okay.


so let's see.


which one of these buttons will do this for me?


aha!


should you go back to the first one?


do i want to go back to the first one?


well


i'm sorry i


well i mean just to


introduce.


yeah um well the search for the middle layer.


it's basically uh talks about uh it just refers to the fact that uh one of main things we had to do was to decide what the intermediate sort of nodes were.


i can read!


i'm kidding.


uhhuh.


you know because


but if you really want to find out what it's about you have to click on the little light bulb.


although i've i've never i don't know what the light bulb is for.


i didn't install that into my powerpoint presentation.


it opens the assistant that tells you that the font type is too small.


uh.


do you want to try?


ach


i'd prefer not to.


okay.


continue.


it's a needless good idea.


is that the idea?


why are you doing this in this mode and not in the presentation mode?


because i'm going to switch to the javabayes program.


oh okay.


of course.


uhhuh.


and then if i do that it'll mess everything up.


i was wondering.


is that okay?


yeah it's okay.


sure.


can you maximize the window?


proceed.


you want me to


wait what do you want me to do?


can you maximize the window so all that stuff on the side isn't doesn't appear?


no it's okay.


it's it'll work.


well i can do that.


but then i have to end the presentation in the middle so i can go back to open up javabayes.


okay fine.


here let's see if i can


all right.


very nice.


is that better?


yeah.


okay.


uh i'll also get rid of this click to add notes.


perfect.


so then the features we decided or we decided we were talked about.


right?


uh the the prosody the discourse verb choice.


you know we had a list of things like to go and to visit and what not.


the landmark-iness of uh


i knew you'd like that.


nice coinage.


thank you.


uh of a of a building.


whether the


and this we actually have a separate feature.


but i decided to put it on the same line for space.


nice walls which we can look up.


because i mean if you're going to get real close to a building in the tango mode


there's got to be a reason for it.


and it's either because you're in route to something else or you want to look at the walls.


the context which in this case we've limited to business person tourist or unknown.


the time of day.


and open to suggestions isn't actually a feature.


it's we are open to suggestions.


right.


can i just ask the nice walls part of it is that uh in this particular domain


you said be it could be on two different lines.


but are you saying that in this particular domain it happens the that landmark-iness is correlated with


no.


oh


we have a separate


they're separate things.


their being nice


feature.


okay.


yeah.


i either could put nice walls on its own line or open to suggestions off the slide.


and and


like you could have a


by nice you mean


you like you could have a post office with uh you know nice murals or something.


right.


or one time i was at this


so nice walls is a stand in for like architecturally it uh significant.


architecturally appealing from the outside.


or something like that.


but see the thing is if it's


yeah but if it's architecturally significant you might be able to see it from


like you might be able to vista it.


right?


uhhuh.


and be able to


appreciate it.


yeah.


versus like


i was at this place in europe where they had little carvings of like dead people on the walls or something.


i don't remember


it was a long time ago.


there's a lot of those.


but if you looked at it real close you could see the the intricacy of the of the walls.


okay.


so that count as counts as a nice wall.


right.


the


something you want to inspect at close range because it's interesting.


exactly.


huh.


robert?


well there there is a term that's often used.


that's saliency or the salience of an object.


and i was just wondering whether that's the same as what you describe as landmark-iness.


but it's really not.


i mean an object can be very salient.


but not a landmark at all.


not a landmark at all.


there's landmark for um touristic reasons and landmark for


i don't know.


navigational reasons or something.


yep.


yeah.


we meant uh touristic reasons.


okay.


right.


but you can imagine maybe wanting the both kinds of things there for different um goals.


huh.


right?


but


touristy landmarks also happen to be


wouldn't couldn't they also be


they're not exclusive groups.


are they?


like non tourist y landmarks and


or it can be


they're not mutually exclusive?


direct navigational


definitely.


okay so our initial idea was not very satisfying because uh our initial idea was basically all the features pointing to the output node.


uh


so a big flat structure.


right.


yep.


and uh so we


reasons being you know it'd be a pain to set up all the probabilities for that.


if we moved onto the next step and did learning of some sort uh according bhaskara we'd be handicapped.


i don't know belief nets very well.


well usually i mean you know n


if you have n features then it's two to the n or exponential in n.


and they wouldn't look pretty.


so


yeah.


they'd all be like pointing to the one node.


uhhuh.


so then our next idea was to add a middle layer.


right?


so the thinking behind that was we have the features that we've drawn from the communication of some like the someone


the person at the screen is trying to communicate some abstract idea.


like i'm the the abstract idea being i am a tourist.


i want to go to this place.


so we're going to set up features along the lines of where they want to go.


and what they've said previously and whatnot.


and then we have the means that they should use.


but the middle thing we were thinking along the lines of maybe trying to figure out like the concept of whether they're a tourist or whether they're running an errand or something like that.


along those lines.


or


yes we things we couldn't extract the from the data the hidden variables.


yes.


good.


so then the hidden variables hair variables we came up with were whether someone was on a tour running an errand or whether they were in a hurry.


because we were thinking uh if they were in a hurry there'd be less likely to like or


want to do vista.


right?


because if you want to view things you wouldn't be in a hurry.


right.


or they might be more likely to be using the place that they want to go to as a like a navigational point to go to another place.


uhhuh.


whether the destination was their final destination.


whether the destination was closed.


those are all


and then let's look at the belief net okay.


so that means that i should switch to the other program.


um right now it's still kind of in a toy version of it.


because we didn't know the probabilities of or


well i'll talk about it when i get the picture up.


no one knows it.


okay.


so this right what we


let's see.


what happens if i maximize this?


there we go.


but uh


so the mode basically has three different outputs.


the probability whether the probability of a vista tango or enter


um the context we simplified.


basically it's just the businessman the tourist unknown.


verb used is actually personally amusing.


mainly because it's it's just whether the verb is a tango verb an enter verb or a vista verb.


yeah.


that one needs a lot of


and are those mutually exclusive sets?


not at all.


that's that that needs a lot of work.


no.


right.


got it.


but uh that would've made the probably significantly be more complicated to enter.


uhhuh.


so we decided that for the purposes of this it'd be simpler to just have three verbs.


simple.


stab at it.


yep.


um


why don't you mention things about this bhaskara?


that i am not that are not coming to my mind right now.


okay so yeah so note the four nodes down there the sort of the things that are not directly extracted.


actually the five things.


the closed is also not directly extracted i guess.


from the uh


well it's


from the utterance?


huh.


actually no.


it's it sort of is.


wait.


it is.


because it's because have the the time of day.


okay closed sort of is.


and the close


it just had the uh and what time it closed.


right.


so


but the other ones the final destination the whether they're doing business whether they're in a hurry and whether they're tourists.


that kind of thing is all uh sort of you know probabilistically depends on the other things.


inferred from the other ones?


yeah.


okay.


and the mode you know depends on all those things only.


yeah the the actual parse is somewhere up around in here.


so we haven't uh managed


like we don't have nodes for discourse and parse.


although like in some sense they are parts of this belief net.


uhhuh.


but uh the idea is that we just extract those features from them so we don't actually have a node for the entire parse.


because we'd never do inference on it anyway.


so some of the the top row of things


what's what's disc admission fee?


whether they discuss the admission fees.


so we looked at the data.


oh.


and in a lot of data people were saying things like can i get to this place.


what is the admission fee.


so that's like a huge uh clue that they're trying to enter the place rather than uh to tango or vista.


right.


okay.


so


i see.


there were there'd be other things besides just the admission fee.


but you know we didn't have


uhhuh.


that was like our example.


that was the initial one that we found.


so there are certain cues that are very strong either lexical or topic based um concept cues.


from the discourse that


yeah.


for one of those.


and then in that second row or whatever that row of time of day through that so all of those some of them come from the utterance.


and some of them are sort of either world knowledge or situational things.


right?


so that you have no distinction between those and


right.


okay.


one uh uh um


anything else you want to say bhaskara?


um


unmark time of day?


i i mean


one thing uh


they're they're are a couple of more things.


i mean uh i would actually suggest we go through this one more time so we we all uh agree on what what the meaning of these things is at the moment.


and maybe what changes we


yeah


so one thing i i'm you know unsure about is how we have the uh the admission fee thing set up.


so one thing that we were thinking was by doing the layers like this


uh we kept um things from directly affecting the mode beyond the concept.


but you could see perhaps the admission fee going directly to the mode pointing at enter.


uhhuh.


right?


versus pointing just at tourist.


okay?


but we just decided to keep all the things we extracted to point at the middle and then down.


why is the landmark


okay.


the landmark is facing to the tourists.


that's because we're talking about landmarks as touristic landmarks.


right.


not as possible um


yeah.


navigational landmarks.


navigational cue.


so


then


that would be whatever building they referred to.


prosody.


so let's see.


the variables.


admission fee is a binary thing.


uhhuh.


time of day is like morning afternoon night.


is that the deal?


that's how we have it currently set up.


but it could be you know based upon hour.


yep.


or we could discrete it descret-ize it.


whatever granularity.


yeah.


normally context will include a huge amount of information.


but um we are just using the particular part of the context which consists of the switch that they flick to indicate whether they're a tourist or not i guess.


okay.


so that's given in their input.


right.


so


right?


so it's not really all of context.


similarly prosody is not all of prosody.


but simply for our purposes whether or not they appear tense or relaxed.


uhhuh.


that's very nice.


huh?


and


the the so the context is a switch between tourist or non tourist?


or also unknown?


or unknown.


yeah.


unknown.


so final


so it seems like that would really help you for doing business versus tourist.


which is which one?


but


okay.


so the the context being um


i don't know if that question's sort of in general.


are you


i mean the


are


do they allow business people to be doing non business things at the moment?


yeah it does.


so then you just have some probabilities over


everything is probablistic.


and there's always


over which which of those it is.


yeah.


um right.


so then landmark is


oh!


sorry.


verb used is like


right now we only have three values.


but in general they would be a probability distribution over all verbs.


uhhuh.


rather


let me rephrase that.


it it can take values in the set of all verbs that they could possibly use.


um nice walls is binary.


closed is binary.


final destination again


yeah all those are binary i guess.


and mode is one of three things.


so the the middle layer is also binary?


no


yeah.


anything with a question mark after it in that picture is a binary node.


uh it


but all those things without question marks are also binary.


right?


which things?


uhhuh.


nice walls.


oh.


nice walls is uh something that we extract from our world knowledge.


yeah oh.


yeah sorry it is binary.


it is binary.


that's true.


but it doesn't have question mark because it's extracted.


okay i see your point.


okay.


i i gotcha.


yeah similarly closed i guess.


so we can either be in a hurry or not.


but we cannot be in a medium hurry at the moment?


well


to do that we would add another uh value for that.


uhhuh.


and that would require updating the probability distribution for mode as well.


because it would now have to like uh take that possibility into account.


take a


so um of course this will happen when we think more about the kinds of verbs that are used in each cases.


yeah yeah.


but you can imagine that it's verb plus various other things that are also not in the bottom layer that would that would help you.


yeah.


like it's a conjunction of


i don't know.


you know the verb used and some other stuff that that would determine


right.


other syntactic information you mean?


exactly.


um


well the the sort of the landmark is is sort of the object.


right?


the argument in a sense?


usually.


i i don't know if that's always the case.


i i guess haven't looked at the data as much as you guys have.


so um


that's always warping on something some entity.


uhhuh.


and um uh maybe at this stage we will we do want to uh sort of get uh modifiers in there.


huh yeah.


because they may also tell us whether the person is in a hurry or not.


yeah.


i want to get to the church quickly.


and uh


right.


that would be a cue.


what's the fastest way


correct.


um okay.


excellent.


do we have anything else to say about this?


we can do a little demo.


oh the


yeah we could.


but the demo doesn't work very well.


no.


then it wouldn't be a demo.


i mean we can do a demo in the sense that we can um just observe the fact that this will in fact do inference.


i was just going to


observe nodes.


so we can you know set some of the uh nodes.


yeah.


go ahead.


and then try to find the probability of other nodes.


okay.


dat-dat-dah.


what should i observe?


just set a few of them.


you don't have to do the whole thing that we did last time.


just like uh maybe the fact that they use a certain verb.


actually forget the verb.


just uh


i don't know.


say they discussed the admission fee.


and uh the place has nice walls.


i love nice walls.


i'm a big fan.


it's starting to grow on me.


and it's night.


and the time of day is night?


yeah.


no.


wait.


that that doesn't uh


it's not really consistent.


they don't discuss the admission fee.


make that false.


all right.


oh they


okay.


oh whoops.


that didn't work.


i forgot to uh


ach!


i'd like to do that again.


one thing that bugs me about javabayes is you have to click that and do this.


that seems kind of redundant.


but


that all you want?


yes.


so let's see.


i want to query.


go and right query.


right?


the mode.


okay and then on here


so that is the probability that they're entering vista ing or tango ing.


uhhuh.


yeah.


and uh


so slightly biased toward tango ing?


okay.


if it's night time they have not discussed admission fee and the walls are nice.


so yeah.


i guess that sort of makes sense.


the reason i say the demo doesn't work very well is yesterday we uh observed everything in favor of taking a tour.


and it came up as tango.


over and over again.


so


we couldn't we couldn't figure out how to turn it off of tango.


huh!


it loves the tango.


um


well that's obviously just to do with our probabilities.


yeah yeah.


like we totally hand tuned the probabilities.


yeah.


right.


we were like huh well if the person does this and this and this let's say forty percent for this.


okay.


fifty like you know


so obviously that's going to happen.


but it it


maybe the bias toward tango ing was yours then.


it's so we have to like fit the probabilities.


that's that's at


spent my youth practicing the tango de la muerte.


so the real case.


however you know it the purpose was not really at this stage to come up with meaningful probabilities.


but to get thinking about that hidden middle layer.


uhhuh.


and so


and


we would actually


i guess once we look at the data more we'll get more hidden nodes.


yeah.


but i'd like to see more.


not because it would expedite the probabilities because it wouldn't.


it would actually slow that down tremendously.


um well yeah.


i guess.


but


not that much though.


only a little early.


no.


i think we should have uh exponentially more middle nodes than features we've extracted.


okay.


i'm i'm just


so are doing business versus tourist


they refer to your current task.


like like current thing you want to do at this moment.


um yeah.


well that's that's an interesting point.


whether you're it's whether it's not


and are


i think it's more like are you are tourist.


are you in like heidelberg for a


oh.


so i thought that was directly given by the context switch.


that's a different thing.


what if the context which is not set but still they say things like i want to go uh see the uh the the castle and uh et cetera?


is it


well the i kind of thought of doing business as more of running an errand type thing.


yeah.


business on the other hand is uh definitely what you're doing.


so if you run out of cash as a tourist and and and you need to go to the a


so


okay.


oh i see.


you may have a task.


you have to go get money and so you are doing business at that stage.


huh.


right.


how do i get to the bank.


i see.


and that'll affect whether you want to enter or you if you


kind of thing.


so the tourists node should be um very consistent with the context node.


right?


if you say that's more their in general what their background is.


yeah.


i think this context node is a bit of a


i don't know.


like in


uh do we want to have


like it's


are you assuming that or not?


like is that to be i mean if that's accurate then that would determine tourist node.


if the context were to set one way or another that like strongly uh um says something about whether whether or not they're tourists.


uhhuh.


so what's interesting is when it's not when it's set to unknown.


what set the they set the context to unknown?


okay.


right now we haven't observed it.


so i guess it's sort of averaging over all those three possibilities.


right.


but yes.


you can set it to unknown.


and if we now do leave everything else as is the results should be the same.


oops!


right?


no.


well no.


because we the way we set the probabilities might not have


yeah.


it's it's an it's an issue.


like


pretty much the same?


it is.


so the issue is that um in belief nets it's not common to do what we did of like having you know a bunch of values and then unknown as an actual value.


what's common is you just like don't observe the variable.


and then just marginalizes.


yep.


but uh we didn't do this because we felt that there'd


i guess we were thinking in terms of a switch that actually.


we were


uhhuh.


but uh i don't know what the right thing is to do for that.


i'm not i don't know if i totally am happy with the way it is.


why don't we


can we


um how long would it take to to add another node on the observatory and um play around with it?


another node on what?


uh well it depends on how many things it's linked to.


let's just say make it really simple.


if we create something that for example would be um


so some things can be landmarks in your sense.


but they can never be entered.


so for example a statue.


good point.


yeah?


right.


so maybe we want to have landmark meaning now enterable landmark versus um something that's simply just a vista point for example.


yeah that's true.


uh a statue or um


so basically it's addressing a variable that's enterable or not.


so like an enterable question mark.


also you know didn't we have a size as one?


what?


the size of the landmark.


um not when we were doing this.


because if it's


yeah.


but i guess at some point we did.


for some reason i had that okay that was a thought that i had at one point.


but then went away.


so you want to have a a node for like whether or not it can be entered?


well for example if we include that.


yeah?


um accessibility or something.


huh.


is it can it be entered?


then of course this is sort of binary as well.


and then um there's also the question whether it may be entered.


in the sense that you know if it's tom the house of tom cruise you know it's enterable but you may not enter it.


you know?


you're not allowed to.


unless you are whatever his his divorce lawyer or something.


and um and these are very observable sort of from the from the ontology sort of things.


does it actually help to distinguish between those two cases though?


whether it's practically speaking enterable or actually physically enterable or not?


it seems like it would for uh uh determining whether they want to go into it or not.


if if you're running an errand you maybe more likely to be able to enter places that are usually not you're not usually not allowed to uh


because they


well i can see why


let's get this uh clearer.


so it's matrix between if it's not enterable.


whether it's a whether it's a public building and whether it's actually has a door.


period.


yeah.


exactly.


okay.


this is sort of uh


uhhuh.


so tom cruise's house is not a public building.


but it has a door.


right.


but the thing is


okay


explain to me why it's necessary to distinguish between whether something has a door and is not public.


or if something


it seems like it's equivalent to say that it doesn't have a door and it


or not public and not a door are equivalent things.


it seems like in practice.


so we would have


what does it mean then that we have to


we have an object type statue.


that really is an object type.


so there is there's going to be a bunch of statues.


and then we have for example an object type huh that's a hotel.


how about hotels?


okay.


so the most famous building in heidelberg is actually a hotel.


it's the hotel zum ritter.


which is the only renaissance building in heidelberg that was left after the big destruction and for the thirty years war blah blah blah.


huh.


does it have nice walls?


it has wonderful walls.


excellent.


um and lots of detail and carvings engravings and so forth.


so


but um it's still an unlikely candidate for the tango mode i must say.


but um so so if you are a


well it's very tricky.


so i guess your question is so far i have no really no real argument why to differentiate between statues as statues and houses of celebrities.


from that point of view.


let let's do a


can we add just so i can see how it's done uh a has door property?


or


okay.


what would it uh connect to?


like what would uh it affect?


um i think um it might affect


oh actually it's it it wouldn't affect any of our nodes.


right?


what i was thinking was if you had a like


oh it's it affects the doing business is certainly not.


you could affect theoretically you could affect doing business with has door.


yeah.


huh.


it should um inhibit that.


right.


let's see.


i don't know if javabayes is nice about that.


it might be that if you add a new thing pointing to a variable you just like it just overwrites everything.


but you can check.


well we have it saved.


so we can open it up again.


okay.


it's true.


the safety net.


i think you could just add it.


i mean i have before.


whew!


well that's fine.


but we have to see the function now.


has it become all point fives or not?


oh right.


so this is has door.


uh true false.


that's acceptable.


and i want to edit the function going to that.


right?


no.


this is fine.


oh no.


this business.


right.


it was fine.


added this one.


yep.


what would be nice if it is if it just like kept the old function for either value.


this


but


nope.


didn't do it.


oh.


oh wait.


it might be


did we


yes.


that's not good.


that's kind of annoying.


okay.


so just dismiss everything.


close it and and load up the old state so it doesn't screw screw that up.


let's see.


oops!


huh.


maybe you can read in.


so have you used javabayes a lot?


really i i've i haven't used it a lot.


and i haven't used it in the last you know many months.


so


um uh we can ask someone.


it might be worth uh asking around.


um


like we looked at sort of uh a page that had like a bunch of


yeah.


srini


okay.


srini's the one to ask i would say.


i guess he'd be the person.


um he might know.


because


and


i mean in a way this is a lot of good features in java.


it's has a gui and it's uh


uhhuh.


i guess those are the main two things.


it does learning.


it has


no it doesn't actually.


what?


i didn't think it did learning.


maybe it did a little bit of learning.


oh right.


i don't remember.


maybe you're right.


okay.


right.


but uh it's free.


which is quite positive.


yeah.


but uh


maybe another thing that uh


but i mean its interface is not the greatest.


so


uhhuh.


but actually it had an interface.


a lot of them were like you know


yep.


command line.


huh.


what is the code?


can can we see that?


how do you write the code?


the


or do you actually never have to write any code there?


there is actually a text file that you can edit.


but it's


you don't have to do that.


there's like an x m l format for bayes-nets.


is it x m l?


there is one.


i don't know if this uses it.


oh i see.


no this doesn't use it.


but it


i didn't think it did.


yeah the the


you can look at the text file.


yeah.


but do you have it here?


well maybe you don't.


uh yes i do actually.


let me see.


oh yes.


of course.


like there's the


oh man.


i didn't is there an ampersand in dos?


nope.


just start up a new d o s.


that's all right.


i can probably double click on it.


or


yeah right.


uh


let's see.


yep.


come on.


it'll ask you what you what it wants what you want to open it with and see what bat i guess.


one of these days it should open this.


theoretically.


go right mouse.


open with.


that's


oh!


oh there we go.


maybe it was just


oh.


uh it was dead to the world.


god!


okay.


through the old notepad.


that's my favorite editor.


i like i like word pad because it has the uh the returns.


wordpad?


i


the carriage returns on some of them.


uhhuh.


you know how they get auto fills i guess.


or whatever you call it.


anyway there it is.


so this is sort of lisp y?


no.


uh yeah.


it just basically looks like it just specifies a bunch of structs.


yeah.


that's how actual probability tables are specified.


as like lists of numbers.


so theoretically you could edit that.


it just that it's


but they're not very friendly.


yeah the ordering isn't very clear on


right.


so you'd have to like figure out like you have to go and


the layout of the table.


well i


actually we could write a program that could generate this.


i think so.


you could.


yeah you could.


it's not


we were doing it


yeah.


we can maybe write an interface for uh entering probability distributions easily.


something like like a little script.


that might be worth it.


and that might do.


i actually seem to recall srini complaining about something to do with entering probability.


so this is probably


the other thing is it is in java.


yeah it's


so


we could manipulate the source itself?


or


do you have the true source files?


i don't know if he actually


or just the class?


uh yeah we do.


does he


i i saw directory called source.


oh.


uhhuh.


i didn't


yeah.


go up one.


up one.


uh yes good.


source.


that's that's quite nice.


i don't know if it actually manipulate the source though.


that might be a bit complicated.


i think it might it might be simpler to just have a script that you know


it's like friendly.


the the data tables.


it allows you enter things well.


right.


but if if there is an x m l file that or format that it can also read


i mean it just reads this.


right?


when it starts.


uhhuh.


yeah i know there is an i was looking on the web page.


and he's updated it for an x m l version of i guess bayes-nets.


there's a bayes-net spec for in x m l.


he's like this guy has?


the javabayes guy?


yeah.


so but he doesn't use it.


so in what sense has he updated it?


well you can either you or you can read both.


oh i see.


to my understanding.


okay.


oh.


that would be awesome.


because uh


well at least the uh


i could have misread the web page.


i have a habit of doing that.


but


okay wonderful.


so you got more slides?


do i have more slides.


um yes.


one more.


future work.


i think every presentation have a should have a future work slide.


but uh it's basically


we already talked about all this stuff.


so


um the additional thing is i guess learning the probabilities also.


that's maybe


i don't know if


uh that's future future work.


does


that's


yeah.


right.


very future.


uhhuh.


and of course if you have a presentation that doesn't have something that doesn't work at all then you have what i learned as a slide.


can't you have both?


you could.


my first approach failed.


what i learned.


okay so i think that uh our presentation's finished.


good.


i know what i like about these meetings is one person will nod and then the next person will nod and then it just goes all the way around the room.


so the uh


i missed my turn.


no i earlier i went and bhaskara went and you did it.


you did it.


it's like yawning.


and this announcement was in stereo.


ha!


okay.


so this means um


should i pull up the net again?


yeah.


could you put the the um net up again?


yes.


thanks.


there we go.


and actually i was


because i got a wireless mike on.


so a more general thing than discussed admission fee um could be


i i'm just wondering whether the context the background context of the discourse might be


i don't know.


if there's a way to define it or maybe you know generalize it some way.


um there might be other cues that say um in the last few utterances there has been something that has strongly associated with say one of the particular modes.


uh i don't know if that might be


uhhuh.


uh and and into that node would be various various things that that could have specifically come up.


i think we


i think a a sort of general strategy here


you know this is this is excellent because um it gets you thinking along these terms


is that maybe we we could observe a couple of um discourse phenomena such as the admission fee.


and something else and something else that happened in the discourse before.


right.


and um let's make those four.


and maybe there are two um


so maybe this could be sort of a separate region of the net which has two has it's own middle layer.


maybe this you know has some kind of um funky thing that


if this


and this may influence these hidden nodes of the discourse.


which is maybe something that is uh a more general version of the actual phenomenon that you can observe.


so things that point towards


so instead of single node for like if they said the word admission fee


exactly.


yeah.


admission fee or maybe you know how much to enter.


or you know something.


opening hours or something like that.


other cues.


that would all funnel into one node that would constitute entrance requirements or something like that.


so pay a visit


uhhuh.


uh uh


sure.


yeah?


i mean it sort of get into plan recognition kinds of things in the discourse.


i mean that's like the bigger um version of it.


and then maybe there are some discourse acts if they happened before.


um it's more for um a cue that the person actually wants to get somewhere else.


and that you are in a in a in a route.


um sort of proceeding past these things.


so this would be just something that where you want to pass it.


huh.


is that it?


however these are of course then


the the nodes the observed nodes for your middle layer.


so this again points to final destination doing business tourist hurry and so forth.


okay.


and so then we can say okay.


we have a whole region in a


that's a whole set of discourse related cues to your middle layer.


yeah exactly.


right?


and this is just then just one.


so because at the end the more we um add you know the more spider-web-ish it's going to become in the middle and the more of hand editing.


it's going to get very ugly.


but with this way we could say okay these are the discourse phenomena.


they may have there own hidden layer that points to some of the the real hidden layer um or the general hidden layer.


sure.


and the same we will be able to do for syntactic information.


the verbs used the object types used modifiers.


and maybe there's a hidden layer for that.


yep.


and so forth and so forth.


then we have context.


yeah.


so essentially a lot of those nodes can be expanded into little bayes-nets of their own.


uhhuh.


precisely.


so


one thing that's kind of been bugging me when i more i look at this is that the


i guess the fact that the there's a complete separation between the observed features and in the output.


i mean it makes it cleaner but then uh i mean


that's true.


for instance if the discourse does


what do you mean by that?


well for instance the discourse admission fee node seems like it should point directly to the


or increase the probability of enter directly versus going there via tourist.


or we could like add more uh sort of middle nodes.


like we could add a node like do they want to enter it.


which is affected by admission fee and by whether it's closed and by whether it has a door.


so it's like there are those are the two options.


right.


either like make an arrow directly or put a new node.


huh.


yeah.


that makes sense.


and if it if you do it if you could connect it too hard you may get such phenomenon that like so how much has it cost to enter.


and the answer is two hundred fifty dollars.


and then the persons says um yeah i want to see it.


yeah?


meaning it's way out of my budget.


um


there are places in germany where it costs two hundred fifty dollars to enter?


um nothing comes to mind.


without thinking too hard.


um maybe


yeah of course.


um opera premiers.


really?


so you know


huh.


or or any good old pink floyd concert.


i see.


if you want to see the magic flute or something.


yeah.


or maybe um a famous restaurant.


or i don't know.


there are various things that you might not want to eat a meal there but your own table.


the spagos of heidelberg.


i think that the i mean nothing beats the the admission charge prices in japan.


so there two hundred dollars is is moderate for getting into a discotheque.


you know.


then again everything else is free then once you're in there.


food and drink and so forth.


so i mean


but you know we can


something somebody can have discussed the admission fee and the answer is if we


um you know um still based on that result is never going to enter that building.


huh.


because it's just too expensive.


oh yeah.


i think i see.


so the discourse refers to admission fee but it just turns out that they change their mind in the middle of the discourse.


yeah.


you have to have some notion of not just


i mean there's a there's change across several turns of discourse.


right.


so i don't know how if any of this was discussed but how if it all this is going to interact with whatever general uh other other discourse processing that might be happen.


uhhuh.


i mean


yeah yeah.


what sort of discourse processing is uh are the


how much is built into smartkom and


it works like this.


the uh um i mean


the first thing we get is that already the intention is sort of


they tried to figure out the intention.


right?


simply by parsing it.


and this um won't differentiate between all modes.


yeah?


but at least it'll tell us okay here we have something that


somebody that wants to go someplace.


now it's up for us to figure out what kind of going there is is is happening.


and um if the discourse takes a couple of turns before everything all the information is needed


what happens is you know the parser parses it.


and then it's handed on to the discourse history which is um one of the most elaborate elaborate modules.


it's it's actually the the whole memory of the entire system that knows what who said what which was what was presented.


it helps anaphora resolution and it and it fills in all the structures that are omitted.


so um


because you say okay how can i get to the castle.


oh how how much is it.


and um yeah i would like uh um to let's do it and so forth.


so even without an anaphora somebody has to make sure that information we had earlier on is still here.


uhhuh.


because not every module keeps a memory of everything that happened.


so whenever the uh um person is not actually rejecting what happened before.


so as in no i really don't want to see that movie.


i'd rather stay home and watch t v.


um what movie was selected in what cinema in what town is is going to be sort of added into the into the representations every at each dialogue step by the discourse model discourse model.


yeah that's what it's called.


and um it does some help in the anaphora resolution.


and it also helps in coordinating the gesture screen issues.


so a person pointing to something on the screen you know


huh.


the discourse model actually stores what was presented at what location on the on the screen.


so it's a it's a rather huge huge thing.


but um um we can sort of


it has a very clear interface.


we can query it whether admission fees were discussed in the last turn.


and and the turn before that or you know


how deep we want to search


okay.


um which is a question.


how deep do we want to sear?


you know?


um but we should try to keep in mind that you know we're doing this sort of for research.


so we we should find a limit that's reasonable and not go you know all the way back to adam and eve.


you know did that person ever discuss admissions fee fees in his entire life?


and the dialogues are pretty pretty you know concise.


and


anyway


so one thing that might be helpful which is implicit in the use of admission fee discussion as a cue for entry is thinking about the plans that various people might have.


like all the different sort of general schemas that they might be following.


this person is um finding out information about this thing in order to go in as a tourist or finding out how to get to this place in order to do business.


um because then anything that's a cue for one of the steps would be slight evidence for that overall plan.


um i don't know.


they're in in in sort of more traditional a i kinds of plan recognition things you sort of have you know some idea at each turn of agent doing something.


okay what plans is this a consistent with?


and then get some more information and then you see here's a sequence that this sort of roughly fits into.


it it might be useful here too.


i i don't know how


uhhuh.


you know you'd have to figure out what what knowledge representation would work for that.


i mean the


huh.


it's in the these these these plan schemas.


i mean there are some some of them are extremely elaborate.


you know.


what do you need need to buy a ticket?


and it it's fifty steps.


just for buying a ticket at a ticket counter.


you know and and maybe that's helpful to look at it to look at those.


it's amazing what human beings can do.


when we talked uh we had the example you know of you being uh a a person on a ticket counter working at railway station.


and somebody runs up to you with a suitcase in his hands says new york.


and you say track seven.


huh?


and it's because you know that that person actually is following you know you execute a whole plan of going through a hundred and fifty steps you know without any information other than new york.


inferring everything from the context.


so works


um even though there is probably no train from here to new york.


right?


huh.


not direct.


you'd uh probably have to transfer in chicago.


uhhuh.


but uh it's possible.


um no you probably have to transfer also somewhere else.


is that san francisco chicago?


i think


is that possible?


one time i saw a report on trains.


and i think there is a


i don't know if


i thought there was a line that went from somewhere.


maybe it was sacramento to chicago.


but there was like a california to chicago line of some sort.


i could be wrong though.


it was a while ago.


the transcontinental railroad.


doesn't that ring a bell?


i think it has to exist somewhere.


yeah.


but i don't know if it's still


they might have blown it up.


well it never went all the way.


right?


i mean you always had to change trains at omaha.


well most of the way.


uh uhhuh.


one track ended there and the other one started at five meters away from that.


and sort of


well you seem to know better than we do so.


yeah?


has anybody ever been on an amtrak?


i have.


but not transcontinentally.


i'm frightened by amtrak myself.


what?


why?


i just


they seem to have a lot of accidents on the amtrak.


really?


their reputation is very bad.


yeah yeah.


huh.


it's not maybe reality.


it's not like german trains.


like german trains are really great.


so


but you know i don't know whether it's which ones are safer you know statistically.


um but they're faster.


yeah.


much faster.


uhhuh.


and there's much more of them.


yeah they're yeah it's way better.


yeah i used um amtrak quite a bit on the east coast.


and i was surprised.


it was actually okay.


you know on boston new york.


new york rhode island.


i've done that kind of thing.


whatever.


boston.


but


that's a different issue.


this is going to be an interesting transcript.


huh.


i i want to see what it does with uh landmark-iness.


that's


yeah.


let's all say it a few more times.


just kidding.


so


it'd help it figure it out?


right.


so by the way that structure that robert drew on the board was like more um cue type based.


right?


here's like we're going to segment off a bit of stuff that comes from discourse.


and then some of the things we're talking about here are more


you know we mentioned maybe if they talk about um


i don't know.


entering or you know like they might be more task based.


so i i don't know if there there's obviously some more than one way of organizing the variables into something.


i think that um what you guys did is really nicely sketching out different tasks and maybe some of their conditions.


uhhuh.


one task is more likely you're in a hurry when you do that kind of doing business.


and and less in a hurry when uh you're a tourist.


um tourists may have never have final destinations you know because they are eternally traveling around.


so maybe what what what happened what might happen is that we do get this sort of task based middle layer.


and then we'll get these sub middle layers that are more cue based.


that feed into those.


nah?


might be might be a nice dichotomy of of the world.


so um i suggest to for to proceed with this in in the sense that maybe throughout this week the three of us will will talk some more about maybe segmenting off different regions.


and we make up some some toy observable nodes.


is that what


refined just refine the


okay.


what's the technical term?


for which?


for the uh nodes that are observable.


the outer layer.


just observable nodes.


evidence nodes.


the features.


i don't know.


whatever you


feature make up some features for those.


yeah.


identify four regions.


maybe make up some features for each region.


and uh and uh uh and uh


middle layer for those.


and then these should then connect somehow to the more plan based deep space.


basically just refine some of the more general nodes.


yep they they will be ad hoc for for for some time to come.


yeah this is totally like


the probabilities and all are completely ad hoc.


we need to look at all of them.


i mean but they're even like i mean like close to the end we were like uh you know we were like uh really ad hoc.


it's a even distribution.


like


whatever.


right?


because if it's like uh if it's four things coming in.


and say some of them have like three possibilities and all that.


so you're thinking like like a hundred and forty four or something possible things numbers to enter.


and


that's terrible.


so


some of them are completely absurd too.


that's uh well


like they want to enter but it's closed.


it's night time.


you know


there are tourists and all this weird stuff happens at the line up and you're like


yeah the only like possible interpretation is that they are like come here just to rob the museum or something to that effect.


confused.


in which case you're supposed to alert the authorities and see appropriate action.


yeah.


yeah another thing to do um is also to um i guess to ask around people about other bayes-net packages.


is srini going to be at the meeting tomorrow?


do you know?


maybe.


the day after tomorrow.


wait


quite possibly.


oh oh sorry.


wednesday.


day after tomorrow.


sorry.


maybe we can ask him about it.


who's talking on wednesday?


huh.


i haven't jerry never sent out a sent out an email.


did he ever?


no.


but he mentioned at the last meeting that someone was going to be talking.


i forget who.


uh


oh.


isn't ben?


ben?


i think it's ben actually.


ben then.


ben.


yeah.


uh!


um


giving his job talk i think.


sorry.


i was just reading the screen.


okay.


so the uh


that will be one one thing we could do.


i actually uh have um


also we can uh start looking at the smartkom tables.


and i will


right.


i actually wanted to show that to you guys now.


but um


do you want to trade?


um no.


i i actually made a mistake because it it fell asleep.


and when linux falls asleep on my machine it's it doesn't wake up ever.


so i had to reboot.


oh no!


and if i reboot without a network i will not be able to start smartkom because i need to have a network.


uh.


so we'll do that maybe uh


but


okay.


but once you start start smartkom you can be on you don't have to be on a network anymore.


is that the deal?


yep.


uh interesting.


why does smartkom need a network?


um it looks up some stuff that you know is is that is in the written by the operating system only if it if you get a d h c p request.


so it you know my computer does not know its i p address.


you know?


you know so


unless it boots up with networking.


it's plugged in.


yeah.


and i don't have an i p address they can't look up


they don't know who localhost is and so forth and so forth.


huh.


always fun.


but it's a um simple solution.


we can just um go downstairs and and and look at this.


but maybe not today.


the other thing um i will


oh yeah.


okay i have to report um data collection.


we interviewed fey.


uhhuh.


she's willing to do it.


meaning be the wizard for the data collection.


also maybe transcribe a little bit if she has to.


but also recruiting subjects organizing them and so forth.


so that looks good.


jerry however suggested that we should uh have a trial run with her.


see whether she can actually do all the uh spontaneous eloquent and creativeness that we uh expect of the wizard.


and i talked to liz about this.


and it looks as if friday afternoon will be the time when we have a first trial run for the data.


so who would be the subject of this trial run?


pardon me?


who will there be a is one is you one of you going to be the subject?


like are you


um liz also volunteered to be the first subject which i think might be even better than us guys.


good.


one of us.


yeah.


if we do need her for the technical stuff then of course one of you has to sort of uh jump in.


i like how we've you guys have successfully narrowed it down.


is one of you going to be the subject.


is one of you


jump in.


reference.


i haven't done it yet.


well i just figured it has to be someone who's um familiar enough with the data to cause problems for the wizard so we can uh see if they're you know good.


oh plants someone who can plant difficult things.


i mean that's what we want to check.


right?


um


well in this case it's a it's a sort of testing of the wizard rather than of the subject.


isn't that what it is?


it's uh


yes we we would like to test the wizard.


but you know if we take a subject that is completely unfamiliar with the task or any of the set up we get a more realistic


i guess that would be reasonable.


yeah.


you know set up as


i know.


that's probably a good enough test of


uhhuh.


sort of having an actively antagonistic uh


that might be a little unfair.


i'm sure if we uh


you think there's a chance we might need liz for whatever the technical side of things.


i'm sure we can get other people around who don't know anything um if we want another subject.


yeah yeah.


you know like i can drag ben into it or something.


although he might cause problems.


but


so is it a experimental setup for the um data collection totally ready determined?


i like that.


test the wizard.


i want that on a t shirt.


um i think it's it's it's i mean


experimental setup on the technical issue.


yes.


except we i think we still need uh a recording device for the wizard.


just a tape recorder that's running in a room.


uhhuh.


but um in terms of specifying the scenario um uh uh we've gotten a little further.


but um we wanted to wait until we know who is the wizard.


and have the wizard partake in the ultimate sort of definition probe.


so so if if on friday it turns out that she really likes it and and we really like her then nothing should stop us from sitting down next week and getting all the details completely figured out.


and um


okay.


so the ideal task um will have whatever


i don't know how much the structure of the evolving bayes-net will affect


like we want to we want to be able to collect as much of the variables that are needed for that.


huh yeah some


right?


in the course of the task?


well not all of them.


but you know


i'm even this this tango enter vista is sort of itself an ad hoc scenario.


the the basic um idea behind the uh data collection was the following.


the data we get from munich is very command line.


simple linguistic stuff.


uhhuh.


hardly anything complicated.


no metaphors whatsoever.


not a rich language.


so we wanted just to collect data to get that that that elicits more uh that elicits richer language.


and we actually did not want to constrain it too much.


you know?


just see what people say.


and then maybe we'll discover the phenomenon the phenomena that we want to solve you know with whatever engine we we come up with.


um so this this this is a parallel track.


okay.


you know there they hopefully meet.


so in other words this data collection is more general.


but since


it could it could be used for not just this task.


it should tell us you know what kind of phenomenon could occur.


it should tell us also maybe something about the difference between people who think they speak to a computer versus people who think they speak to a human being.


uhhuh.


and the sort of differences there.


so it may get us some more information on the human machine pragmatics um that no one knows anything about as of yesterday.


and uh nothing has changed since then.


so


uh and secondly now that of course we have sort of started to lick blood with this.


and especially since um johno can't stop tango-ing we may actually include you know those those intentions.


so now i think we should maybe have at least one navigational task with with sort of explicit uh


not it's implicit that the person wants to enter.


and maybe some task where it's more or less explicit.


that the person wants to take a picture.


or see it or something.


so that we can label it.


i mean that's how we get a corpus that we can label.


exactly.


whereas you know if we'd just get data we'd never know what they actually wanted.


we'd get no cues.


yep.


alrighty.


okay.


that was that.


so is this the official end of the meeting now?


looks like it.


so what's economics the fallacy?


i just randomly label things.


so that has nothing to do with economics or anything.


oh really?


maybe we ought to switch off these things before we continue.


switching


good.


yeah.


okay so let's get started.


nancy said she's coming.


and that means she will be.


um my suggestion is that robert and johno sort of give us a report on last week's adventures uh to start.


so everybody knows there were these guys uh from uh uh actually from uh d f k i uh part of the german smartkom project who were here for the week.


and i think got a lot done.


yeah i think so too.


um


the we got to the point where we can now speak into the smartkom system and it'll go all the way through and then say something like roman numeral one am smarticus.


it actually says roemisch einz am smarticus.


which means it's just using a german synthesis module for english sentences.


okay.


so uh


it doesn't know i.


um the uh


oh.


am spartacus.


i am i am smarticus is what it's saying.


verstehe.


right.


i


the uh synthesis is just a question of um hopefully it's just a question of exchanging a couple of files once we have them.


and um it's not going to be a problem because we decided to stick to the so called concept to speech approach.


so i'm i'm i'm going backwards now.


so synthesis is where you sort of make this uh make these sounds.


and concept to speech is feeding into this synthesis module giving it what needs to be said and the whole syntactic structure.


so it can pronounce things better presumably then just with text to speech.


uhhuh.


and uh johno learned how to write x m l tags.


uh and did write the tree adjoining grammar for some some sentences.


no?


right?


yeah for a couple


yeah.


so uh the way the uh the dialogue manager works is it dumps out what it wants to know or what it wants to tell the person to a uh in x m l.


and there's a conversion system for different uh to go from x m l to something else.


and so the knowledge base for the system that generates the syntactic structures for the generation is uh in a lisp like the knowledge base is in a lisp like form.


and then the thing that actually builds these syntactic structures is something based on prolog.


so you have a basically a goal.


and it you know says okay well i'm going to try to do the greet the person goal.


so it just starts uh it binds some variables and it just decides to you know do some subscold.


basically it just means build the tree.


and then it passes the tree onto uh the the generation module.


okay.


but i think that the point is that out of the twelve possible utterances that the german system can do we've already written the the syntax trees for three or four.


we


so the syntax trees are very simple.


it's like most of the sentences in one tree.


uhhuh.


and instead of you know breaking down to like small units and building back up they basically took the sentences and basically cut them in half or you know into thirds or something like that and made trees out of those.


and so uh uh tilman wrote a little tool that you could take lisp notation and generate an x m l uh tree uh what do structure from the from the lisp.


and so basically you just say you know noun goes to you know


nah i don't i've never been good at those.


so there's like the v p goes to n and those things in lisp and it will generate for you.


n v.


yeah.


all right.


and because we're sticking to that structure the synthesis module doesn't need to be changed so all that fancy stuff.


and the texas speech version of it which is actually the simpler version is going to be done in october which is much too late for us.


so


this way we we worked around that.


the uh the system um i can show you the system.


i actually want at least maybe you should be able to start it on your own if you want to play around with it in in the future.


right now it's brittle and you need to start it up and then make twenty changes on on on on seventeen modules before they actually can stomach it anything.


and send in a a a couple of side queries on some dummy center set up program so that it actually works.


because it's designed for this seevit thing where you have the gestural recognition running with this siemens virtual touch screen which we don't have here.


uhhuh.


and so we're doing it via mouse.


but the whole system was designed to work with this thing.


and it was it was a lot of engineering stuff.


no science in there whatsoever.


but it's working now.


and um that's the good news.


so everything else actually did prove to be language independent except for the parsing and the generation.


why i had i did need to generate different trees than the german ones.


mainly because you know like uh the gerund in in german is automatically taken care of with just a regular verb.


you have to switch it on.


so i'd uh have to add am walking.


or i'd have to add a little stem for the am when i build the built the tree.


okay.


yeah i noticed that um that some of the examples they had had you know non english word orders and so on.


you know and then all that good stuff.


so


yeah.


all right.


like


so it might be worth keith you looking at this.


um


well tilman


i i still don't i still don't really understand like


i mean we sort of say um


you know i i still don't exactly understand sort of the information flow uh in in this thing or what the modules are and so on.


so you know like just that such and such module uh um decides that it wants to achieve the goal of greeting the user.


and then magically it sort of


yeah


i mean how does it know which syntactic structure to pull out and all that?


i


so i think it's not worth going over in the group.


uh


but sort of when you get free and you have the time uh either robert or johno or i can walk you through it.


sure.


yeah.


soon.


okay.


and you can ask all the questions about how this all fits together.


that's fine.


it's eee messy.


but once you understand it you understand it.


it's it's there's nothing really complicated about it.


no.


and i remember one thing that that came up in the talk last wednesday um was this


i i think he talked about the idea of like um he was talking about these lexicalized uh uh tree adjoining grammars where you sort of for each word you um


okay you know how to do it?


for each lexical item the lexical entry says what all the uh trees are that it can appear in.


and of course that's not that's the opposite of constructional.


that's you know that's that's h p s g or whatever.


right.


you know?


now we're we're not committed for our research to do any of those things.


yeah.


uhhuh.


so uh we are committed for our funding.


okay?


to uh


make our stuff fit to that.


yeah to


no to just get the get the demos they need.


so between us all we have to get the demos they need.


if it turns out we can also give them lots more than that by you know tapping into other things we do that's great.


you should probably move the microphone closer to your face.


but it turns out not to be in any of the contracts.


there's like a little the twisty thing you can move it with.


okay.


and deliberately.


so the reason i'd like you to understand uh what's going on in this demo system is not because it's important to the research.


it's just for closure.


so that if we come up with a question of could we fit this deeper stuff in there or something.


you know what the hell we're talking about fitting in.


right.


so it's just uh in the same actually with the rest of us.


we just need to really understand what's there.


is there anything we can make use of?


uh is there anything we can give back beyond the sort of minimum requirements?


but none of that has a short time fuse.


so the the demo requirements for this fall are sort of taken care of as of later this week or something.


and then so it's probably fifteen months or something until there's another serious demo requirement.


that doesn't mean we don't think about it for fifteen months.


oh okay.


but it means we can not think about it for six months.


right yeah.


so the plan for this summer uh really is to step back from the applied project.


keep the keep the context open but actually go after the basic issues.


huh.


and


so the idea is there's this uh other subgroup that's worrying about formalizing the getting a notation.


but sort of in parallel with that uh the hope is in particularly you will work on constructions in english and german for this domain.


uhhuh.


but not worry about parsing them or fitting them into smartkom or any of the other anything any other constraints for the time being.


yeah.


okay.


got it.


it's hard enough to get it semantically and syntactically right and then and get the constructions in their form and stuff.


and i i don't want you feeling that you have to somehow meet all these other constraints.


right okay.


um and similarly with the parsing uh we're going to worry about parsing uh the general case.


you know construction parser for general constructions.


and if we need a cut down version for something or whatever we'll worry about that later.


so i'd like to for the summer turn into science mode.


and i assume that's also uh your plan as well.


right.


so i mean the the point is that like the meetings um so far that i've been at have been sort of been geared towards this demo.


but but we're


and then that's going to go away pretty soon.


and then we'll sort of shift gears fairly substantially.


it's


yeah.


it's got


huh?


what i what i think is is a good idea that i can can show to anyone who's interested.


we can even make a sort of an internal demo.


and i i show you what i do.


uhhuh.


i speak into it and you hear it talk.


okay.


and i can sort of walk through the information.


so this is like in half hour or forty five minutes.


just fun.


and so you when somebody on the streets comes up to you and asks you what is smartkom so you can sort of give a sensible answer.


right.


so we could set that up as actually an institute wide thing?


just give a talk in the big room and and so people know what's going on.


when you're ready.


absolutely.


yeah i mean that's the kind of thing


that's the level at which you know we can just invite everybody and say this is a project that we've been working on and here's a demo version of it and stuff like that.


yeah.


well we we do want to have all the bugs out where you have to sort of pipe in extra x m l messages from left and right before you're


indeed.


makes sense.


but so that


it's clear then i think.


actually roughly starting uh let's say next meeting because this meeting we have one other thing to tie up besides the trip report.


okay.


but uh starting next meeting i think we want to flip into this mode where


uh i mean there are a lot of issues.


what's the ontology look like.


uhhuh.


you know what do the constructions look like.


what's the execution engine look like.


huh lots of things.


but more focused on uh an idealized version than just getting the demo out.


now before we do that let's get back in


oh!


but it's still i think useful for you to understand the demo version enough so that you can can see what what it is that that uh it might eventually get retro fitted into or something.


yeah.


right.


and johno's already done that uh looked at the uh the looked at the smartkom stuff.


uh


to some


uh what what part of the smartkom stuff?


well the parser and that stuff.


oh yeah yeah.


okay anyway.


so the trip


the report on these


the last we we sort of interrupted you guys telling us about what happened last week.


it's all right.


um well it was just amazing to to see uh how how instable the whole thing is.


maybe you're done then.


and if you just take the


and i i got the feeling that we are the only ones right now who have a running system.


i don't know what the guys in kaiserslautern have running.


because


the version that is the full version that's on the server does not work.


and you need to do a lot of stuff to make it work.


and so it's


and even tilman and ralf sort of said yeah there never was a really working version that uh did it without all the shortcuts that they built in for the uh october version.


so we're actually maybe ahead of the system gruppe by now.


the system the integration group.


and it was uh it was fun to some extent.


but the uh the outcome that is sort of of scientific interest is that i think both ralf and tilman


um i know that they enjoyed it here.


and they they they liked uh a lot of the stuff they saw here.


what what we have been thinking about.


and they're more than willing to to um cooperate by all means.


and um part of my responsibility is uh to use our internal group ware server at e m l.


make that open to all of us and them.


so that whatever we discuss in terms of parsing and and generating and constructions we we sort of uh put it in there.


and they put what they do in there.


and maybe we can even um get some overlap get some synergy out of that.


and um


the uh


if i find someone at in e m l that is interested in that um i i may even think that we could look take constructions and and generate from them.


because the tree adjoining grammars that that tilman is using is as you said nothing but a mathematical formalism.


and you can just do anything with it whether it's syntactic trees h p s g like stuff or whether it's construction.


so if you ever get to the generation side of constructing things and there might be something of interest there.


but in the moment we're of course definitely focused on the understanding um pipeline.


any other uh visit reports sort of stories?


uh we so we now know i think what the landscape is like.


uhhuh.


and so we just push on and and uh do what we need to do.


and one of the things we need to do is the um and this i think is relatively tight tightly constrained is to finish up this belief net stuff.


so


uh and i was going to switch to start talking about that unless there're other more general questions.


okay so here's where we are on the belief net stuff as far as i understand it.


um going back i guess two weeks ago uh robert had laid out this belief net missing only the connections.


right?


that is so he'd put all all the dots down.


and we went through this and i think more or less convinced ourselves that at least the vast majority of the nodes that we needed for the demo level we were thinking of were in there.


yeah we may run across one or two more.


but of course the connections weren't.


so uh bhaskara and i went off and looked at some technical questions about were certain operations sort of legitimate belief net computations and was there some known problem with them or had someone already uh solved you know how to do this and stuff.


and so bhaskara tracked that down.


the answer seems to be uh no no one has done it but yes it's a perfectly reasonable thing to do if that's what you set out to do.


and so the current state of things is that again starting now um we'd like to actually get a running belief net for this particular subdomain done in the next few weeks.


so bhaskara is switching projects as of the first of june.


and uh he's going to leave us an inheritance which is a uh hopefully a belief net that does these things.


and there're two aspects to it one of which is you know technical.


getting the coding right and making it run and uh stuff like that.


and the other is the actual semantics.


okay what all you know what are the considerations and how and what are the ways in which they relate?


so he he doesn't need help from this group on the technical aspects or if he does uh we'll do that separately.


uhhuh.


but in terms of what are the decisions and stuff like that that's something that we all have to work out.


is is that right?


i mean that's that's both you guys' understanding of where we are?


absolutely.


okay.


so i guess um is there like a latest version of the belief net of the proposed belief net?


like


we had um decided


um well no we didn't decide.


we wanted to look into maybe getting it the visualization a bit clearer.


but i think if we do it um sort of a paper version of all the nodes and then the connections between them that should suffice.


yeah.


that should be fine.


yeah i


yeah i mean that's a separate problem.


we do in the long run want to do better visualization and all that stuff.


that's separable.


i did look into that uh in terms of you know exploding the nodes out and down


yep.


right.


javabayes does not support that.


i can imagine a way of hacking at the code to do that.


it'd probably take two weeks or so to actually go through and do it.


not not at this point.


and i went through all the other packages on kevin murphy's page.


and i couldn't find the necessary mix of free and uh with the gui and with this thing that we want.


well we can if it's if we can pay


yeah if you know it's paying a thousand dollars or something we can do that.


okay?


okay.


so so don't view free as as a absolute constraint.


okay so then i'll go back and look at the ones on the list that


yeah.


but


and you can ask kevin.


yeah the one that uh people seem to use is uh hugin or whatever.


huh.


hugin.


yeah that's free.


how


i don't think it's


is it free?


because i've seen it advertised in places so i it seems to


uh it may be free to academics.


like i i don't know.


i have a i have a copy that i i downloaded.


okay.


so at one point it was free.


uh but i noticed people do use hugin.


so um


how do you spell that?


why?


h u g i n.


and bhaskara can give you a pointer.


so then in any case um


but paying a


you know if if it's uh


probably for university it's it's going to be real cheap anyway.


but um you know if it's fifty thousand dollars we aren't going to do it.


i'm mean we have no need for that.


i i also would suggest not to spend two weeks in in in changing the the javabayes code.


no.


he's not going to do that.


yeah.


okay.


i i will send you a pointer to a java applet that does that.


it's sort of a fish eye.


you you have a node and you click on it and it shows you all the connections.


and then if you click on something else that moves away that goes into the middle.


huh.


and maybe there is an easy way of interfacing those two.


if that doesn't work it's not a problem we we need to solve right now.


what i'm what my job is i will um give you the input in terms of of the internal structure.


maybe node by node or something like this.


or should i collect it all?


uhhuh.


and


doesn't matter.


um just any like like sort of rough representation of the entire belief net is probably best.


and um you're going to be around again always tuesdays and thursdays afternoon-ish as usual?


yeah.


or will that change?


i mean yeah.


i can like i


um this week i guess um kind of i have a lot of projects and stuff but after that i will generally be more free.


so yes i might i can be around.


and i mean generally if you email me also i can be around on other days.


okay.


yeah and this is not a crisis that i mean you do everybody who's a student should you know do their work get their courses all in good shape.


and and and and then we'll dig dig down on this.


yeah that's


no that's good.


that means i have i i can spend this week doing it.


so


how do you go about this process of deciding what these connections are?


i know that there's an issue of how to weight the different things too and stuff.


right.


right?


i mean do you just sort of guess and see if it sort of


well there there there there're two different things you do.


it's


one is you design and the other is you learn.


okay?


so uh what we're going to do initially is is do design and if you will guess.


okay.


uh that is you know use your best knowledge of of the domain to uh hypothesize what the dependencies are and stuff.


if it's done right and if you have data then there are techniques for learning the numbers given the structure.


yeah.


and there are even techniques for learning the structure although that takes a lot more data.


and it's not as and so forth and so on.


so uh


but for the limited amount of stuff we have for this particular exercise i think we'll just design it.


all right.


hopefully as time passes we'll get more and more data from heidelberg and from people actually using it and stuff.


so but this is the long run.


but to solve our problems uh a mediocre design will do i think in the beginning.


that's right.


yeah oh and by the way speaking of data um are there


i could swore uh i could swear i saw it sitting on someone's desk at some point.


but is there a um a transcript of any of the sort of initial interactions of people with the with the system?


uhhuh.


because you know i'm still sort of itching to to look at what look at the stuff and see what people are saying.


yeah make yourself a note.


so and and of course keith would like the german as well as the english.


so whatever you guys can get.


the german?


oh yeah of course german.


yeah.


yeah the your native language.


right?


you remember that one.


okay.


that's important yeah.


so he'll get you some data.


yeah


yeah i mean i i sort of um found the uh uh the audio of some of those.


huh.


and um it kind of sounded like i didn't want to trudge through that you know.


it was just strange.


but


yep.


we probably will not get those to describe because they were trial runs.


oh yeah.


um but uh that's but we have data in english and german already.


so


yeah i mean


transcribed.


i will send you that.


okay.


so while we're still at this sort of top level anything else that we ought to talk about today?


how was your thingy?


oh um i just wanted to uh like mention as an issue um you know last meeting i wasn't here.


because i went to a linguistics colloquium on the fictive motion stuff.


oh right.


and that was pretty interesting.


and you know i mean


seems to me that that will fairly obviously be of relevance to uh to what we're doing here.


because you know people are likely to give descriptions.


like you know what's that thing uh right where you start to go up the hill or something like that.


you know meaning a few feet up the hill or whatever from some reference point and all that stuff.


so i mean i'm sure in terms of you know people trying to state locations or you know all that kind of stuff this is going to be very relevant.


so


um now that was the talk was about english versus japanese um which obviously the japanese doesn't affect us directly except that um some of the construction


he'd what he talked about was that you know in english we say things like you know your bike is parked across the street.


and we use these prepositional phrases you know well if you were to move across the street you would be at the bike.


but um in in japanese the the more conventionalized tendency is to use a sort of a description of where one has crossed to the river there is a tree.


um and you know you can actually say things like um there's a tree where one has crossed the river but no one has ever crossed the river or something like that.


so the idea is that this really is you know that's supposed show that's it's really fictive and so on.


but um


but the point is that that kind of construction is also used in english you know like right where you start to go up the hill or just when you get off the train or something like that to uh to indicate where something is.


huh.


so we'll have to think about


how much is that used in german?


um the uh well i i was on a uh on a on a different sidetrack.


i mean the the deep map project which um is undergoing some renovation at at the moment.


oh okay.


but this is a a three language project german english japanese.


okay.


and um we have a uh uh i have taken care that we have the the japanese generation and stuff.


and so i looked into uh spatial description.


so we can generate spatial descriptions how to get from a to b and and information on objects in german english and japanese.


uhhuh.


and there is a huge uh project on spatial descriptions uh differences in spatial descriptions.


well if if you're interested in that.


so how how


i mean it does sort of go all the way down to the conceptual level to some extent.


so


um


so where is this huge project?


it's kleist.


it's the uh bielefeld generation of uh spatial descriptions and whatever.


well that may be another thing that keith wants to look at.


but um i i think we should leave japanese constructions maybe outside of the scope for for now.


yeah.


but um definitely it's interesting to look at at cross the bordered there.


are are you going to pay any attention to the relative position of of the direction relative relative to the speaker?


for example there are some differences between hebrew and english.


we can say um park in front of the car as you come you drive behind the car.


in hebrew it means park behind the car because to follow the car is defined as it faces you.


uhhuh.


intrinsic.


while in english front of the car is the absolute front of the car.


okay.


right so the canonical direction of motion determines where the front is.


so


right.


so is german uh closer to to uh uh uh uh to i mean uh


i don't think it it's related to syntax though so it may be entirely different.


um as a matter of fact


no it's not.


yeah.


um did you ever get to look at the the paper that i sent you on the on that problem in english and german?


i think


carroll ninety three.


um i there is a a study on the differences between english and german on exactly that problem.


huh.


so it's they actually say the monkey in front of the car.


where's the monkey?


uhhuh.


and um they found statistically very significant differences in english and german.


so i i i


it might be since there are only a finite number of ways of doing it that that german might be more like hebrew in that respect.


the solution they proposed was that it was due to syntactic factors.


that but it wasn't was


that syntactic factors do do play a role there whether you're more likely you know to develop uh choices that lead you towards using uh intrinsic versus extrinsic reference frames.


right.


i mean it seems to me that you can get both in in english depending


you know like in front of the car could you know like


here's the car sideways to me in between me and the car or something's in front of the car or whatever.


i could see that.


absolutely.


but


but anyway so you know i mean this was this was a a very good talk on those kinds of issues and so on.


so uh


i can also give you uh a pointer to a paper of mine which is the the ultimate taxonomy of reference frames.


all right.


so


cool.


i'm the only person in the world who actually knows how it works.


oh.


not really.


great.


no i've not seen that.


it's called a


what do you mean um reference frames?


uh uh


it's it's spatial reference frames.


you actually have only


um if you want to have a


this is usually um


i should there should be an l though.


well actually you have only have two choices.


you can either do a two point or a three point.


which is you you're familiar with with the origo?


where that's the center origo is the center of the frame of reference.


and then you have the reference object and the object to be localized.


huh.


okay?


uhhuh.


in some cases the origo is the same as the reference object.


this was like


so that would be origin in english.


the origin.


right?


yeah.


right.


origo is a terminus technikus in that sense.


that's even used in the english literature.


origo.


oh okay.


all right.


i never heard it.


okay.


and um so


this video tape is in front of me.


i'm the origo and i'm also the reference object.


uhhuh.


those are two point.


and three point relations is if something has an intrinsic front side like this chair then your shoe is behind the chair.


and reference object and um no from from my point of view your shoe is left of the chair.


you you can actually say things like um it's behind the tree from me or something like that i think in in in certain circumstances in english.


right?


yeah.


as sort of from where i'm standing it would appear that


so


looks a little bit like reichenbach for time.


yeah it sounds like it.


doesn't it?


it's a lot like it.


and then and then here you


um


on this scale you have it either be ego or allocentric.


and that's that's basically it.


so egocentric two point egocentric three point or you can have allocentric.


so as seen from the church the town hall is right of that um fire station.


oh okay.


uhhuh it's hardly ever used but it's


i'd love to see it if you if you have a copy kind of.


i see this is this is getting into ami's thing.


yeah.


uh


uhhuh.


he's he's very interested in that.


here


okay.


so


uh yeah.


me too.


well why don't you just put it on the web page?


there's this e d u


right?


it's or or just


it's also all on my my home page at e m l.


or a link to it.


it's called an anatomy of a spatial description.


just


but i'll send that link.


okay great.


maybe just put a link on.


yep.


yeah.


by the way there something that i didn't know until about a week ago or so is apparently there are separate brain areas for things within reach and things that are out of reach.


so there's there's uh all this linguistic stuff about you know near and far or yon and and so forth.


huh.


uhhuh.


so this is all this is there's this linguistic facts.


but apparently the uh here's the way the findings go.


that you know they do m r i and and if you're uh got something within reach then there's one of your areas lights up.


and if something's out of reach uh a different one.


but here's the the amazing result um they say.


you get someone with a with a deficit so that they have a perfectly normal ability at distance things.


so the typical task is subdivision.


so there's a a line on the wall over there and you give them a laser pointer and you say where's the midpoint.


and they do fine.


if you give them the line and they have to touch it.


they can't.


there's just that part of the brain isn't functioning so they can't do that.


here's the real experiment.


the same thing on the wall you give them a laser where is it.


they do it.


give them a stick long stick and say do it.


they can't do it.


so there's a remapping of distant space into nearby space.


right.


so they doubled


because it's within reach now?


the the end the end of this


it's not within reach and you use the within reach uh mechanism.


yeah.


oh wow!


circuits.


so i'll i'll dig you up this reference.


that's cool.


and so this this is uh


first of all it explains something that i've always wondered about.


and i'll do this this test on you guys as well.


so


uh i have had an experience not often but a certain number of times when for example i'm working with a tool a screwdriver or something for a long time i start feeling the tip directly.


not indirectly.


but you actually can feel the tip.


and people who are uh accomplished violinists and stuff like that claim they also have this kind of thing where you get a direct sensation of physical sensation of the end affector.


what's going on at the end of the tool.


the the the the extension.


huh?


what's going on at the end of the tool or whatever.


yeah.


within


right.


the extension of of your hand right.


have you had this?


the


i i think so.


i mean it's not exactly the same thing but but it it it's getting close to that.


what does it feel like?


oh it feels like your as if your uh neurons had extended themselves out to this tool and you're feeling forces on it and so forth.


and and you deal directly with it.


i once i i was playing you know with those um uh devices that allow you to manipulate objects when it's dangerous to get close.


right yeah yeah yeah.


oh okay.


so you can insert your hand something.


and there's a correspondence between


so i played with it.


after a while you don't feel the difference anymore.


yeah right.


i i mean it's kind of


uhhuh.


very kind of you stop back and suddenly it goes away.


and you have to kind of work again to recapture it but yeah.


yeah.


right.


yeah so anyway so so this was the first actual experimental evidence i'd seen that was consistent with this anecdotal stuff.


that's cool.


and of course it makes a lovely uh story about why languages uh make this distinction.


of course there are behavioral differences too.


things you can reach are really quite different than things you can't.


but there seems to be an really deep embodied neural difference.


and this is um


so in addition to the


this is more proximal distal.


yeah uh exactly.


so in addition to ego and allocentric uh which appear all over the place you also apparently have this proximal distal thing which is very deeply uh embedded.


well dan montello sort of


he he does the uh uh the cognitive map world down in santa barbara.


and he he always talks about these.


he he already well


probably most likely without knowing this this evidence uh is talking about these small scale spaces that you can manipulate versus large scale environmental spaces.


yeah.


well there's there's uh been a lot of behavioral things on this but that was the first neuro physiological thing i saw.


anyway yeah so we'll we'll look at this.


and so all of these issues now are now starting to come up.


so now we're now done with demos.


we're starting to do science.


right?


and so these issues about uh reference and spatial reference discourse reference uhuh uhuh all this sort of stuff uh deixis which is part of what you were talking about.


uhhuh.


um so all of this stuff is coming up essentially starting now.


so we got to do all this.


so there's that.


and then there's also a set of system things that come up.


so okay we're not using their system that means we need our system.


it it follows.


and so uh in addition to the business about just getting the linguistics right and the formalism and stuff we're actually going to build something.


and uh johno is point person on the parser analyzer whatever that is.


and we're going to start on that in parallel with the um the grammar stuff.


but to do that we're going to need to make some decisions like ontology.


all right.


so um and so this is another thing where we're going to you know have to get involved and make relatively early i think make some decisions on uh is there an ontology a p i that that


there's a sort of standard way of getting things from ontologies.


and we build the parser and stuff around that.


or is there a particular ontology that we're going to standardize on and if so for example is there something that we can use there?


does uh either the uh smartkom project or one of the projects at e m l have something that we can just pull out for that?


uh so there are going to be some some some things like that which are not science but system.


but we aren't going to ignore those.


because we're we're not only going the plan is not only to lay out this thing but to actually uh build some of it.


and how much we build and and so forth.


i


uh part of it if it works right is


it looks like we're now in a position that the construction analyzer that we want for this applied project can be the same as the construction analyzer that nancy needs for the child language modeling.


so it's always been out of phase.


but it now seems that um there's a good shot at that.


so we've talked about it.


and the hope is that we can make these things the same thing.


okay.


and of course it's only in both cases it's only one piece of a bigger system.


uhhuh.


but it would be nice if that piece were exactly the same piece.


it was just this uh construction analyzer.


right.


and so we think we think we have a shot at at that.


so the


so


to to come full circle on that this formalization task


okay?


is trying to get the formalism into into a shape where it can actually uh


yeah.


be of use to someone who's trying to do this.


right?


well yeah.


where it actually is is covers the whole range of things.


and the the the the thing that got mark into the worst trouble is he had a very ambitious thing he was trying to do.


and he insisted on trying to do it with a limited set of mechanisms.


it turned out inherently not to cover the space.


okay.


and it just it was just terribly frustrating for him.


and he seemed fully committed to both sides of this irreconcilable thing.


i see.


right.


and


uh johno is much more pragmatic.


huh?


good to know.


is this is true.


is it not?


yes.


so there's you know sort of yeah deep really deep emotional commitment to a certain theory being uh complete.


oh okay.


you don't have a hidden purist streak?


oh no.


well it hasn't it it certainly hasn't been observed in any case.


just checking.


no sir.


all right.


um now you do but that's okay.


uh so for for


because i don't have to implement anything.


exactly right.


i have a problem then.


exactly.


it's so whether i do depends on whether i'm talking to him or him probably.


huh.


yeah right.


right.


which meeting i'm in.


why actually uh the thing is you you do but the thing you have to implement is so small that uh


it's okay to be purist within that context.


within that.


yes.


yeah.


good.


and uh it's and still i think you know get something done.


cool!


but to try to do something upscale and purist particularly if if um what you're purist about doesn't actually work is real hard.


yay.


uhhuh.


okay.


and then the other thing is while we're doing this uh robert's going to pick a piece of this space.


it's possible yeah.


okay?


uh for his absentee thesis.


i think you all know that that you can just in germany almost just send in your thesis.


just a drive up.


ca-chuk.


yeah right.


um


there you go.


there there's a drive in thesis uh joint over in saarbruecken.


exactly.


drive through yeah.


it costs a lot.


the the amount you put in your credit card and as well.


but uh


but anyway.


so uh that's um also got to be worked out hopefully over the next few weeks.


so that that it becomes clear uh what piece uh robert wants to jump into.


and while we're at this level uh there's at least one new doctoral student in computer science who will be joining the project either next week or the first of august depending on the blandishments of microsoft.


okay.


so uh and her name is eva.


it really is.


nobody believed that.


yeah i thought it had to be a joke of your part you know.


like johno made it up.


yeah.


i'm sure.


is this person someone who's in first year this year?


no first year coming.


or


so she's she's now out here.


she's moved.


and she'll be a student as of then.


and probably she'll pick up from you on the belief net stuff.


so she'll be chasing you down and stuff like that.


document.


uh


right?


uh against all traditions.


and actually i talked today to a uh undergraduate who wants to do an honors thesis on this.


someone from the class?


no.


interestingly enough.


we always get these people who are not in the class who


some of some of them yeah.


it's interesting.


so anyway.


uh but uh she's another one of these ones with a three point nine average and so forth and so on.


uhhuh.


uh so um i've i've given her some things to read.


so we'll see how this goes.


oh there's yet another one of the incoming first incoming first year graduate students who's expressed interest.


so we'll see how that goes.


um


anyway


so i think as far as this group goes um it's certainly worth continuing for the next few weeks to get closure on the uh belief net and the ideas that are involved in that and what are what are the concepts.


we'll see whether it's going to make sense to have this be separate from the other bigger effort with the formalization stuff or not.


i'm not sure.


it partly depends on what your thesis turns out to be and how that goes.


so we'll see.


and then ami you can decide you know how much time you want to put into it.


and uh it's beginning to take shape.


okay.


so uh and


right


i think you will find that if you want to look technically at some of the your traditional questions in this light uh keith who's building constructions will be quite happy to uh see what you know you envision as the issues and the problems and um how they might uh get reflected in constructions.


sure.


i suspect that's right.


yeah.


i i may have to go to switzerland for in june or beginning of july for between two weeks and four weeks but uh after that or before that.


fine.


and um if it's useful we can probably arrange for you to drop by and visit either at heidelberg or at the german a i center while you're in in the neighborhood.


right.


yeah uh


actually i'm invited to do some consulting with a bank in geneva which has an affiliation with a research institute in geneva which i forgot the name of.


yep.


do


well we're connected to uh


there's a there's a a very significant connection between


we'll we'll go through this.


i c s i and e p f l which is the uh it's the


germany's got two big technical institutes.


there's one in in zurich.


uhhuh.


e t h.


and then there's one the french speaking one in lausanne.


okay?


oh so in switzerland.


which is uh e p f l.


so find out who they are associated with in geneva.


great.


probably we're connected to them.


right.


i'll let you know.


i'll send you email.


okay.


yeah.


and so anyway we uh we can undoubtedly get ami uh to give a talk at uh e m l or something like that while he's in in uh


huh.


uh i i think the one you you gave here a couple of weeks ago would be of interest there too.


sure yeah.


a lot of interest actually either place d f k i or uh


yeah so and and if there is a book that you'll be building up some audience for it.


and you'll get feedback from these guys.


great yeah.


because they've actually these d f k i guys have done as much as anyone over the last decade in trying to build them.


so we'll set that up.


cool.


so uh unless we want to start digging into the uh the belief net and the decisions now which would be fine it's probably


i i


it's probably better if i come next week with the um version o point nine of the structure.


so how about if you two guys between now and next week come up with something that is partially proposal and partially questions.


saying here's what we think we understand here are the things we think we don't understand.


and that we as a group will try to to finish it.


what i'd like to do is shoot for finishing all this next monday.


sure.


okay?


uh these are the decisions


i don't think we're going to get lots more information.


it's a design problem.


uhhuh.


yeah.


you know we


and let's come up with a first cut at what this should look like and then finish it up.


okay.


does that make sense?


and um the the semester will be over next week but then you have projects for one more week to come?


no i i think i'll be done everything by this uh by the end of this week.


same with you?


no.


nnn


this well i've i have projects but then the my professor of one of my classes also has a final that he's giving us.


and he's giving us five days to do it which means it going to be hard.


oh is it a take home final?


who's doing this?


aikin alex yeah.


yeah figured.


that would have been my guess.


huh.


right.


um but anyway yeah.


pretty soon.


okay.


okay so i guess that's


so the seventeenth will definitely be the last day like it or not for me.


so let's do this.


and then well there's going to be some separate


these guys are talking.


uh we have a group on the formalization.


uh nancy and johno and i are going to talk about parsers.


so there're various kinds of uh


of course nothing gets done even in a meeting of seven people.


right?


so um two or three people is the size in which actual work gets done.


yeah.


so we'll do that.


great.


oh the other thing we want to do is catch up with uh ellen and see what she's doing.


because the um image schemas are going to be um an important


quite relevant yeah.


we we want those.


and we want them formalized and stuff like that.


oh yeah.


so let me let me make a note to do that.


okay.


yeah i'm actually probably going to be in contact with her uh pretty soon anyway because of various of us students were going to have a reading group about precisely that sort of thing over the summer.


oh right.