have this Grad B: Well we 'd have to seed that , I mean .
Grad D: No . Let 's we have to we have some some top - down processing , given certain setting . OK , now we change nothing , and just say ask something . Right ?
Grad B :
Grad D: What would it ask ?
Grad B: It wouldn't know what to ask . I mean .
Grad D: It shur
Grad B: Unless it was in a situation . We 'd have to set up a situation where , it didn't know where something was and it wanted to go there .
Grad D: Yeah !
Grad C: Mm - hmm .
Grad D: Yeah .
Grad B: Which means that we 'd need to set up an intention inside of the system . Right ? Which is basically , " I don't know where something is and I need to go there " .
Grad D: Eh , n
Grad E: Yeah .
Grad D: Ooh , do we really need to do that ? Because ,
Grad B: Well , no I guess not . Excel
Grad D: s It 's i I know it 's it 's strange , but look at it look at our Bayes - net . If we don't have Let 's assume we don't have any input from the language . Right ? So there 's also nothing we could query the ontology , but we have a certain user setting . If you just ask , what is the likelihood of that person wanting to enter some something , it 'll give you an answer .
Grad B: Sure .
Grad D: Right ? That 's just how they are . And so , @ @ whatever that is , it 's the generic default intention . That it would find out . Which is , wanting to know where something is , maybe nnn and wanting I don't know what it 's gonna be , but there 's gonna be something that
Grad E: Well you 're not gonna are you gonna get a variety of intentions out of that then ? I mean , you 're just talking about like given this user , what 's the th what is it what is that user most likely to want to do ?
Grad D: Well you can observe some user and context stuff and ask , what 's the posterior probabilities of all of our decision nodes .
Grad E: And , have it talk about OK .
Grad D: You could even say , " let 's take all the priors , let 's observe nothing " , and query all the posterior probabilities . It - it 's gonna tell us something . Right ?
Grad B: Well , it will d r assign values to all the nodes . Yes .
Grad D: And Yes . And come up with posterior probabilities for all the values of the decision nodes . Which , if we have an algorithm that filters out whatever the the best or the most consistent answer out of that , will give us the intention ex nihilo . And that is exactly what would happen if we ask it to produce an utterance , it would be b based on that extension , ex nihilo , which we don't know what it is , but it 's there . So we wouldn't even have to t to kick start it by giving it a certain intention or observing anything on the decision node . And whatever that maybe that would lead to " what is the castle ? " ,
Grad B: I 'm just Grad D: or " what is that whatever " .
Grad B: I guess what I 'm afraid of is if we don't , you know , set up a situation , {comment} we 'll just get a bunch of garbage out , like you know , everything 's exactly thirty percent .
Grad D: No Grad C: Mmm .
Grad D: Yeah . So what we actually then need to do is is write a little script that changes all the settings , you know , go goes through all the permutations , which is we did a didn't we calculate that once ?
Grad B: Well that was that was absurdly low , in the last meeting ,
Grad D: It 's a Grad C: Uh ,
Grad B: cuz I went and looked at it cuz I was thinking , that could not be right , and it would it was on the order of twenty output nodes and something like twenty Grad C: And like thirty input nodes
Grad B: thirty input nodes .
Grad C: or some Grad B: So to test every output node , uh , would at least Let 's see , so it would be two to the thirty for every output node ? Which is very th very large .
Grad D: Oh ! That 's n
Grad E: Oh .
Grad D: that 's that 's nothing for those neural guys . I mean , they train for millions and millions of epochs .
Grad B: Well , I 'm talking about
Grad D: So .
Grad B: Oh , I was gonna take a drink of my water . I 'm talking about billions and billions and billions and a number two to the thirty is like a Bhaskara said , we had calculated out and Bhaskara believes that it 's larger than the number of particles in the universe . And if i
Grad E: I don't know if that 's right or not . Th - that 's big . That 's just That 's uh It 's a billion , right ?
Grad B: Two to the thirty ? Well , two to the thirty is a billion , but if we have to do it two to the twenty times , then that 's a very very large number .
Grad E: Right . Argh . Oh , OK . Yeah . Yeah , that 's big .
Grad B: Cuz you have to query the node , for every a uh , or query the net two to the twenty times .
Grad E: Sure . Alright .
Grad B: Or not two to th excuse me , twenty times .
Grad E: OK . So , is it t comes to twenty billion or something ?
Grad B: Yes . As far as Grad E: That 's pretty big , though .
Grad B: That 's @ @ That 's big . Actually Oh ! We calculated a different number before . How did we do that ?
Grad C: Hmm .
Grad E: I remember there being some other one floating around . But anyway , uh .
Grad C: I don't really know .
Grad E: Yeah , it 's g Anyway , the point is that given all of these different factors , it 's uh e it 's it 's still going to be impossible to run through all of the possible situations or whatever .
Grad C: Ooo , it 's just big .
Grad E: But I mean , this 'll get us a bit closer at least , right ? I mean .
Grad B: If it takes us a second to do , for each one , and let 's say it 's twenty billion , {comment} then that 's twenty billion seconds , which is Grad E: Yeah .
Grad B: Eva , do the math .
Grad C: Can't .
Grad E: Long !
Grad C :
Grad B: Hours and hours and hours and hours . But we can do randomized testing .
Grad E: Tah - dah !
Grad B: Which probabilistically will be good enough .
Grad D: Mm - hmm . Yeah . So , it be it it 's an idea that one could n for for example run run past , um , what 's that guy 's name ? You know ? He - he 's usually here . Tsk . J J Jer - Jerj
Grad E: Here in the group ? Jerry Feldman .
Grad D: Oh , yeah . That 's the guy . We we we we g
Grad B: Wait , who ?
Grad E: Yeah , i that would the g the bald guy .
Grad B: Oh ! My advisor !
Grad D: And um . so this is just an idea that 's floating around and we 'll see what happens . And um , hmm , what other news do I have ? Well we fixed some more things from the SmartKom system , but that 's not really of general interest , Um , Oh ! Questions , yeah . I 'll ask Eva about the E Bayes and she 's working on that . How is the generation XML thing ?
Grad B: I 'm gonna work on that today and tomorrow .
Grad D: OK . No need to do it today or tomorrow even . Do it next week or Grad B: I 'm gonna finish it today , uh hopefully .
Grad D: OK .
Grad B: I wanna do one of those things where I stay here . Cuz uh , if I go home , I can't finish it . I 've tried about five times so far , where I work for a while and then I 'm like , I 'm hungry . So I go home , and then I think Grad E: I 'm not going back .
Grad B: Yeah . Either that or I think to myself , I can work at home . And then I try to work at home , but I fail miserably .
Grad E: Yeah .
Grad B: Like I ended up at Blakes last night .
Grad E: Non - conducive .
Grad B: No . I almost got into a brawl . But I did not finish the uh , But I 've been looking into it . I th @ @ It 's not like it 's a blank slate . I found everything that I need and stu and uh ,
Grad D: But st
Grad B: At the b uh furthermore , I told Jerry that I was gonna finish it before he got back . So .
Grad D: OK .
Grad E: That 's approaching . He 's coming back when ? Uh next Grad B: Well , I think we think we 'll see him definitely on Tuesday for the next Or , no , wait . The meetings are on Thursday .
Grad D: Maybe .
Grad B: Maybe .
Grad D: Who knows .
Grad E: OK .
Grad B: Well , we 'll see him next week .
Grad E: Alright .
Grad D: That 's good . Yeah . The paper .
Grad E: Hmm .