B: Yeah , so there 's there 's two separate things , Robert .
B: The f the the the Bayes - nets in general are quite good at saying , " if you have no current information about this variable just take the prior for that . "
B: OK ? Th - that 's what they 're real good at .
B: So , if you don't have any information about the discourse , you just use your priors of of whatever eh the discourse B: uh ,
B: eh , basically whatever w it 's Probabilistically , whatever it would be .
B: And it 's it 's sort of not a great estimate ,
C: Mm - hmm .
B: but it 's the best one you have ,
B: and , so forth .
B: So that , they 're good at .
B: But the other problem is , how do you fill in all these numbers ?
B: And I think that 's the one he was getting at .
E: Mm - hmm .
D: Yeah .
E: Yeah .
D: So , specifically in this case you have to f have this many numbers ,
D: whereas in this case you just have to have three for this , three for this , three for this . Right ?
D: So you have to have just three N ?
C: Mm - hmm .
D: So , this is much smaller than that .
A: Asymptotically .
E: Mm - hmm .
D: Yeah .
B: Well , pretty quickly .
D: Right .
A: U
A: yeah , yeah .
E: So , you don't need da data enough to cover uh , nearly as much stuff .
B: I mean D: I don't know .
A: So , really , i What a A Noisy - OR seems to kind of " neural - net - acize " these Bayes - nets ?
B: Eh well to some
B: No , no .
B: So , " Noisy - OR " is a funny way of referring to this ,
B: because the Noisy - OR is only one instance .
D: Yeah . This isn't a Noisy - OR anymore .
B: That one actually isn't a Noisy - OR .
B: So we 'll have to think of of a way t t
A: Yeah .
D: it 's a Noisy - arg - max or a Noisy - whatever .
B: Yeah , whatever .
B: Yeah .
B: So Eh <comment> Um
A: Well , my point was more that we just eh A: With the neural net , right , eh , things come in , you have a function that combines them
A: and B: Yeah , it it Tha - that 's true .
B: It is a is also more neural - net - like , although  Uh , it isn't necessarily sum uh , s you know , sum of weights or anything like that .
A: Right .
B: I mean i You could have ,
B: uh , like the Noisy - OR function , really is one that 's essentially says , uh , take the max .
D: Well , the " OR " .
B: Same .
D: Right . I guess you 're right .
D: Yeah .
B: Uh
B: But anyway .
B: So B: And , I thi I think that 's the standard way people get around the uh
B: There are a couple other ones .
B: There are ways of breaking this up into s to to subnets and stuff like that .
B: But , um
B: The
B: I think we definitely I think it 's a great idea tha to to pursue that .
D: Yep .
D: So
C: Wha - still sort of leaves one question .
C: It I mean you you can always uh see easily that that I 'm not grasping everything correctly ,
C: but what seemed attractive to me in im uh in the last discussion we had , was that we find out a means of of getting these point four , point five , point one , of C - four ,
C: not because , you know , A is a Landmark or not , but we we we label this whatever object type ,
C: and if it 's a garden , it 's point three , point four , point two .
C: If it 's a castle , it 's point eight , point one , point one .
C: If it 's , uh , a town hall , it 's point two , point three , point five .
B: Right .
C: And so forth .
C: And we don't want to write this down necessarily every time for something
C: but , uh D: It 'll be students C: let 's see .
D: Where else would it be stored ?
D: That 's the question .
C: Well , in the beginning , we 'll write up a flat file .
C: We know we have twenty object types
B: Oh .
B: Yeah .
C: and we 'll write it down in a flat file .
B: No .
B: So , i is
B: Well , let me say something , guys ,
B: cuz there 's not There 's a pretty point about this we might as well get in right now .
D: Mm - hmm .
B: Which is The hierarchy that s comes with the ontology is just what you want for this .
B: So that  Uh , if you know about it let 's say , a particular town hall  that , it 's one that is a monument , then , that would be stored there .
B: If you don't , you look up the hierarchy ,
B: Eh so , you you you may or So , then you 'd have this little vector of , um , you know , Approach Mode or EVA Mode .
B: Let 's OK , so we have the EVA vector for for various kinds of landmarks .
B: If you know it for a specific landmark you put it there .
B: If you don't , you just go up the hierarchy to the first place you find one .
D: OK .
D: So , is the idea to put it in the ontology ?
B: Absolutely .
D: OK .
B: Uh , or , link to B: or B: but but in any case i View it logically as being in the ontology .
B: It 's part of what you know about a an object , is its EVA vector .
D: OK .
D: Mm - hmm .
B: And , if yo As I say , if you know about a specific object , you put it there .
B: This is part of what Dekai was doing .
B: So , when we get to Wu , The - e We 'll see w what he says about that .
D: Right .
B: And , then if you If it isn't there , it 's higher ,
B: and if you don't know anything except that it 's a b it 's it 's a building , then up at the highest thing , you have the pr what amounts to a prior .
B: If you don't know anything else about a building , uh , you just take whatever your crude approximation is up at that level ,
D: Right .
B: which might be equal , or whatever it is .
D: Yeah .
B: So , that 's a very pretty relationship between these local vectors and the ontology .
B: And it seems to me the obvious thing to do , unless we find a reason to do something different .
D: Yeah .
B: Does this make sense to you ?
D: So B: Bhask - ?
D: Yeah .
D: So , we are but we we 're not doing the ontology ,
D: so we have to get to whoever is doing the u ultimately ,
B: Indeed .
D: we have to get them to B: So , that 's another thing we 're gonna need to do , is is , to , either B: We 're gonna need some way to either get a p tag in the ontology , or add fields ,
B: or <comment> <comment> some way to associate B: Or , w It may be that all we can do is , um , some of our own hash tables that it B: Th - the th you know , there 's always a way to do that .
B: It 's a just a question of B: i
A: Yeah , hash on object name to , you know , uh , the probabilities or whatever .
B: th
B: Yeah .
B: e
B: Right .
B: And , so ,
B: i
B: uh C: But it 's , uh  Well , it strikes me as a What For
C: If we get the mechanism , that will be sort of the wonderful part .
C: And then , how to make it work is is the second part ,
C: in the sense that I mean , m the guy who was doing the ontology eh , eh , s ap apologized that i it will take him another through two to three days
C: because they 're having really trouble getting the upper level straight ,
C: and
C: right now .
C: The reason is , given the craw bet uh , the the the projects that all carry their own taxonomy and , on all history , they 're really trying to build one top level ontology ft that covers all the EML projects ,
C: and that 's , uh , uh , sort of a tough cookie ,
C: a little bit tougher than they figured .
C: I could have told them s so .
B: Right .
C: Uh .
B: Yeah .
C: But , nevertheless , it 's going to be there by n by , uh , next Monday
C: and I will show you what 's what some examples from that for towers , and stuff .
C: And , um ,
C: what I don't think is ever going to be in the ontology , is sort of , you know , the likelihood of , eh , people entering r town halls , and looking at town halls , and approaching town halls ,
C: especially since we are b dealing with a case - based ,
C: not an instance - based ontology .
C: So , there will be nothing on on that town hall , or on the Berkeley town hall , or on the Heidelberg town hall ,
C: it 'll just be information on town halls .
B: Well , they they they How ar What are they gonna do with instances ?
C: But what B: I mean , you y
C: Well , that 's C: Hhh .
C: That 's that 's al different question .
C: I mean , th the first , they had to make a design question , " do we take ontologies that have instances ?
C: or just one that does not , that just has the types ? "
B: OK .
C: And , so , since the d decision was on types , on a d simply type - based , we now have to hook it up to instances .