Just don't interfere with other devices like 'cause like all these people do that their lights are turning on with clapping.


yeah. I think that could be in the component uh concept uh.


No , cuz she already told me it , before she told you.


No , cuz she already told me it , before she told you.

No , she told me a long time ago. She told me  she told me like two weeks ago.


No , cuz she already told me it , before she told you.

No , she told me a long time ago. She told me  she told me like two weeks ago.

Oh , well , it doesn't matter what time.


No , cuz she already told me it , before she told you.

No , she told me a long time ago. She told me  she told me like two weeks ago.

Oh , well , it doesn't matter what time.

OK. You know how to toggle the display width  function


No , she told me a long time ago. She told me  she told me like two weeks ago.

Oh , well , it doesn't matter what time.

OK. You know how to toggle the display width  function

Well maybe she hadn't just started transcribing me yet.


Oh , well , it doesn't matter what time.

OK. You know how to toggle the display width  function

Well maybe she hadn't just started transcribing me yet.


OK. You know how to toggle the display width  function

Well maybe she hadn't just started transcribing me yet.


Well maybe she hadn't just started transcribing me yet.


But you have to say something genuinely funny before you 'll get an example.


But you have to say something genuinely funny before you 'll get an example.

The thing is I don't know how to get to the next page. Here.


But you have to say something genuinely funny before you 'll get an example.

The thing is I don't know how to get to the next page. Here.

No. You should be  at least be self - satisfied enough to laugh at your own jokes.


The thing is I don't know how to get to the next page. Here.

No. You should be  at least be self - satisfied enough to laugh at your own jokes.


No. You should be  at least be self - satisfied enough to laugh at your own jokes.


What ? ! Oh. OK. I wasn't even doing anything.  OK.


What ? ! Oh. OK. I wasn't even doing anything.  OK.

Eva 's got a laptop , she 's trying to show it off.


What ? ! Oh. OK. I wasn't even doing anything.  OK.

Eva 's got a laptop , she 's trying to show it off.

That was r actually Robert 's idea. But anyhow. Um


Eva 's got a laptop , she 's trying to show it off.

That was r actually Robert 's idea. But anyhow. Um


That was r actually Robert 's idea. But anyhow. Um

Once again , right , together. Um , so we haven't had a meeting for a while , and  and probably won't have one next week , I think a number of people are gone. Um , so Robert , why don't you bring us up to date on where we are with EDU ?


Once again , right , together. Um , so we haven't had a meeting for a while , and  and probably won't have one next week , I think a number of people are gone. Um , so Robert , why don't you bring us up to date on where we are with EDU ?

Um , uh in a  in a smaller group we had uh , talked and decided about continuation of the data collection. So Fey 's time with us is almost officially over , and she brought us some thirty subjects and , t collected the data , and ten dialogues have been transcribed and can be looked at. If you 're interested in that , talk to me. Um , and we found another uh , cogsci student who 's interested in playing wizard for us. Here we 're gonna make it a little bit more complicated for the subjects , uh this round. She 's actually suggested to look um , at the psychology department students , because they have to partake in two experiments in order to fulfill some requirements. So they have to be subjected ,   before they can actually graduate. And um , we want to design it so that they really have to think about having some time , two days , for example , to plan certain things and figure out which can be done at what time , and , um , sort of package the whole thing in a  in a re in a few more complicated um , structure. That 's for the data collection. As for SmartKom , I 'm  the last SmartKom meeting I mentioned that we have some problems with the synthesis , which as of this morning should be resolved. And , so ,


Once again , right , together. Um , so we haven't had a meeting for a while , and  and probably won't have one next week , I think a number of people are gone. Um , so Robert , why don't you bring us up to date on where we are with EDU ?

Um , uh in a  in a smaller group we had uh , talked and decided about continuation of the data collection. So Fey 's time with us is almost officially over , and she brought us some thirty subjects and , t collected the data , and ten dialogues have been transcribed and can be looked at. If you 're interested in that , talk to me. Um , and we found another uh , cogsci student who 's interested in playing wizard for us. Here we 're gonna make it a little bit more complicated for the subjects , uh this round. She 's actually suggested to look um , at the psychology department students , because they have to partake in two experiments in order to fulfill some requirements. So they have to be subjected ,   before they can actually graduate. And um , we want to design it so that they really have to think about having some time , two days , for example , to plan certain things and figure out which can be done at what time , and , um , sort of package the whole thing in a  in a re in a few more complicated um , structure. That 's for the data collection. As for SmartKom , I 'm  the last SmartKom meeting I mentioned that we have some problems with the synthesis , which as of this morning should be resolved. And , so ,

" should be " means they aren't yet , but  but I think I have the info now that I need. Plus , Johno and I are meeting tomorrow , so maybe uh uh , when tomorrow is over , we 're done. And ha n hav we 'll never have to look at it again Maybe it 'll take some more time , to be realistic , but at least we 're  we 're seeing the end of the tunnel there. That was that. Um , the uh , uh I don't think we need to discuss the formalism that 'll be done officially s once we 're done. Um , something happened , in  on Eva 's side with the PRM that we 're gonna look at today , and um , we have a visitor from Bruchsal from the International University. Andreas , I think you 've met everyone except Nancy.


Um , uh in a  in a smaller group we had uh , talked and decided about continuation of the data collection. So Fey 's time with us is almost officially over , and she brought us some thirty subjects and , t collected the data , and ten dialogues have been transcribed and can be looked at. If you 're interested in that , talk to me. Um , and we found another uh , cogsci student who 's interested in playing wizard for us. Here we 're gonna make it a little bit more complicated for the subjects , uh this round. She 's actually suggested to look um , at the psychology department students , because they have to partake in two experiments in order to fulfill some requirements. So they have to be subjected ,   before they can actually graduate. And um , we want to design it so that they really have to think about having some time , two days , for example , to plan certain things and figure out which can be done at what time , and , um , sort of package the whole thing in a  in a re in a few more complicated um , structure. That 's for the data collection. As for SmartKom , I 'm  the last SmartKom meeting I mentioned that we have some problems with the synthesis , which as of this morning should be resolved. And , so ,

" should be " means they aren't yet , but  but I think I have the info now that I need. Plus , Johno and I are meeting tomorrow , so maybe uh uh , when tomorrow is over , we 're done. And ha n hav we 'll never have to look at it again Maybe it 'll take some more time , to be realistic , but at least we 're  we 're seeing the end of the tunnel there. That was that. Um , the uh , uh I don't think we need to discuss the formalism that 'll be done officially s once we 're done. Um , something happened , in  on Eva 's side with the PRM that we 're gonna look at today , and um , we have a visitor from Bruchsal from the International University. Andreas , I think you 've met everyone except Nancy.


" should be " means they aren't yet , but  but I think I have the info now that I need. Plus , Johno and I are meeting tomorrow , so maybe uh uh , when tomorrow is over , we 're done. And ha n hav we 'll never have to look at it again Maybe it 'll take some more time , to be realistic , but at least we 're  we 're seeing the end of the tunnel there. That was that. Um , the uh , uh I don't think we need to discuss the formalism that 'll be done officially s once we 're done. Um , something happened , in  on Eva 's side with the PRM that we 're gonna look at today , and um , we have a visitor from Bruchsal from the International University. Andreas , I think you 've met everyone except Nancy.


So when you said " Andreas " I thought you were talking about Stolcke.


So when you said " Andreas " I thought you were talking about Stolcke.

Andy , you actually go by Andy , right ? Oh , OK.


Andy , you actually go by Andy , right ? Oh , OK.


So my scientific director of the EML is also the dean of the International University , one of his many occupations that just contributes to the fact that he is very occupied. And , um , the  um , he @ @ might tell us a little bit about what he 's actually doing , and why it is s somewhat related , and  by uh using maybe some of the same technologies that we are using. And um. Was that enough of an update ?


Uh , yeah , sure. Um , so , I 've be just been looking at , um , Ack ! What are you doing ? Yeah. OK. Um , I 've been looking at the PRM stuff. Um , so , this is , sort of like the latest thing I have on it , and I sorta constructed a couple of classes. Like , a user class , a site class , and  and you know , a time , a route , and then  and a query class. And I tried to simplify it down a little bit , so that I can actually um , look at it more. It 's the same paper that I gave to Jerry last time. Um , so basically I took out a lot of stuff , a lot of the decision nodes , and then tried to  The red lines on the , um , graph are the um , relations between the different um , classes. Like , a user has like , a query , and then , also has , you know um , reference slots to its preferences , um , the special needs and , you know , money , and the user interest. And so this is more or less similar to the flat Bayes - net that I have , you know , with the input nodes and all that. And  So I tried to construct the dependency models , and a lot of these stuff I got from the flat Bayes - net , and what they depend on , and it turns out , you know , the CPT 's are really big , if I do that , so I tried to see how I can do , um  put in the computational nodes in between. And what that would look like in a PRM. And so I ended up making several classes  Actually , you know , a class of  with different attributes that are the intermediate nodes , and one of them is like , time affordability money affordability , site availability , and the travel compatibility. And so some of these classes are  s some of these attributes only depend on stuff from , say , the user , or s f just from , I don't know , like the site. S like , um , these here , it 's only like , user , but , if you look at travel compatibility for each of these factors , you need to look at a pair of , you know , what the um , preference of the user is versus , you know , what type of an event it is , or you know , which form of transportation the user has and whether , you know , the onsite parking matters to the user , in that case. And that makes the scenario a little different in a PRM , because , um , then you have one - user objects and potentially you can have many different sites in  in mind. And so for each of the site you 'll come up with this rating , of travel compatibility. And , they all depend on the same users , but different sites , and that makes a  I 'm tr I w I wa have been trying to see whether the PRM would make it more efficient if we do inferencing like that. And so , I guess you end up having fewer number of nodes than in a flat Bayes - net , cuz otherwise you would  c well , it 's probably the same. But um , No , you would definitely have  be able to re - use , like ,  um , all the user stuff , and not  not having to recompute a lot of the stuff , because it 's all from the user side. So if you changed sites , you  you can , you know , save some work on that. But , you know , in the case where , it depends on both the user and the site , then I 'm still having a hard time trying to see how um , using the PRM will help. Um , so anyhow , using those intermediate nodes then , this  this would be the class that represent the intermediate nodes. And that would  basically it 's just another class in the model , with , you know , references to the user and the site and the time. And then , after you group them together this  no the dependencies would  of the queries would be reduced to this. And so , you know , it 's easier to specify the CPT and all. Um , so I think that 's about as far as I 've gone on the PRM stuff.


Uh , yeah , sure. Um , so , I 've be just been looking at , um , Ack ! What are you doing ? Yeah. OK. Um , I 've been looking at the PRM stuff. Um , so , this is , sort of like the latest thing I have on it , and I sorta constructed a couple of classes. Like , a user class , a site class , and  and you know , a time , a route , and then  and a query class. And I tried to simplify it down a little bit , so that I can actually um , look at it more. It 's the same paper that I gave to Jerry last time. Um , so basically I took out a lot of stuff , a lot of the decision nodes , and then tried to  The red lines on the , um , graph are the um , relations between the different um , classes. Like , a user has like , a query , and then , also has , you know um , reference slots to its preferences , um , the special needs and , you know , money , and the user interest. And so this is more or less similar to the flat Bayes - net that I have , you know , with the input nodes and all that. And  So I tried to construct the dependency models , and a lot of these stuff I got from the flat Bayes - net , and what they depend on , and it turns out , you know , the CPT 's are really big , if I do that , so I tried to see how I can do , um  put in the computational nodes in between. And what that would look like in a PRM. And so I ended up making several classes  Actually , you know , a class of  with different attributes that are the intermediate nodes , and one of them is like , time affordability money affordability , site availability , and the travel compatibility. And so some of these classes are  s some of these attributes only depend on stuff from , say , the user , or s f just from , I don't know , like the site. S like , um , these here , it 's only like , user , but , if you look at travel compatibility for each of these factors , you need to look at a pair of , you know , what the um , preference of the user is versus , you know , what type of an event it is , or you know , which form of transportation the user has and whether , you know , the onsite parking matters to the user , in that case. And that makes the scenario a little different in a PRM , because , um , then you have one - user objects and potentially you can have many different sites in  in mind. And so for each of the site you 'll come up with this rating , of travel compatibility. And , they all depend on the same users , but different sites , and that makes a  I 'm tr I w I wa have been trying to see whether the PRM would make it more efficient if we do inferencing like that. And so , I guess you end up having fewer number of nodes than in a flat Bayes - net , cuz otherwise you would  c well , it 's probably the same. But um , No , you would definitely have  be able to re - use , like ,  um , all the user stuff , and not  not having to recompute a lot of the stuff , because it 's all from the user side. So if you changed sites , you  you can , you know , save some work on that. But , you know , in the case where , it depends on both the user and the site , then I 'm still having a hard time trying to see how um , using the PRM will help. Um , so anyhow , using those intermediate nodes then , this  this would be the class that represent the intermediate nodes. And that would  basically it 's just another class in the model , with , you know , references to the user and the site and the time. And then , after you group them together this  no the dependencies would  of the queries would be reduced to this. And so , you know , it 's easier to specify the CPT and all. Um , so I think that 's about as far as I 've gone on the PRM stuff.

No. So y you didn't yet tell us what the output is.


No. So y you didn't yet tell us what the output is.


No. So y you didn't yet tell us what the output is.

OK. So it only makes two decisions , in this model. And one is basically how desirable a site is meaning , um , how good it matches the needs of a user. And the other is the mode of the visit , whether th It 's the EVA decision. Um , so , instead of um ,  doing a lot of , you know , computation about , you know , which one site it wants of  the user wants to visit , I 'll come  well , try to come up with like , sort of a list of sites. And for each site , you know , where  h how  how well it fits , and basically a rating of how well it fits and what to do with it. So. Anything else I missed ?


OK. So it only makes two decisions , in this model. And one is basically how desirable a site is meaning , um , how good it matches the needs of a user. And the other is the mode of the visit , whether th It 's the EVA decision. Um , so , instead of um ,  doing a lot of , you know , computation about , you know , which one site it wants of  the user wants to visit , I 'll come  well , try to come up with like , sort of a list of sites. And for each site , you know , where  h how  how well it fits , and basically a rating of how well it fits and what to do with it. So. Anything else I missed ?

So that was pretty quick. She 's ac uh uh Eva 's got a little write - up on it that uh , probably gives the  the details to anybody who needs them. Um , so the  You  you didn't look at all yet to see if there 's anybody has a implementation.


OK. So it only makes two decisions , in this model. And one is basically how desirable a site is meaning , um , how good it matches the needs of a user. And the other is the mode of the visit , whether th It 's the EVA decision. Um , so , instead of um ,  doing a lot of , you know , computation about , you know , which one site it wants of  the user wants to visit , I 'll come  well , try to come up with like , sort of a list of sites. And for each site , you know , where  h how  how well it fits , and basically a rating of how well it fits and what to do with it. So. Anything else I missed ?

So that was pretty quick. She 's ac uh uh Eva 's got a little write - up on it that uh , probably gives the  the details to anybody who needs them. Um , so the  You  you didn't look at all yet to see if there 's anybody has a implementation.

OK. So one  so one of the questions , you know , about these P R Ms is


So that was pretty quick. She 's ac uh uh Eva 's got a little write - up on it that uh , probably gives the  the details to anybody who needs them. Um , so the  You  you didn't look at all yet to see if there 's anybody has a implementation.

OK. So one  so one of the questions , you know , about these P R Ms is


OK. So one  so one of the questions , you know , about these P R Ms is

uh , we aren't gonna build our own interpreter , so if  if we can't find one , then we uh , go off and do something else and wait until s one appears. Uh , so one of the things that Eva 's gonna do over the next few weeks is see if we can track that down. Uh , the people at Stanford write papers as if they had one , but , um , we 'll see. So w Anyway. So that 's a  a major open issue. If there is an interpreter , it looks like you know , what Eva 's got should run and we should be able to actually um , try to solve , you know , the problems , to actually take the data , and do it. Uh , and we 'll see. Uh , I actually think it is cleaner , and the ability to instantiate , you know , instance of people and sites and stuff , um , will help in the expression. Whether the inference gets any faster or not I don't know. Uh , it wouldn't surprise me if it  if it doesn't.


uh , we aren't gonna build our own interpreter , so if  if we can't find one , then we uh , go off and do something else and wait until s one appears. Uh , so one of the things that Eva 's gonna do over the next few weeks is see if we can track that down. Uh , the people at Stanford write papers as if they had one , but , um , we 'll see. So w Anyway. So that 's a  a major open issue. If there is an interpreter , it looks like you know , what Eva 's got should run and we should be able to actually um , try to solve , you know , the problems , to actually take the data , and do it. Uh , and we 'll see. Uh , I actually think it is cleaner , and the ability to instantiate , you know , instance of people and sites and stuff , um , will help in the expression. Whether the inference gets any faster or not I don't know. Uh , it wouldn't surprise me if it  if it doesn't.

You know , it 's the same kind of information. I think there are things that you can express this way which you can't express in a normal belief - net , uh , without going to some incredible hacking of  sort of rebuilding it on the fly. I mean , the notion of instantiating your el elements from the ontology and stuff fits this very nicely and doesn't fit very well into the extended belief - net. So that was one of the main reasons for doing it. Um. I don't know. So , uh , people who have thought about the problem , like Robert i it looked to me like if  Eva were able to come up with a  you know , value for each of a number of uh , sites plus its EVA thing , that a travel planner should be able to take it from there. And  you know , with some other information about how much time the person has and whatever , and then plan a route.


uh , we aren't gonna build our own interpreter , so if  if we can't find one , then we uh , go off and do something else and wait until s one appears. Uh , so one of the things that Eva 's gonna do over the next few weeks is see if we can track that down. Uh , the people at Stanford write papers as if they had one , but , um , we 'll see. So w Anyway. So that 's a  a major open issue. If there is an interpreter , it looks like you know , what Eva 's got should run and we should be able to actually um , try to solve , you know , the problems , to actually take the data , and do it. Uh , and we 'll see. Uh , I actually think it is cleaner , and the ability to instantiate , you know , instance of people and sites and stuff , um , will help in the expression. Whether the inference gets any faster or not I don't know. Uh , it wouldn't surprise me if it  if it doesn't.

You know , it 's the same kind of information. I think there are things that you can express this way which you can't express in a normal belief - net , uh , without going to some incredible hacking of  sort of rebuilding it on the fly. I mean , the notion of instantiating your el elements from the ontology and stuff fits this very nicely and doesn't fit very well into the extended belief - net. So that was one of the main reasons for doing it. Um. I don't know. So , uh , people who have thought about the problem , like Robert i it looked to me like if  Eva were able to come up with a  you know , value for each of a number of uh , sites plus its EVA thing , that a travel planner should be able to take it from there. And  you know , with some other information about how much time the person has and whatever , and then plan a route.

Um - hmm , um ,  well , first of all uh , uh , great looks , mu much cleaner , nnn , nnn , Certain  certain beauty in it , so , um , if beauty is truth , then , uh we 're in good shape. But , the um , as , uh , mentioned before we probably should look at t the details. So if you have a write - up then uh , I 'd love to read it


You know , it 's the same kind of information. I think there are things that you can express this way which you can't express in a normal belief - net , uh , without going to some incredible hacking of  sort of rebuilding it on the fly. I mean , the notion of instantiating your el elements from the ontology and stuff fits this very nicely and doesn't fit very well into the extended belief - net. So that was one of the main reasons for doing it. Um. I don't know. So , uh , people who have thought about the problem , like Robert i it looked to me like if  Eva were able to come up with a  you know , value for each of a number of uh , sites plus its EVA thing , that a travel planner should be able to take it from there. And  you know , with some other information about how much time the person has and whatever , and then plan a route.

Um - hmm , um ,  well , first of all uh , uh , great looks , mu much cleaner , nnn , nnn , Certain  certain beauty in it , so , um , if beauty is truth , then , uh we 're in good shape. But , the um , as , uh , mentioned before we probably should look at t the details. So if you have a write - up then uh , I 'd love to read it


You know , it 's the same kind of information. I think there are things that you can express this way which you can't express in a normal belief - net , uh , without going to some incredible hacking of  sort of rebuilding it on the fly. I mean , the notion of instantiating your el elements from the ontology and stuff fits this very nicely and doesn't fit very well into the extended belief - net. So that was one of the main reasons for doing it. Um. I don't know. So , uh , people who have thought about the problem , like Robert i it looked to me like if  Eva were able to come up with a  you know , value for each of a number of uh , sites plus its EVA thing , that a travel planner should be able to take it from there. And  you know , with some other information about how much time the person has and whatever , and then plan a route.

Um - hmm , um ,  well , first of all uh , uh , great looks , mu much cleaner , nnn , nnn , Certain  certain beauty in it , so , um , if beauty is truth , then , uh we 're in good shape. But , the um , as , uh , mentioned before we probably should look at t the details. So if you have a write - up then uh , I 'd love to read it

and uh  because , um , i Can you go all the way back to the  the very top ?


Um - hmm , um ,  well , first of all uh , uh , great looks , mu much cleaner , nnn , nnn , Certain  certain beauty in it , so , um , if beauty is truth , then , uh we 're in good shape. But , the um , as , uh , mentioned before we probably should look at t the details. So if you have a write - up then uh , I 'd love to read it

and uh  because , um , i Can you go all the way back to the  the very top ?


and uh  because , um , i Can you go all the way back to the  the very top ?

Um ,  uh these  @ @  these  w w when these are instantiated they take on the same values ? that we had before ?


Um ,  uh these  @ @  these  w w when these are instantiated they take on the same values ? that we had before ?

or are they  have they changed , in a sense ?


Um ,  uh these  @ @  these  w w when these are instantiated they take on the same values ? that we had before ?

or are they  have they changed , in a sense ?

Well I think I basically leave them to similar things.


or are they  have they changed , in a sense ?

Well I think I basically leave them to similar things.


or are they  have they changed , in a sense ?

Well I think I basically leave them to similar things.

Some of the things might  that might be different , maybe like  are that the hours for the site.


Well I think I basically leave them to similar things.

Some of the things might  that might be different , maybe like  are that the hours for the site.


Some of the things might  that might be different , maybe like  are that the hours for the site.

And , eventually I meant that to mean whether they 're open at this hour or not.


And , eventually I meant that to mean whether they 're open at this hour or not.

And status would be , you know , more or less like , whether they 're under construction , and  and  or stuff like that.


And , eventually I meant that to mean whether they 're open at this hour or not.

And status would be , you know , more or less like , whether they 're under construction , and  and  or stuff like that.

And the , uh , other question I would have is that presumably , from the way the Stanford people talk about it , you can put the probabilities also on the relations. If


And status would be , you know , more or less like , whether they 're under construction , and  and  or stuff like that.

And the , uh , other question I would have is that presumably , from the way the Stanford people talk about it , you can put the probabilities also on the relations. If


And status would be , you know , more or less like , whether they 're under construction , and  and  or stuff like that.

And the , uh , other question I would have is that presumably , from the way the Stanford people talk about it , you can put the probabilities also on the relations. If

Yeah. Yeah , I  that 's  That I think was actually in the previous  the Ubenth stuff. I don't remember whether they carried that over to this or not ,


And the , uh , other question I would have is that presumably , from the way the Stanford people talk about it , you can put the probabilities also on the relations. If

Yeah. Yeah , I  that 's  That I think was actually in the previous  the Ubenth stuff. I don't remember whether they carried that over to this or not ,


Yeah. Yeah , I  that 's  That I think was actually in the previous  the Ubenth stuff. I don't remember whether they carried that over to this or not ,


Yeah. Yeah , I  that 's  That I think was actually in the previous  the Ubenth stuff. I don't remember whether they carried that over to this or not ,

It 's sort of in the definition or  in the  in Daphne 's definition of a PRM is that classes and relations ,


It 's sort of in the definition or  in the  in Daphne 's definition of a PRM is that classes and relations ,


It 's sort of in the definition or  in the  in Daphne 's definition of a PRM is that classes and relations ,

and you 're gonna have CPT 's over the classes and their relations.


and you 're gonna have CPT 's over the classes and their relations.


I remember them learning when , you know , you don't know the structure for sure ,


I remember them learning when , you know , you don't know the structure for sure ,

but I don't remember reading how you specify


I remember them learning when , you know , you don't know the structure for sure ,

but I don't remember reading how you specify

Yeah , that would be exactly my question.


but I don't remember reading how you specify

Yeah , that would be exactly my question.


Yeah , that would be exactly my question.


Yeah. So , uh , the  the plan is  is when Daphne gets back , we 'll get in touch and supposedly , um , we 'll actually get s deep  seriously connected to  to their work and


Yeah. So , uh , the  the plan is  is when Daphne gets back , we 'll get in touch and supposedly , um , we 'll actually get s deep  seriously connected to  to their work and

somebody 'll  Uh , you know  If it 's a group meeting once a week probably someone 'll go down and , whatever. So , we 'll actually figure all this out.


Yeah. So , uh , the  the plan is  is when Daphne gets back , we 'll get in touch and supposedly , um , we 'll actually get s deep  seriously connected to  to their work and

somebody 'll  Uh , you know  If it 's a group meeting once a week probably someone 'll go down and , whatever. So , we 'll actually figure all this out.

OK. OK. Then I think the w  long term perspective is  is pretty clear. We get rocking and rolling on this again , once we get a package , if , when , and how , then this becomes foregrounded


somebody 'll  Uh , you know  If it 's a group meeting once a week probably someone 'll go down and , whatever. So , we 'll actually figure all this out.

OK. OK. Then I think the w  long term perspective is  is pretty clear. We get rocking and rolling on this again , once we get a package , if , when , and how , then this becomes foregrounded


OK. OK. Then I think the w  long term perspective is  is pretty clear. We get rocking and rolling on this again , once we get a package , if , when , and how , then this becomes foregrounded


And um , until then we 'll come up with a something that 's  @ @  that 's way more complicated for you. Right ?


And um , until then we 'll come up with a something that 's  @ @  that 's way more complicated for you. Right ?

Because this was laughingly easy , right ?


And um , until then we 'll come up with a something that 's  @ @  that 's way more complicated for you. Right ?

Because this was laughingly easy , right ?

Actually I had to take out a lot of the complicated stuff , cuz I  I made it really complicated in the beginning , and Jerry was like ,  " this is just too much ".


Because this was laughingly easy , right ?

Actually I had to take out a lot of the complicated stuff , cuz I  I made it really complicated in the beginning , and Jerry was like ,  " this is just too much ".

Yeah. So , um , you could , from this , go on and say suppose there 's a group of people traveling together and you wanted to plan something that somehow , with some Pareto optimal uh ,  uh , thing for


Because this was laughingly easy , right ?

Actually I had to take out a lot of the complicated stuff , cuz I  I made it really complicated in the beginning , and Jerry was like ,  " this is just too much ".

Yeah. So , um , you could , from this , go on and say suppose there 's a group of people traveling together and you wanted to plan something that somehow , with some Pareto optimal uh ,  uh , thing for

That 's good. That 's definitely a job for artificial intelligence.


Actually I had to take out a lot of the complicated stuff , cuz I  I made it really complicated in the beginning , and Jerry was like ,  " this is just too much ".

Yeah. So , um , you could , from this , go on and say suppose there 's a group of people traveling together and you wanted to plan something that somehow , with some Pareto optimal uh ,  uh , thing for

That 's good. That 's definitely a job for artificial intelligence.


Yeah. So , um , you could , from this , go on and say suppose there 's a group of people traveling together and you wanted to plan something that somehow , with some Pareto optimal uh ,  uh , thing for

That 's good. That 's definitely a job for artificial intelligence.

Except for humans can't really solve it either , so.


That 's good. That 's definitely a job for artificial intelligence.

Except for humans can't really solve it either , so.

Well that 's not  not even something humans  yeah.


Except for humans can't really solve it either , so.

Well that 's not  not even something humans  yeah.

Right. Right. Well that 's the  that would  that would be a  uh , you could sell it , as a


Well that 's not  not even something humans  yeah.

Right. Right. Well that 's the  that would  that would be a  uh , you could sell it , as a

OK , eh you don't have to fight about this , just give your preferences to the


Right. Right. Well that 's the  that would  that would be a  uh , you could sell it , as a

OK , eh you don't have to fight about this , just give your preferences to the


OK , eh you don't have to fight about this , just give your preferences to the


Hmm. But what does it  uh  Would a pote potential result be to  to split up and never talk to each other again ? You know.


Anyway. So. So there i there are some  some u uh , you know , uh , elaborations of this that you could try to put in to this structure , but I don't think it 's worth it now. Because we 're gonna see what  what else uh  what else we 're gonna do. Anyway. But uh , it 's good , yeah and  and there were a couple other ideas of  of uh , things for Eva to look at in  in the interim.


Anyway. So. So there i there are some  some u uh , you know , uh , elaborations of this that you could try to put in to this structure , but I don't think it 's worth it now. Because we 're gonna see what  what else uh  what else we 're gonna do. Anyway. But uh , it 's good , yeah and  and there were a couple other ideas of  of uh , things for Eva to look at in  in the interim.

Good. Then , we can move on and see what Andreas has got out his sleeve. Or Andy , for that matter ?


Anyway. So. So there i there are some  some u uh , you know , uh , elaborations of this that you could try to put in to this structure , but I don't think it 's worth it now. Because we 're gonna see what  what else uh  what else we 're gonna do. Anyway. But uh , it 's good , yeah and  and there were a couple other ideas of  of uh , things for Eva to look at in  in the interim.

Good. Then , we can move on and see what Andreas has got out his sleeve. Or Andy , for that matter ?

OK. So uh , uh , well , thanks for having me here , first of all. Um , so maybe just a  a little background on  on my visit. So , uh , I 'm not really involved in any project , that 's uh  that 's relevant to you uh , a at the moment , uh , the  the reason is really for me uh , to have an opportunity to talk to some other researchers in the field. And  and so I 'll just n sort of give you a real quick introduction to what I 'm working on , and um , I just hope that you have some comments or , maybe you 're interested in it to find out more , and  and so I 'll be uh , happy to talk to you and  and uh , I 'd also like to find out some more and  and maybe I 'll just walk around the office and and then  and ask some  some questions , uh , in a couple days. So I 'll be here for uh , tomorrow and then uh , the remainder of uh , next week. OK , so , um , what I started looking at , uh , to begin with is just uh , content management systems uh , i i in general. So um , uh what 's uh  Sort of the state of the art there is to um  uh you have a bunch of  of uh documents or learning units or learning objects , um , and you store meta - data uh , associate to them. So there 's some international standards like the I - triple - E , uh  There 's an I - triple - E , LON standard , and um , these fields are pretty straightforward , you have uh author information , you have uh , size information , format information and so on. Uh , but they 're two uh fields that are um , more interesting. One is uh you store keywords associated with the uh  with the document , and one is uh , you have sort of a , um , well , what is the document about ? So it 's some sort of taxonomic uh , ordering of  of the  of the units. Now , if you sort of put on your semantic glasses , uh you say , well that 's not all that easy , because there 's an implicit um , uh , assumption behind that is that uh , all the users of this system share the same interpretation of the keyword and the same interpretation of uh , whichever taxonomy is used , and uh , I think that 's a  that 's a very  that 's a key point of these systems and they sort of always brush over this real quickly without really elaborating much of that and uh  As a matter of fact , the only thing that m apparently really works out so far are library ordering codes , which are very , very coarse grain , so you have some like , science , biology , and then  But that 's really all that we have at the moment. So I think there 's a huge , um , uh need for improvement there. Now , what this uh  a standard like this would give us is we could um , sort of uh with a search engine just query uh , different repositories all over the world. But we can't really  Um , so what I 'm  what I try to do is um , to have um , uh  So. So the scenario is the following , you you 're working on some sort of project and you encounter a certain problem. Now , what  what we have at our university quite a bit is that uh , students um , try to u program a certain assignment , for example , they always run into the same problems , uh , and they always come running to us , and they 'll say why 's it not  it 's not working , and we always give out the same answer , so we thought , well , it 'd be nice to have a system that could sort of take care of this , and so , what I want to build is basically a  a smart F A Q system. Now , what you uh need to do here is you need to provide some context information which is more elaborate than " I 'm looking for this and this and this keyword. " So. And I think that I don't need to tell you this. I 'm  I 'm sure you have the same  when  when somebody utters a sentence in a certain , uh , context it , and  and the same sentence in another context makes a huge difference. So , I want to be able to model information like , um , so in the  in the context of  in the context of developing distributed systems , of a at a computer science school , um , what kind of software is the person using , which homework assignment is he or she working on at the moment , um , maybe what 's the background of that student 's um , which um , which error message was encountered. So this sort of information I think should be transmitted , uh , when a certain document is retrieved. Now , um , basically giving this um  Uh so we somehow need to have a formalized um , way of writing this down basically , and that 's where the shared interpretation of  of certain terms and keywords comes in again. And , using this and some  some uh , knowledge about the domain I think you can do some  some simple inferences. Like you know that when somebody 's working about  uh , working on  on servlets for example , he 's using Java , cuz servlets are used  are written in Java. So some  some inferences like that , now , um , u using this you can infer more information , and you could then match this to the meta - data of um  off the documents you 're  you 're searching against. So , uh what I wanna do is basically have some sort of um  given these inputs , and then I can compute how many documents match , and use this as a metric in the search. Now , what I plan to do is I want to uh sort of do a uh  uh  try to improve the quality of the search results , and I want to do this by having a depth uh , um , um  steepest descent approach. So if I knew which operating system the person was working on , would this improve my search result ? And  and having uh , uh a symbolic formalized model of this I could simply compute that , and find out which um  which questions are worth um , asking. And that 's what I then propagate back to the user , and  and sort of try to optimize the search in this way. Now , the big problem that I 'm facing right now is um , it 's fairly easy to hack up a system uh quickly , that  that works in the small domain , but the problem is obviously the scalability. And uh uh , so Robert was mentioning uh , earlier today is that uh , Microsoft for example with their printer set up program has a Bayesian network , which does exactly this , but there you face a problem that these are very hard to extend. And so , uh what I 'm  What I try to do is basically try to model this uh , in a way that you could really combine uh , knowledge from very different sources , and  and um , sort of looking into some of the ideas that the semantic web community uh , came up with. Trying to  to have uh , an approach how to integrate s uh certain uh  representation of certain concepts and also some computational rules , um , what you can do with those. Um. What I 'm also looking into is a probabilistic approach into this because document retrievals is a very fuzzy procedure , so it 's probably not that easy to simply have a symbolic uh , computational model. That  that probably isn't expressive enough. So. So that 's another thing , um , which I think you 're also uh , uh looking into right now. And then um , uh sort of as an add - on to this whole idea , um , uh that would be now , depending on what the search engine or the content repository  depending on which  um , uh , which uh , rules and which ontologies it  it uses , or basically its view of the world , uh you can get very different results. So it might ma make a lot of sense to actually query a lot of different search engines. And there you could have an idea where you actually have sort of a  a peer to peer approach , where we 're all sort of carrying around our individual bookshelves , and um , if you have a question about a homework , it 's  probably makes sense to ask somebody who 's in your class with you , sort of the guru in the certain area , rather than going to some Yahoo - like uh , search engine. So these are some of the  just in a nutshell , some of the ideas. And I think a lot of the  even though it 's a  it 's a very different domain , but I think a lot of the , um , issues are  are fairly similar. So. OK.


Anyway. So. So there i there are some  some u uh , you know , uh , elaborations of this that you could try to put in to this structure , but I don't think it 's worth it now. Because we 're gonna see what  what else uh  what else we 're gonna do. Anyway. But uh , it 's good , yeah and  and there were a couple other ideas of  of uh , things for Eva to look at in  in the interim.

Good. Then , we can move on and see what Andreas has got out his sleeve. Or Andy , for that matter ?

OK. So uh , uh , well , thanks for having me here , first of all. Um , so maybe just a  a little background on  on my visit. So , uh , I 'm not really involved in any project , that 's uh  that 's relevant to you uh , a at the moment , uh , the  the reason is really for me uh , to have an opportunity to talk to some other researchers in the field. And  and so I 'll just n sort of give you a real quick introduction to what I 'm working on , and um , I just hope that you have some comments or , maybe you 're interested in it to find out more , and  and so I 'll be uh , happy to talk to you and  and uh , I 'd also like to find out some more and  and maybe I 'll just walk around the office and and then  and ask some  some questions , uh , in a couple days. So I 'll be here for uh , tomorrow and then uh , the remainder of uh , next week. OK , so , um , what I started looking at , uh , to begin with is just uh , content management systems uh , i i in general. So um , uh what 's uh  Sort of the state of the art there is to um  uh you have a bunch of  of uh documents or learning units or learning objects , um , and you store meta - data uh , associate to them. So there 's some international standards like the I - triple - E , uh  There 's an I - triple - E , LON standard , and um , these fields are pretty straightforward , you have uh author information , you have uh , size information , format information and so on. Uh , but they 're two uh fields that are um , more interesting. One is uh you store keywords associated with the uh  with the document , and one is uh , you have sort of a , um , well , what is the document about ? So it 's some sort of taxonomic uh , ordering of  of the  of the units. Now , if you sort of put on your semantic glasses , uh you say , well that 's not all that easy , because there 's an implicit um , uh , assumption behind that is that uh , all the users of this system share the same interpretation of the keyword and the same interpretation of uh , whichever taxonomy is used , and uh , I think that 's a  that 's a very  that 's a key point of these systems and they sort of always brush over this real quickly without really elaborating much of that and uh  As a matter of fact , the only thing that m apparently really works out so far are library ordering codes , which are very , very coarse grain , so you have some like , science , biology , and then  But that 's really all that we have at the moment. So I think there 's a huge , um , uh need for improvement there. Now , what this uh  a standard like this would give us is we could um , sort of uh with a search engine just query uh , different repositories all over the world. But we can't really  Um , so what I 'm  what I try to do is um , to have um , uh  So. So the scenario is the following , you you 're working on some sort of project and you encounter a certain problem. Now , what  what we have at our university quite a bit is that uh , students um , try to u program a certain assignment , for example , they always run into the same problems , uh , and they always come running to us , and they 'll say why 's it not  it 's not working , and we always give out the same answer , so we thought , well , it 'd be nice to have a system that could sort of take care of this , and so , what I want to build is basically a  a smart F A Q system. Now , what you uh need to do here is you need to provide some context information which is more elaborate than " I 'm looking for this and this and this keyword. " So. And I think that I don't need to tell you this. I 'm  I 'm sure you have the same  when  when somebody utters a sentence in a certain , uh , context it , and  and the same sentence in another context makes a huge difference. So , I want to be able to model information like , um , so in the  in the context of  in the context of developing distributed systems , of a at a computer science school , um , what kind of software is the person using , which homework assignment is he or she working on at the moment , um , maybe what 's the background of that student 's um , which um , which error message was encountered. So this sort of information I think should be transmitted , uh , when a certain document is retrieved. Now , um , basically giving this um  Uh so we somehow need to have a formalized um , way of writing this down basically , and that 's where the shared interpretation of  of certain terms and keywords comes in again. And , using this and some  some uh , knowledge about the domain I think you can do some  some simple inferences. Like you know that when somebody 's working about  uh , working on  on servlets for example , he 's using Java , cuz servlets are used  are written in Java. So some  some inferences like that , now , um , u using this you can infer more information , and you could then match this to the meta - data of um  off the documents you 're  you 're searching against. So , uh what I wanna do is basically have some sort of um  given these inputs , and then I can compute how many documents match , and use this as a metric in the search. Now , what I plan to do is I want to uh sort of do a uh  uh  try to improve the quality of the search results , and I want to do this by having a depth uh , um , um  steepest descent approach. So if I knew which operating system the person was working on , would this improve my search result ? And  and having uh , uh a symbolic formalized model of this I could simply compute that , and find out which um  which questions are worth um , asking. And that 's what I then propagate back to the user , and  and sort of try to optimize the search in this way. Now , the big problem that I 'm facing right now is um , it 's fairly easy to hack up a system uh quickly , that  that works in the small domain , but the problem is obviously the scalability. And uh uh , so Robert was mentioning uh , earlier today is that uh , Microsoft for example with their printer set up program has a Bayesian network , which does exactly this , but there you face a problem that these are very hard to extend. And so , uh what I 'm  What I try to do is basically try to model this uh , in a way that you could really combine uh , knowledge from very different sources , and  and um , sort of looking into some of the ideas that the semantic web community uh , came up with. Trying to  to have uh , an approach how to integrate s uh certain uh  representation of certain concepts and also some computational rules , um , what you can do with those. Um. What I 'm also looking into is a probabilistic approach into this because document retrievals is a very fuzzy procedure , so it 's probably not that easy to simply have a symbolic uh , computational model. That  that probably isn't expressive enough. So. So that 's another thing , um , which I think you 're also uh , uh looking into right now. And then um , uh sort of as an add - on to this whole idea , um , uh that would be now , depending on what the search engine or the content repository  depending on which  um , uh , which uh , rules and which ontologies it  it uses , or basically its view of the world , uh you can get very different results. So it might ma make a lot of sense to actually query a lot of different search engines. And there you could have an idea where you actually have sort of a  a peer to peer approach , where we 're all sort of carrying around our individual bookshelves , and um , if you have a question about a homework , it 's  probably makes sense to ask somebody who 's in your class with you , sort of the guru in the certain area , rather than going to some Yahoo - like uh , search engine. So these are some of the  just in a nutshell , some of the ideas. And I think a lot of the  even though it 's a  it 's a very different domain , but I think a lot of the , um , issues are  are fairly similar. So. OK.

And so some of the  I don't know how much you know about the larger Heidelberg project , I  Are you


Good. Then , we can move on and see what Andreas has got out his sleeve. Or Andy , for that matter ?

OK. So uh , uh , well , thanks for having me here , first of all. Um , so maybe just a  a little background on  on my visit. So , uh , I 'm not really involved in any project , that 's uh  that 's relevant to you uh , a at the moment , uh , the  the reason is really for me uh , to have an opportunity to talk to some other researchers in the field. And  and so I 'll just n sort of give you a real quick introduction to what I 'm working on , and um , I just hope that you have some comments or , maybe you 're interested in it to find out more , and  and so I 'll be uh , happy to talk to you and  and uh , I 'd also like to find out some more and  and maybe I 'll just walk around the office and and then  and ask some  some questions , uh , in a couple days. So I 'll be here for uh , tomorrow and then uh , the remainder of uh , next week. OK , so , um , what I started looking at , uh , to begin with is just uh , content management systems uh , i i in general. So um , uh what 's uh  Sort of the state of the art there is to um  uh you have a bunch of  of uh documents or learning units or learning objects , um , and you store meta - data uh , associate to them. So there 's some international standards like the I - triple - E , uh  There 's an I - triple - E , LON standard , and um , these fields are pretty straightforward , you have uh author information , you have uh , size information , format information and so on. Uh , but they 're two uh fields that are um , more interesting. One is uh you store keywords associated with the uh  with the document , and one is uh , you have sort of a , um , well , what is the document about ? So it 's some sort of taxonomic uh , ordering of  of the  of the units. Now , if you sort of put on your semantic glasses , uh you say , well that 's not all that easy , because there 's an implicit um , uh , assumption behind that is that uh , all the users of this system share the same interpretation of the keyword and the same interpretation of uh , whichever taxonomy is used , and uh , I think that 's a  that 's a very  that 's a key point of these systems and they sort of always brush over this real quickly without really elaborating much of that and uh  As a matter of fact , the only thing that m apparently really works out so far are library ordering codes , which are very , very coarse grain , so you have some like , science , biology , and then  But that 's really all that we have at the moment. So I think there 's a huge , um , uh need for improvement there. Now , what this uh  a standard like this would give us is we could um , sort of uh with a search engine just query uh , different repositories all over the world. But we can't really  Um , so what I 'm  what I try to do is um , to have um , uh  So. So the scenario is the following , you you 're working on some sort of project and you encounter a certain problem. Now , what  what we have at our university quite a bit is that uh , students um , try to u program a certain assignment , for example , they always run into the same problems , uh , and they always come running to us , and they 'll say why 's it not  it 's not working , and we always give out the same answer , so we thought , well , it 'd be nice to have a system that could sort of take care of this , and so , what I want to build is basically a  a smart F A Q system. Now , what you uh need to do here is you need to provide some context information which is more elaborate than " I 'm looking for this and this and this keyword. " So. And I think that I don't need to tell you this. I 'm  I 'm sure you have the same  when  when somebody utters a sentence in a certain , uh , context it , and  and the same sentence in another context makes a huge difference. So , I want to be able to model information like , um , so in the  in the context of  in the context of developing distributed systems , of a at a computer science school , um , what kind of software is the person using , which homework assignment is he or she working on at the moment , um , maybe what 's the background of that student 's um , which um , which error message was encountered. So this sort of information I think should be transmitted , uh , when a certain document is retrieved. Now , um , basically giving this um  Uh so we somehow need to have a formalized um , way of writing this down basically , and that 's where the shared interpretation of  of certain terms and keywords comes in again. And , using this and some  some uh , knowledge about the domain I think you can do some  some simple inferences. Like you know that when somebody 's working about  uh , working on  on servlets for example , he 's using Java , cuz servlets are used  are written in Java. So some  some inferences like that , now , um , u using this you can infer more information , and you could then match this to the meta - data of um  off the documents you 're  you 're searching against. So , uh what I wanna do is basically have some sort of um  given these inputs , and then I can compute how many documents match , and use this as a metric in the search. Now , what I plan to do is I want to uh sort of do a uh  uh  try to improve the quality of the search results , and I want to do this by having a depth uh , um , um  steepest descent approach. So if I knew which operating system the person was working on , would this improve my search result ? And  and having uh , uh a symbolic formalized model of this I could simply compute that , and find out which um  which questions are worth um , asking. And that 's what I then propagate back to the user , and  and sort of try to optimize the search in this way. Now , the big problem that I 'm facing right now is um , it 's fairly easy to hack up a system uh quickly , that  that works in the small domain , but the problem is obviously the scalability. And uh uh , so Robert was mentioning uh , earlier today is that uh , Microsoft for example with their printer set up program has a Bayesian network , which does exactly this , but there you face a problem that these are very hard to extend. And so , uh what I 'm  What I try to do is basically try to model this uh , in a way that you could really combine uh , knowledge from very different sources , and  and um , sort of looking into some of the ideas that the semantic web community uh , came up with. Trying to  to have uh , an approach how to integrate s uh certain uh  representation of certain concepts and also some computational rules , um , what you can do with those. Um. What I 'm also looking into is a probabilistic approach into this because document retrievals is a very fuzzy procedure , so it 's probably not that easy to simply have a symbolic uh , computational model. That  that probably isn't expressive enough. So. So that 's another thing , um , which I think you 're also uh , uh looking into right now. And then um , uh sort of as an add - on to this whole idea , um , uh that would be now , depending on what the search engine or the content repository  depending on which  um , uh , which uh , rules and which ontologies it  it uses , or basically its view of the world , uh you can get very different results. So it might ma make a lot of sense to actually query a lot of different search engines. And there you could have an idea where you actually have sort of a  a peer to peer approach , where we 're all sort of carrying around our individual bookshelves , and um , if you have a question about a homework , it 's  probably makes sense to ask somebody who 's in your class with you , sort of the guru in the certain area , rather than going to some Yahoo - like uh , search engine. So these are some of the  just in a nutshell , some of the ideas. And I think a lot of the  even though it 's a  it 's a very different domain , but I think a lot of the , um , issues are  are fairly similar. So. OK.

And so some of the  I don't know how much you know about the larger Heidelberg project , I  Are you


OK. So uh , uh , well , thanks for having me here , first of all. Um , so maybe just a  a little background on  on my visit. So , uh , I 'm not really involved in any project , that 's uh  that 's relevant to you uh , a at the moment , uh , the  the reason is really for me uh , to have an opportunity to talk to some other researchers in the field. And  and so I 'll just n sort of give you a real quick introduction to what I 'm working on , and um , I just hope that you have some comments or , maybe you 're interested in it to find out more , and  and so I 'll be uh , happy to talk to you and  and uh , I 'd also like to find out some more and  and maybe I 'll just walk around the office and and then  and ask some  some questions , uh , in a couple days. So I 'll be here for uh , tomorrow and then uh , the remainder of uh , next week. OK , so , um , what I started looking at , uh , to begin with is just uh , content management systems uh , i i in general. So um , uh what 's uh  Sort of the state of the art there is to um  uh you have a bunch of  of uh documents or learning units or learning objects , um , and you store meta - data uh , associate to them. So there 's some international standards like the I - triple - E , uh  There 's an I - triple - E , LON standard , and um , these fields are pretty straightforward , you have uh author information , you have uh , size information , format information and so on. Uh , but they 're two uh fields that are um , more interesting. One is uh you store keywords associated with the uh  with the document , and one is uh , you have sort of a , um , well , what is the document about ? So it 's some sort of taxonomic uh , ordering of  of the  of the units. Now , if you sort of put on your semantic glasses , uh you say , well that 's not all that easy , because there 's an implicit um , uh , assumption behind that is that uh , all the users of this system share the same interpretation of the keyword and the same interpretation of uh , whichever taxonomy is used , and uh , I think that 's a  that 's a very  that 's a key point of these systems and they sort of always brush over this real quickly without really elaborating much of that and uh  As a matter of fact , the only thing that m apparently really works out so far are library ordering codes , which are very , very coarse grain , so you have some like , science , biology , and then  But that 's really all that we have at the moment. So I think there 's a huge , um , uh need for improvement there. Now , what this uh  a standard like this would give us is we could um , sort of uh with a search engine just query uh , different repositories all over the world. But we can't really  Um , so what I 'm  what I try to do is um , to have um , uh  So. So the scenario is the following , you you 're working on some sort of project and you encounter a certain problem. Now , what  what we have at our university quite a bit is that uh , students um , try to u program a certain assignment , for example , they always run into the same problems , uh , and they always come running to us , and they 'll say why 's it not  it 's not working , and we always give out the same answer , so we thought , well , it 'd be nice to have a system that could sort of take care of this , and so , what I want to build is basically a  a smart F A Q system. Now , what you uh need to do here is you need to provide some context information which is more elaborate than " I 'm looking for this and this and this keyword. " So. And I think that I don't need to tell you this. I 'm  I 'm sure you have the same  when  when somebody utters a sentence in a certain , uh , context it , and  and the same sentence in another context makes a huge difference. So , I want to be able to model information like , um , so in the  in the context of  in the context of developing distributed systems , of a at a computer science school , um , what kind of software is the person using , which homework assignment is he or she working on at the moment , um , maybe what 's the background of that student 's um , which um , which error message was encountered. So this sort of information I think should be transmitted , uh , when a certain document is retrieved. Now , um , basically giving this um  Uh so we somehow need to have a formalized um , way of writing this down basically , and that 's where the shared interpretation of  of certain terms and keywords comes in again. And , using this and some  some uh , knowledge about the domain I think you can do some  some simple inferences. Like you know that when somebody 's working about  uh , working on  on servlets for example , he 's using Java , cuz servlets are used  are written in Java. So some  some inferences like that , now , um , u using this you can infer more information , and you could then match this to the meta - data of um  off the documents you 're  you 're searching against. So , uh what I wanna do is basically have some sort of um  given these inputs , and then I can compute how many documents match , and use this as a metric in the search. Now , what I plan to do is I want to uh sort of do a uh  uh  try to improve the quality of the search results , and I want to do this by having a depth uh , um , um  steepest descent approach. So if I knew which operating system the person was working on , would this improve my search result ? And  and having uh , uh a symbolic formalized model of this I could simply compute that , and find out which um  which questions are worth um , asking. And that 's what I then propagate back to the user , and  and sort of try to optimize the search in this way. Now , the big problem that I 'm facing right now is um , it 's fairly easy to hack up a system uh quickly , that  that works in the small domain , but the problem is obviously the scalability. And uh uh , so Robert was mentioning uh , earlier today is that uh , Microsoft for example with their printer set up program has a Bayesian network , which does exactly this , but there you face a problem that these are very hard to extend. And so , uh what I 'm  What I try to do is basically try to model this uh , in a way that you could really combine uh , knowledge from very different sources , and  and um , sort of looking into some of the ideas that the semantic web community uh , came up with. Trying to  to have uh , an approach how to integrate s uh certain uh  representation of certain concepts and also some computational rules , um , what you can do with those. Um. What I 'm also looking into is a probabilistic approach into this because document retrievals is a very fuzzy procedure , so it 's probably not that easy to simply have a symbolic uh , computational model. That  that probably isn't expressive enough. So. So that 's another thing , um , which I think you 're also uh , uh looking into right now. And then um , uh sort of as an add - on to this whole idea , um , uh that would be now , depending on what the search engine or the content repository  depending on which  um , uh , which uh , rules and which ontologies it  it uses , or basically its view of the world , uh you can get very different results. So it might ma make a lot of sense to actually query a lot of different search engines. And there you could have an idea where you actually have sort of a  a peer to peer approach , where we 're all sort of carrying around our individual bookshelves , and um , if you have a question about a homework , it 's  probably makes sense to ask somebody who 's in your class with you , sort of the guru in the certain area , rather than going to some Yahoo - like uh , search engine. So these are some of the  just in a nutshell , some of the ideas. And I think a lot of the  even though it 's a  it 's a very different domain , but I think a lot of the , um , issues are  are fairly similar. So. OK.

And so some of the  I don't know how much you know about the larger Heidelberg project , I  Are you

So it seems like a lot of  some of the issues are the same. It 's like , um , you know , the c context - based factors that influence how you interpret ,


And so some of the  I don't know how much you know about the larger Heidelberg project , I  Are you

So it seems like a lot of  some of the issues are the same. It 's like , um , you know , the c context - based factors that influence how you interpret ,


So it seems like a lot of  some of the issues are the same. It 's like , um , you know , the c context - based factors that influence how you interpret ,

um , s how to interpret. In  in this case , infer in in knowing  wanting to know what kinds of things to ask. We - we 've kind of talked about that , but we haven't worried too much about that end of the discourse.


um , s how to interpret. In  in this case , infer in in knowing  wanting to know what kinds of things to ask. We - we 've kind of talked about that , but we haven't worried too much about that end of the discourse.

But maybe you guys had that in the previous models.


um , s how to interpret. In  in this case , infer in in knowing  wanting to know what kinds of things to ask. We - we 've kind of talked about that , but we haven't worried too much about that end of the discourse.

But maybe you guys had that in the previous models.

Well , in a  in one  t one s mmm , small difference in a  in a way , is that he doesn't have to come up with an answer , but he wants to point to the places w w


But maybe you guys had that in the previous models.

Well , in a  in one  t one s mmm , small difference in a  in a way , is that he doesn't have to come up with an answer , but he wants to point to the places w w


But maybe you guys had that in the previous models.

Well , in a  in one  t one s mmm , small difference in a  in a way , is that he doesn't have to come up with an answer , but he wants to point to the places w w

Yeah , so. So I 'm  I 'm not  I 'm not building an expert


Well , in a  in one  t one s mmm , small difference in a  in a way , is that he doesn't have to come up with an answer , but he wants to point to the places w w

Yeah , so. So I 'm  I 'm not  I 'm not building an expert


Yeah , so. So I 'm  I 'm not  I 'm not building an expert

Uh , I want to build a smart librarian , basically


Uh , I want to build a smart librarian , basically

that can point you to the right reference. I don't wanna compute the answer , so it 's a little bit easier for me.


Uh , I want to build a smart librarian , basically

that can point you to the right reference. I don't wanna compute the answer , so it 's a little bit easier for me.

Well. Uh , you have to s still m understand what the content says about itself , and then match it to what you think the informational needs


that can point you to the right reference. I don't wanna compute the answer , so it 's a little bit easier for me.

Well. Uh , you have to s still m understand what the content says about itself , and then match it to what you think the informational needs


Well. Uh , you have to s still m understand what the content says about itself , and then match it to what you think the informational needs

So you also don't have to figure out what the content is. You 're just taking the keywords as a topic text , as


So you also don't have to figure out what the content is. You 're just taking the keywords as a topic text , as

I  I assume that  that the there will be learning systems that  that tag their  their content.


So you also don't have to figure out what the content is. You 're just taking the keywords as a topic text , as

I  I assume that  that the there will be learning systems that  that tag their  their content.

And um , um , m @ @ and basically what I  what I envision is that you  rather than just supplying a bunch of keywords you could basically  for  for an FAQ for example you could state sort of like a logic condition , when this document applies. So " this document explains how to set up your uh , mail account on Linux " or something like this.


I  I assume that  that the there will be learning systems that  that tag their  their content.

And um , um , m @ @ and basically what I  what I envision is that you  rather than just supplying a bunch of keywords you could basically  for  for an FAQ for example you could state sort of like a logic condition , when this document applies. So " this document explains how to set up your uh , mail account on Linux " or something like this.


And um , um , m @ @ and basically what I  what I envision is that you  rather than just supplying a bunch of keywords you could basically  for  for an FAQ for example you could state sort of like a logic condition , when this document applies. So " this document explains how to set up your uh , mail account on Linux " or something like this.

So. So something  something very specific that you can then  But the  I think that the key point with these uh , learning systems is that uh , a learning system is only as good as uh the amount of content it  it carries.


So. So something  something very specific that you can then  But the  I think that the key point with these uh , learning systems is that uh , a learning system is only as good as uh the amount of content it  it carries.

You can have the best learning system with the best search interface , if there 's no content inside of it , it 's not very useful. So I think ultimately because um , uh developing these  these rules and these inference uh  inferences I think is very costly , so um , uh I think you must be able to reuse some  some existing um , domain  domain information , or  or  or ontologies that  that uh other people wrote and then try to integrate them , and then also search the entire web basically , rather than just the small uh , content management system.


You can have the best learning system with the best search interface , if there 's no content inside of it , it 's not very useful. So I think ultimately because um , uh developing these  these rules and these inference uh  inferences I think is very costly , so um , uh I think you must be able to reuse some  some existing um , domain  domain information , or  or  or ontologies that  that uh other people wrote and then try to integrate them , and then also search the entire web basically , rather than just the small uh , content management system.

So I think that 's  that 's crucial for  for the success of  or @ @


You can have the best learning system with the best search interface , if there 's no content inside of it , it 's not very useful. So I think ultimately because um , uh developing these  these rules and these inference uh  inferences I think is very costly , so um , uh I think you must be able to reuse some  some existing um , domain  domain information , or  or  or ontologies that  that uh other people wrote and then try to integrate them , and then also search the entire web basically , rather than just the small uh , content management system.

So I think that 's  that 's crucial for  for the success of  or @ @

So , you 're not  I guess I 'm trying to figure out how  how it maps to the kinds of things that we 've talked about in this group , and , actually associated groups ,


So I think that 's  that 's crucial for  for the success of  or @ @

So , you 're not  I guess I 'm trying to figure out how  how it maps to the kinds of things that we 've talked about in this group , and , actually associated groups ,


So I think that 's  that 's crucial for  for the success of  or @ @

So , you 're not  I guess I 'm trying to figure out how  how it maps to the kinds of things that we 've talked about in this group , and , actually associated groups ,

cuz some of us do pretty detailed linguistic analyses , and I 'm guessing that you  you won't be doing that ? OK.


So , you 're not  I guess I 'm trying to figure out how  how it maps to the kinds of things that we 've talked about in this group , and , actually associated groups ,

cuz some of us do pretty detailed linguistic analyses , and I 'm guessing that you  you won't be doing that ? OK.


cuz some of us do pretty detailed linguistic analyses , and I 'm guessing that you  you won't be doing that ? OK.


On the other hand , uh , FrameNet could well be useful. So do you know the FrameNet story ?


Oh. Th - that 's another thing you might wanna look into while you 're here.


Oh. Th - that 's another thing you might wanna look into while you 're here.

Because , um , you know , the standard story is that keyworks  keywords evoke frames , and the frames may well give you additional keywords or uh , if you know that  that  that a  a bunch of keywords uh , indicate a frame , then you can find documents that actually have the whole frame , rather th than just uh , individual


Because , um , you know , the standard story is that keyworks  keywords evoke frames , and the frames may well give you additional keywords or uh , if you know that  that  that a  a bunch of keywords uh , indicate a frame , then you can find documents that actually have the whole frame , rather th than just uh , individual

So there 's a lot of stuff , and people are looking at that. Most of the work here is just trying to get the frames right. There 's linguists and stuff and there 's a lot of it and they 're  they 're busily working away. But there are some application efforts trying to exploit it. And this looks t it seems to be that this is a place where you might be able to do that.


Because , um , you know , the standard story is that keyworks  keywords evoke frames , and the frames may well give you additional keywords or uh , if you know that  that  that a  a bunch of keywords uh , indicate a frame , then you can find documents that actually have the whole frame , rather th than just uh , individual

So there 's a lot of stuff , and people are looking at that. Most of the work here is just trying to get the frames right. There 's linguists and stuff and there 's a lot of it and they 're  they 're busily working away. But there are some application efforts trying to exploit it. And this looks t it seems to be that this is a place where you might be able to do that.

Yeah. Yeah. Yeah. I 'm sure I could learn a lot about um , yeah , just how to  how to come up with these structures ,


So there 's a lot of stuff , and people are looking at that. Most of the work here is just trying to get the frames right. There 's linguists and stuff and there 's a lot of it and they 're  they 're busily working away. But there are some application efforts trying to exploit it. And this looks t it seems to be that this is a place where you might be able to do that.

Yeah. Yeah. Yeah. I 'm sure I could learn a lot about um , yeah , just how to  how to come up with these structures ,


So there 's a lot of stuff , and people are looking at that. Most of the work here is just trying to get the frames right. There 's linguists and stuff and there 's a lot of it and they 're  they 're busily working away. But there are some application efforts trying to exploit it. And this looks t it seems to be that this is a place where you might be able to do that.

Yeah. Yeah. Yeah. I 'm sure I could learn a lot about um , yeah , just how to  how to come up with these structures ,

cuz it 's  it 's very easy to whip up something quickly , but it maybe then makes sense to  to me , but not to anybody else , and  and if we want to share and integrate things , they must  well , they must be well designed really.


Yeah. Yeah. Yeah. I 'm sure I could learn a lot about um , yeah , just how to  how to come up with these structures ,

cuz it 's  it 's very easy to whip up something quickly , but it maybe then makes sense to  to me , but not to anybody else , and  and if we want to share and integrate things , they must  well , they must be well designed really.


cuz it 's  it 's very easy to whip up something quickly , but it maybe then makes sense to  to me , but not to anybody else , and  and if we want to share and integrate things , they must  well , they must be well designed really.


cuz it 's  it 's very easy to whip up something quickly , but it maybe then makes sense to  to me , but not to anybody else , and  and if we want to share and integrate things , they must  well , they must be well designed really.

The absolutely no  no linguistic background person that the IU sent over here.


The absolutely no  no linguistic background person that the IU sent over here.


The absolutely no  no linguistic background person that the IU sent over here.

And Andreas and I tried to come up wi or we had come up actually with a eh  with him working on an interface for FrameNet , as it was back then , that would p do some of the work for this machine ,


And Andreas and I tried to come up wi or we had come up actually with a eh  with him working on an interface for FrameNet , as it was back then , that would p do some of the work for this machine ,

which uh , never got done because Prashant found a happy occupation


And Andreas and I tried to come up wi or we had come up actually with a eh  with him working on an interface for FrameNet , as it was back then , that would p do some of the work for this machine ,

which uh , never got done because Prashant found a happy occupation

W yeah , I know , I mean it  it  he  w he did w what  what he did was much more s sensible for him.


which uh , never got done because Prashant found a happy occupation

W yeah , I know , I mean it  it  he  w he did w what  what he did was much more s sensible for him.


W yeah , I know , I mean it  it  he  w he did w what  what he did was much more s sensible for him.

But so  I 'm just saying , the uh , we had that idea


But so  I 'm just saying , the uh , we had that idea

you know  Yeah. The idea was there. Yeah , OK.


But so  I 'm just saying , the uh , we had that idea

you know  Yeah. The idea was there. Yeah , OK.

uh to  to exploit FrameNet there as well.


you know  Yeah. The idea was there. Yeah , OK.

uh to  to exploit FrameNet there as well.


uh to  to exploit FrameNet there as well.


And Srini 's doing information extraction also , right ?


So you  you guys never sent anybody else from I U.


Uh , this was supposedly an exchange program , and  I  we  you know , it 's fine. We don't care , but it just  I 'm a little surprised that uh , Andreas didn't come up with anyone else he wanted to send.


Uh , this was supposedly an exchange program , and  I  we  you know , it 's fine. We don't care , but it just  I 'm a little surprised that uh , Andreas didn't come up with anyone else he wanted to send.

Alright. I mean I had forgotten a I  To be honest with you , I 'd totally forgotten we had a program.


Alright. I mean I had forgotten a I  To be honest with you , I 'd totally forgotten we had a program.


Alright. I mean I had forgotten a I  To be honest with you , I 'd totally forgotten we had a program.

Uh I  I think it 's  it 's really the lack of students uh , at IU at the moment.


Alright. I mean I had forgotten a I  To be honest with you , I 'd totally forgotten we had a program.

Uh I  I think it 's  it 's really the lack of students uh , at IU at the moment.

Yeah. Yeah. No , no. There was a whole co There was a little contract signed. It was  Yeah.


Uh I  I think it 's  it 's really the lack of students uh , at IU at the moment.

Yeah. Yeah. No , no. There was a whole co There was a little contract signed. It was  Yeah.

Yeah , yeah. I think it 's ju it 's more the lack of  of students , really , and w we have all these sponsors that are always sort of eager to get some teams.


Yeah. Yeah. No , no. There was a whole co There was a little contract signed. It was  Yeah.

Yeah , yeah. I think it 's ju it 's more the lack of  of students , really , and w we have all these sponsors that are always sort of eager to get some teams.


Yeah , yeah. I think it 's ju it 's more the lack of  of students , really , and w we have all these sponsors that are always sort of eager to get some teams.


Well I mean if  if I were a student , I 'd love to come here , rather than work for some German   company , or


Well I mean if  if I were a student , I 'd love to come here , rather than work for some German   company , or

You are being recorded right now , so beware.


You are being recorded right now , so beware.

Well , I didn't say anybody to  anything to offend  well , except for the sponsors maybe , but


You are being recorded right now , so beware.

Well , I didn't say anybody to  anything to offend  well , except for the sponsors maybe , but

Right. Anyway. Right. So I thi tha that 's  that 's one of the things that might be worth looking into while you 're here.


Well , I didn't say anybody to  anything to offend  well , except for the sponsors maybe , but

Right. Anyway. Right. So I thi tha that 's  that 's one of the things that might be worth looking into while you 're here.


Well , I didn't say anybody to  anything to offend  well , except for the sponsors maybe , but

Right. Anyway. Right. So I thi tha that 's  that 's one of the things that might be worth looking into while you 're here.

Uh , unfortunately , Srini , who is heavily involved in DAML and all this sort of stuff is himself out of town.


Right. Anyway. Right. So I thi tha that 's  that 's one of the things that might be worth looking into while you 're here.

Uh , unfortunately , Srini , who is heavily involved in DAML and all this sort of stuff is himself out of town.

Mm - hmm. Well I 'll go to the uh , Semantic Web Workshop , uh , in two weeks.


Uh , unfortunately , Srini , who is heavily involved in DAML and all this sort of stuff is himself out of town.

Mm - hmm. Well I 'll go to the uh , Semantic Web Workshop , uh , in two weeks.

Right , and  Yeah , for  for some reason he 's not doing that.


Mm - hmm. Well I 'll go to the uh , Semantic Web Workshop , uh , in two weeks.

Right , and  Yeah , for  for some reason he 's not doing that.

I don't know why he @ @  oh , I , who knows ?


Right , and  Yeah , for  for some reason he 's not doing that.

I don't know why he @ @  oh , I , who knows ?


I don't know why he @ @  oh , I , who knows ?

Anyway , s yeah , you 'll see  you 'll certainly see a lot of the people there.


I don't know why he @ @  oh , I , who knows ?

Anyway , s yeah , you 'll see  you 'll certainly see a lot of the people there.

The other person I thought of is Dan Gildea ? because he did some work on topic spotting


Anyway , s yeah , you 'll see  you 'll certainly see a lot of the people there.

The other person I thought of is Dan Gildea ? because he did some work on topic spotting

Yeah. St - statistical stuff. That would be a very good idea.


Anyway , s yeah , you 'll see  you 'll certainly see a lot of the people there.

The other person I thought of is Dan Gildea ? because he did some work on topic spotting

Yeah. St - statistical stuff. That would be a very good idea.

w um , which is , I mean , you  I mean. I don't  Depending on how well you wanna integrate with that end ,


The other person I thought of is Dan Gildea ? because he did some work on topic spotting

Yeah. St - statistical stuff. That would be a very good idea.

w um , which is , I mean , you  I mean. I don't  Depending on how well you wanna integrate with that end ,


Yeah. St - statistical stuff. That would be a very good idea.

w um , which is , I mean , you  I mean. I don't  Depending on how well you wanna integrate with that end ,

you know , like , taking the data and fig you said the learning systems that figure out  We  There 's someone in ICSI who actually has been working on  has worked on that kinda stuff , and he 's worked with frame net , so you could talk to him about , you know , both of those things at once.


w um , which is , I mean , you  I mean. I don't  Depending on how well you wanna integrate with that end ,

you know , like , taking the data and fig you said the learning systems that figure out  We  There 's someone in ICSI who actually has been working on  has worked on that kinda stuff , and he 's worked with frame net , so you could talk to him about , you know , both of those things at once.


you know , like , taking the data and fig you said the learning systems that figure out  We  There 's someone in ICSI who actually has been working on  has worked on that kinda stuff , and he 's worked with frame net , so you could talk to him about , you know , both of those things at once.

So. And he just finished writing a draft of his thesis. So. I u  Dan Gildea , GILDEA.


So. And he just finished writing a draft of his thesis. So. I u  Dan Gildea , GILDEA.

And , he 's in one of the rooms on the fifth floor and stuff ,


And , he 's in one of the rooms on the fifth floor and stuff ,


Hmm. Well , if you fal solve the problem ,  hope you can do one for us too.


Hmm. Well , if you fal solve the problem ,  hope you can do one for us too.

Alright , was there anything else for this ? One of these times soon we 're gonna hear about construal.


Hmm. Well , if you fal solve the problem ,  hope you can do one for us too.

Alright , was there anything else for this ? One of these times soon we 're gonna hear about construal.

Yeah. I 'm sure. I have um  I think it was November two thousand three or some  No. Wh - I had something in my calendar.


Alright , was there anything else for this ? One of these times soon we 're gonna hear about construal.

Yeah. I 'm sure. I have um  I think it was November two thousand three or some  No. Wh - I had something in my calendar.


Yeah. I 'm sure. I have um  I think it was November two thousand three or some  No. Wh - I had something in my calendar.


Uh well , maybe I can  I can bribe my way out of this. So. So I did some double checking and it seems like spring break in two thousand  one.


Uh well , maybe I can  I can bribe my way out of this. So. So I did some double checking and it seems like spring break in two thousand  one.

Well , no , but he 's  he 's  he 's  he 's  as you said , he 's , like the state legislature , he 's trying to offer us bribes.


Well , no , but he 's  he 's  he 's  he 's  as you said , he 's , like the state legislature , he 's trying to offer us bribes.

At least this is a private meeting. Right , exactly , OK , that 's the link.


Well , no , but he 's  he 's  he 's  he 's  as you said , he 's , like the state legislature , he 's trying to offer us bribes.

At least this is a private meeting. Right , exactly , OK , that 's the link.

This uh  Oh , they refused the budget again ? Is it  so about CITRIS ? Yeah , still nothing.


Well , no , but he 's  he 's  he 's  he 's  as you said , he 's , like the state legislature , he 's trying to offer us bribes.

At least this is a private meeting. Right , exactly , OK , that 's the link.

This uh  Oh , they refused the budget again ? Is it  so about CITRIS ? Yeah , still nothing.

Uh , this  this  this  t the s we 're , uh , involved in a literally three hundred million dollar uh , program. Uh , with the State of California. And , the State of California is now a month and a half behind its legis its legally required date to approve a budget. So the budget has not been approved. And two days ago  There 's two l you know , so , two branches of legislature. One branch approved it ,


At least this is a private meeting. Right , exactly , OK , that 's the link.

This uh  Oh , they refused the budget again ? Is it  so about CITRIS ? Yeah , still nothing.

Uh , this  this  this  t the s we 're , uh , involved in a literally three hundred million dollar uh , program. Uh , with the State of California. And , the State of California is now a month and a half behind its legis its legally required date to approve a budget. So the budget has not been approved. And two days ago  There 's two l you know , so , two branches of legislature. One branch approved it ,


This uh  Oh , they refused the budget again ? Is it  so about CITRIS ? Yeah , still nothing.

Uh , this  this  this  t the s we 're , uh , involved in a literally three hundred million dollar uh , program. Uh , with the State of California. And , the State of California is now a month and a half behind its legis its legally required date to approve a budget. So the budget has not been approved. And two days ago  There 's two l you know , so , two branches of legislature. One branch approved it ,

and , um , yesterdayday  there was this uh  uh I thought that the other branch would just approve it , but now there 's actually a little back sliding to people who  who approved it got flak from there , eh anyway. So , um  Oh ! I have to tell you a wonderful story about this , OK ? And then we 'll go. So , I  it turns out I wound up having lunch today with a guy named Tom Kalil. KILL  KALIL. And , uh , he now works at Berkeley. In fact he 's hired to run a lot of CITRIS , even though we don't have the money they  So they 've been hiring people right and left , so , uh , they think the money 's coming. So  and he was , I think , the chief staffer to Clinton on technology matters. He was in the White House , I don't remember what he was saying. A anyway , like that. And , is now doing all the politics for CITRIS , but also , has a uh , a lot of interest in uh , actually doing things for society , so digital divide and stuff like that. So that 's s interesting to me but maybe not to you. But the really interesting thing was , he st he s he s said something about , you know I 'm interested in things that have high social multiplier , something that is of great social value. He said , " for example " , this was his only example , " if you had a adult literacy program that was as good as an individual tutor , and as compelling as a video game , then that would have a huge social impact ". I said , " Oh great ! That 's a good problem to work on. " Anyway. So it was nice that uh , he 's got this view , of A , that 's what you should try to do , and B , uh , language would be a good way to do it.


Uh , this  this  this  t the s we 're , uh , involved in a literally three hundred million dollar uh , program. Uh , with the State of California. And , the State of California is now a month and a half behind its legis its legally required date to approve a budget. So the budget has not been approved. And two days ago  There 's two l you know , so , two branches of legislature. One branch approved it ,

and , um , yesterdayday  there was this uh  uh I thought that the other branch would just approve it , but now there 's actually a little back sliding to people who  who approved it got flak from there , eh anyway. So , um  Oh ! I have to tell you a wonderful story about this , OK ? And then we 'll go. So , I  it turns out I wound up having lunch today with a guy named Tom Kalil. KILL  KALIL. And , uh , he now works at Berkeley. In fact he 's hired to run a lot of CITRIS , even though we don't have the money they  So they 've been hiring people right and left , so , uh , they think the money 's coming. So  and he was , I think , the chief staffer to Clinton on technology matters. He was in the White House , I don't remember what he was saying. A anyway , like that. And , is now doing all the politics for CITRIS , but also , has a uh , a lot of interest in uh , actually doing things for society , so digital divide and stuff like that. So that 's s interesting to me but maybe not to you. But the really interesting thing was , he st he s he s said something about , you know I 'm interested in things that have high social multiplier , something that is of great social value. He said , " for example " , this was his only example , " if you had a adult literacy program that was as good as an individual tutor , and as compelling as a video game , then that would have a huge social impact ". I said , " Oh great ! That 's a good problem to work on. " Anyway. So it was nice that uh , he 's got this view , of A , that 's what you should try to do , and B , uh , language would be a good way to do it.