It was quick. Excellent. Yes, I'm very happy to be here.
So Lydia, yesterday at the dinner, asked me what I thought when I got the invitation
to speak here. I'm hearing myself.
Anyways. And the answer is I'm quite happy to do these things and to speak to
people kind of outside of the research community about these large language
models because I feel a bit of a responsibility.
They are the product of exactly my research field, and there's a lot of justified confusion out there.
So it is good to get into a conversation with people who might be like you,
who might be shaping how these things are actually going to be used, if at all.
So I have 10 minutes. So this is going to be very high level, obviously.
And what I thought I'd do is, in the first part, I talk a bit about the history, about how we got here.
And so in the field, we had a little bit of a heads up.
We've been seeing this coming for a couple of years, but not a lot.
So a lot of it was surprising for us as well.
And in the second part, I will talk a bit about our current understanding of
LLMs and maybe a positive path forward from it.
Okay, so here are the past three and a half decades.
The history of the field is longer, but this happens to be the time that I was
involved in this field, first as a student and then as a researcher in various capacities.
And what's interesting about this is that I have witnessed already two paradigm
shifts, two very different ways of doing things, which brought us here.
So in the 1990s, This was very much about symbolic methods and knowledge representation.
So what we would do is to process language with computers, we would go to our
linguist friends, get grammars from them, and then write programs that process these grammars.
And these are programs that are very much recognizable to you as well because
they are fully thought through these programs, hopefully at least, ideally at least.
I did around that time in the
late 1990s I did train my first neural network I took
a class on neural networks the University of Bonn where
I studied computer science at the time so
and I also trained my first language model in the late 1990s I think at an exchange
semester in Edinburgh so these are not new techniques right these are fairly
old techniques but they had a very They were very niche at the time.
They had a very specialized use.
I would love to be able to tell you that I stuck with these methods,
like the fathers of deep learning, and reaped the benefit 20 years later.
But I did not. I worked with mainstream methods of the field,
which were symbolic methods at first.
And during the 2000s, we started to use statistical methods or machine learning
methods. methods that basically learn parts of the.
Of the module, of the processing module. But the point is that there was still
symbolic knowledge representations.
The way that you got these representations, it helped you to process language,
to understand language.
It was machine learned, but the representations themselves were designed by humans to be clever.
I myself got an induction in the latest paradigm in 2014 when I did a sabbatical
at Microsoft Research in Seattle.
And it was crazy. There was really a buzz in the air and people were super stoked
by the first neural methods that started to work.
And at that time, that was word embeddings. So there's a particular way of representing
the semantics, the meaning of words.
But what was new about it was that the representations weren't designed.
The representations were machine-learned as well.
The machine basically came up with the best representation that was best for the task.
For a very simple task, but it turned out to be very general.
And that kind of set the mood, set the scene for where we are now.
So the first ingredient that changed many things was representation learning.
The representations are built by the machine itself. And then with that comes end-to-end systems.
So instead of building modular computer systems, what you do is you really train
for the task and let the machine do all the intermediate steps itself.
And then in 2018, the BERT paper came out.
Just to get an idea of how many people know something about this.
Does anyone know this paper?
Yeah, a good few people know this paper. Okay, so this was the first paper that
really showed that transformers would work.
So this was the transformers paper was before that. But this was the first paper
that applied it and got fantastic results.
I was at a conference. This paper came out in 2018, late 2018.
I was at a conference a couple of weeks later, and people were kind of stunned.
People were giving their presentations as they planned.
We were showing their results, and then they said, okay, when we wrote this
paper, these were the state-of-the-art results.
We tried this new bird model, and we got that this bird model is 10% better.
It blows our model out of the water. So that was really, that was a moment there.
And by that time, then the race was on to develop, to do something with this.
For various reasons, as we all know, OpenAI was the team that produced the first,
model or product maybe that caught people's attention outside of the field.
Many other people tried as well. It's a little bit of a puzzle why Google didn't get there.
But anyway, Anyway, so that's where we are now and as on the left-hand side,
I subsumed this as generalist models.
So the really new thing is now that we have models that aren't even trained
for one particular task.
Well, they are trained for one particular task, namely next-word prediction,
but it turns out that this is super powerful that you can turn them into generalist models.
And this really is a new situation, so there has been a paradigm shift and there
has been kind of, okay, what do we do now?
Where do we go now with this field?
And finally, how can we build applications with this? How can we make this useful for people?
Okay, so this is my part one. I'm not really sure what the moral is of this,
because, unfortunately, these methods are a little bit stupid.
It is a little bit insulting, actually, that these very simple methods,
when scaled up enormously, work that well.
But that's where we are, and now we need to do something with this situation.
Okay, so in my research group, we work on language use a lot.
We think about the pragmatics of language, about the subtle cues that change
the meaning and contextualize the meaning.
And we have a lot of projects now trying to understand to what degree LLMs can model this.
I want to give a shout out to one project we're doing.
We have a benchmarking tool for LLMs that tests pragmatic abilities by letting
them play dialogue, conversational games kind of against themselves.
But that's not what I want to talk about in the remaining whatever many minutes.
We are also doing thinking about the role of LLMs that they can and cannot play
in technology. And these are two papers that I want to very briefly touch on
on the remaining slides.
Okay, so here are three observations that we maybe can discuss later and do something with.
The first one is that LLMs have decoupled fluency from expertise.
And I guess you guys all know this.
But it is surprising how many people sort of in the general population still
have not fully understood this, right?
Because it used to be the case that if someone produces flawless text on a subject,
It used to be the case that there are also experts in the subject matter,
but this is very much decoupled for technical reasons.
The training objective of these models rewards them for producing the right
words and the right phrases, but it has no access to the underlying causes for
why you would want to produce particular words.
So it gets the tone very right of almost anything you ask it to do.
But there are no guarantees on whether it will get the content right.
And that is a bit of a problem, perhaps.
Okay, second point. Computers cannot assert, even if the accuracy were a lot
higher and hallucinations were very aware, well, they are still fundamentally
different from human speakers because they don't care.
There's no one there who could care about what is said, right?
And my conclusion, at least from this, is that humans need to retain semantic
control over the output.
Basically, you need to know, you need to be able to understand whatever you're producing with LLMs.
And that restricts the use cases quite a bit, I think. But I think that's non-negotiable
with these technologies.
And the third point is that understanding LLMs as language-controllable function
approximators yields an understanding with which we can do something.
So the idea here is that instead of you writing the code for a function,
you search for this function in the LLM, the latent space of the LLM,
and then you treat the LLM indeed as having implemented this function.
But it's only approximating this function, right?
And that interesting challenges come from this.
Okay, I'm already over time. I'm gonna skip over a lot, but I want to end a
little bit on what I think is a positive way forward for using AI technologies.
And maybe we can discuss this in the panel.
Okay, so in a positive future, In the future, what has happened is that users
and buyers of this technology have developed a good mental model of this technology.
And have avoided all the anthropomorphization that is out there.
So these things are not interns, they're not coworkers, they're not friends.
They're programs, right?
They're programs that have to work and can be evaluated as such.
And if they don't work, you don't use them or you fix them, right?
And the onus here is, I guess, on us as educators, really driving home this
message to people that this is what these things are.
De-skilling has been avoided, right? So there is a danger when you sort of naively
build these techniques into products that you are de-skilling people because
they rely on these products too much and forget how to do the tasks themselves.
And to avoid this, some of this is on you.
I think you can, when you implement these systems, you can make engineering
design decisions that avoid this.
Some of this is on us, it's on the research side.
We need to do more research on interpretability and some is on the regulation side.
Okay, another point, responsibility of loading has been avoided.
So the tools are designed in such a way that people actually accept the fact
that they have to have semantic control.
Again, there's a design component to this. Maybe you have to design friction into the tools, right?
So before sending off the email that you had the LLM formulate for you,
get some proof from the user that they've actually read what has been produced.
Something like this. This might be a naive idea, but this is for you to think about.
Okay, what else do I have? Cost externalization has been reverted.
These things need to be as expensive as they are.
We know this from many areas. This holds for traffic as well.
This holds for manufacturing.
This holds for these things as well. They have to have the real costs,
which might drive some use cases out of being economically viable.
But then so be it, right?
And the last point maybe is something that interests you as well.
Well, some form of cultural sustainability has to be achieved.
So maybe we can come up with models where, so one part might be licensing training
data and paying the creators of the data that these models were trained on in that way.
But there might be room for creative models, like where each instance where
something is generated that can very clearly be traced back to source material,
the creator of the source material is paid in some way.
This is unlike, obviously, a real world. I mean, normally, if a teacher teaches
you something, it's not the textbook author who gets money, the author of the
textbook that the teacher learned this from.
But these are new things, so new models should be tried out.
Okay, with this, I think we can go over to the panel discussion. Okay, thank you.
Thank you so much.
I am going to leave it on this. Yes, coming.
We are going to have a bit of a discussion here.
And then also have time for audience questions.
And my first question for both of you is a friend of mine a while ago said.
AI, thank you AI is always that which we don't quite understand right now and
what was AI 10 years ago we today maybe no longer consider AI,
and I would love to hear from you, what do you consider to be AI today day and
what do you maybe not consider to be AI?
Anyone?
Why don't you stop?
Question uh yeah so um
i i got the question list in advance so
i had time to think about this a little bit um i i know a variant of this um
and it's called the uh ai paradox uh i guess and that is kind of more about
uh goalpost shifting right so so there the complaint is This is that people,
as soon as it starts working, people say this is not real intelligence.
And I think I have little problems with this because I don't think human value
derives from intelligence only.
So I don't feel threatened by programs doing tasks that appear intelligent.
So in that sense, it doesn't quite work for me.
And in a more technical sense, AI is a programming technique for me.
So what has been programmed in this particular way stays that way.
On the contrary, I'm actually still blown away. I'm old enough to remember how
terrible ASR was and I'm still blown away by how good it works these days.
So in that sense, there's still magic for me. Yeah, that's the reply, I guess.
All right, maybe I introduce myself first, because I don't think we've done
that yet, and maybe there are some who are online who don't know.
I'm Eike, I've been a KDE developer for about 20 years on many of our applications
and also on the Plasma user interface, so I think intelligent is a very common.
But when I go look for guidance, what it means to me, I end up thinking about
what our mission is at KDE.
And our mission is chiefly to develop end-user software.
So what does intelligent mean to a user? What makes a user want to describe
a computer system that they interact with as intelligent?
And has that recently changed? And I think it has because particularly large language models,
I think, have given computer systems in a broad way that new ability to do what
you mean rather than what you say,
which is not what users are used to from a computer outside of maybe some sort
of pattern recognition,
habit learning and making suggestions like that.
But now you can talk to your home and say, I want my room to look like the Barbie
movie and it turns the lights pink.
And that's something that it just couldn't do before. And it does this in a
very generalist fashion.
So statistical inference, it doesn't necessarily model you.
So it doesn't understand what your needs are individually.
And that's where some of the sort of problems lurk.
But I think it has raised the bar for what users expect computers to be able to do.
And I think that is something that we definitely have to deal with.
But also we have to, I think, communicate very clearly when we cannot actually
match these expectations and then where to draw the line.
That's a very good point. And I love your Barbie example. example which brings
me to my next question where do you use AI in all the shapes and forms in your daily life right now.
Do you turn your room, Bobby Payne? I do on occasion.
And I think, I mean, for me, and maybe I'm weird, but I love using these models to amuse myself.
So I often generate a funny picture just for my own purposes or to share with
someone or a funny humorous text because it does it faster than I can do it.
I can give it somewhat clear requirements. And, I mean, you can play with the humor of it a lot.
ChatGPT has such a peculiar writing style that is immediately recognizable to
anyone at this point. And you can play around with that.
I also, and this is interesting, right? So I do use it as an accelerator when I do development work.
And it turns out to be most useful when I use it for an application that I'm already an expert in.
Because then I can give it very specific requirements, but I can also check
whether it actually did what I wanted it to do. Like I can tell if this code is bad or not.
And the usefulness sort of tends to break down as soon as I venture into a space
where I cannot verify whether what I got is actually useful or not.
So I think that's an interesting observation too.
Yeah i i guess ai in your question means llms right because i guess we all use ai,
in very many instances like when
we turn on the dishwasher right um and
i mean this this kind of goes back to your first question um they
are still um there's still machine learning uh modules
in there but for llms um yeah that's interesting i um i mean as i said this
just comes out of my field so i feel the responsibility to try this out a little
bit but i don't use it much for many and maybe.
Maybe this is connected to the de-skilling. I've been in this job for a while
now, and many of the things that I'm doing, I've been doing for a very long time.
So I think I'm still a lot better at generating chunks of text than an LLM is,
and it would slow me down if I had to correct the text that comes out of it.
I have found one good use case for me, which is, so I like coding,
but I get to do it a lot less often than you guys, I guess.
So with a space of months in between, so when after a couple of months I get
to look at code again in Emacs or whatever, I have forgotten the most embarrassing things.
I've forgotten how to talk to the syntax of a for loop in Python or something.
And it's quite good for this. So I ask models to solve my problem.
I look at the code. I remember what, code looks like, I remember the syntax,
and then I write it myself.
I completely disregard the solutions. But as this kind of prompt,
as memory aid, I guess it works quite well.
But I'm more worried about what it does to people who are new at tasks rather
than people who have been doing tasks for a very long time.
I think you're onto something there. I think it's useful as a sort of checks
and balances sometimes, and the references.
And you write the code yourself, you also have it generated,
you compare the two, you check whether you've possibly missed anything,
or whether the alternative approach might be different. So I think that's interesting.
I mean, the access to the broad range of data was trained on knowledge-based
queries. It's sometimes quite interesting.
I think I want to, if I can respond, on the de-skilling parts.
I used to be very worried about de-skilling.
And probably I still should be, but I had an experience as well that made me
a little bit less afraid of that.
So in KDE, we have a mentor program. We have a website that lists a number of
members of the community who you can contact on a one-on-one basis if you want
to ask questions or you want some tutoring on how to do KDE development.
And there was a young person who reached out to me and they wanted to be a KDE contributor.
Contributor they had had some limited programming training
but really they were starting out with c++ and this being 2022 i guess of course
they were using chat gpt already because you know it's kind of hard to ignore
if you are trying to climb a mountain like that so we made the agreement that.
Yes he could use chat gpt for this but we would share an account and i would
be able to read what he asks the AI so that I can steer him clear of going into
a completely wrong direction.
And it was pretty interesting to see him interact with the system.
And of course, it was very hard for him to keep up the discipline to try and do things by himself.
He would lean very heavily on code generation in the end, and it would become
sort of a cycle of, well, this doesn't work.
So I go back to the model immediately and I ask it to fix it up or make changes.
And it falls apart pretty quickly there.
You can ask it to generate something once, that sort of works,
but integrating a hundred interactions over time doesn't yield a useful program.
And he started recognizing at some point that he would run into a type of vault
that he just couldn't leap over anymore because he didn't end up understanding
the code file that he had produced in that patchwork fashion.
And he came by himself to the conclusion that whenever I do it manually and
I fight that three-hour battle to produce one line of code, at the end of that,
I actually understand what it does and why it is fashioned that way.
And after about half a year, he was like, this is not mentally healthy for me.
I will stop using this crutch. So I think.
I'm an optimistic person and I think also around this technology society will
come to a point where sort of the use it or lose it of it all is apparent.
It's just, you know, using stairs instead of an elevator occasionally or things like that.
And particularly with writing, I agree with you.
I mean, I even turn off the word completion on my phone keyboard,
which this is a sophisticated version of, right?
Because or spell checking because I've always
felt like my orthography will just suffer if I
don't learn how to spell myself and I think a lot of people come to the same
conclusion so I'm not that worried about it I think we'll learn how to keep
it under control of course I mean in in a company I guess you would want to
measure whether this is an efficient way yeah letting people run into walls,
and take half a year to do so,
there might be more efficient ways of training them up.
Yeah, but I mean, particularly if you're an employer, I think you should also
learn to recognize the value of investing in your people.
And I mean, obviously training is a thing. And the absence of using AI tools
may constitute training that gives you a longer lasting value over a person
if you end up shaping their intellect rather than turning them into a prompt engineer,
for example yeah i hope so at least and it brings us to some of the.
Scenarios that the world is talking about right anything from,
the worst the world's going to end to this is the magical thing that's going
to solve all of our problems tomorrow um and let's start maybe a bit more with
the positive side of that or the more optimistic side of that.
What gets you excited right now and hopeful about current AI development?
Okay, I go first. I mean, for me this is a fascinating, super exciting time.
Obviously, there are things possible in my field that just weren't possible
five years ago. And there's a huge open vista of things to explore.
So that obviously is exciting.
It's also on the sort of more applied side, it will also be super interesting
to see how people turn this into actual products.
And what I always say is these models, and again, this is difficult to understand
if you don't know these models more closely, they are kind of doomed to be generalists, right?
Because they are, after all, on some level of description, they are autocomplete on steroids, right?
And so they will complete anything and everything you give them.
You give them a prompt and they complete it in some way.
And if that's not what you want in your application, because you only want to
produce a summary or you only want to do something else, you have a hard task
of designing out this generality again.
But I think the way forward to actual products is to do exactly that.
And it's what I'm trying to get at with my metaphor of them being function approximators.
So I think also on On the applied side, there's a lot of super interesting work,
especially in the user interface space.
I don't think the free chatbot with a weird, quirky personality type interface
is the final form of this technology.
This is just the first thing that people came up with because it's just so natural.
You can train this behavior in very easily.
But to be really useful...
In sort of standard, normal, accepted ways where you measure the usefulness
other than, wow, this is a funny-sounding limerick or I'm impressed by how cute
this sounds or whatever.
These are not good metrics for measuring actual utility in real context.
So I think there's a lot to be done there, hopefully by some of you here.
So I think what excites me about it comes down a little bit to how I sort of
conceive of the role of what an engineer does in society or what we do broadly do at KDE.
So when I was a young boy, I used to go to the Berlin Natural History Museum a lot.
I requested to go there every birthday that I had.
And the Natural History Museum has a wonderful expo on sort of stone age civilization
and then how people used to live,
many thousands of years ago and you get to see the tools that that society used, you know.
Passion stones to make fire and so on and it occurred to me even as a kid that
somebody had to think of making that stone and then produce it and give it to
others and then they might use it to make fire and then they sit around that fire and
i don't know start speaking and boom civilization
and i think that is sort of um what i aspire to do in its modern incarnation
to build tools that allow other people to live their life a little bit better
and sort of spread civilization and culture and i think ai and large language models
are such a heated topic because they promise to help us to make better tools,
for example, allowing people to accomplish sophisticated tasks without specialized
training, because they maybe bridge that natural language understanding gap,
the kind of do what I mean instead of what I say part.
But at the same time, they seem to, to some, to sort of encroach on that humanity.
So rather than help, they supplant.
And I think that's where a lot of the emotion comes from.
What I liked about your presentation and your approach at all is to sort of
try to turn that heat level of emotion down a bit and pare it down to what does
it actually do, what is the mechanism here, what are the limitations of it.
And I think for developers, it's very useful to think of it as sort of a pure
function that accepts fuzzy parameters and you can't always trust the return value.
I think that that's quite useful. but what that allows you to do is very powerful.
I mean, what you can now accomplish as a developer, let's say building a speech
system on a weekend as opposed to taking five years at Amazon with 300 people
is pretty stunning. So that's exciting.
Now we talked a bit about the positive and hopeful side, but there's also a
lot that gives people pause. What is that for you?
Right. Yeah, I mean, as I said, these are powerful tools, or at least they appear to be powerful tools.
They are very confident themselves that they are powerful tools.
And it gives people the temptation to build powerful applications and to shoot
themselves very powerfully in the foot with them.
And there's also a lot of money in the system. like a lot of money i i wanted
to say that i didn't get to say this but a lot of these,
these breakthroughs i have to admit this they're
coming out of commercial labs they're coming out of industry labs not academic
labs just because of the the insane capital expenditure that is needed to to
build these things so there's a lot of venture capital in in there and at some
point and some people want to see returns on it.
So the temptation to build applications that are given more power than they
should have, given the lack of guarantees, that is real.
And that is the part where doom lies.
It does not lie in chat GPT becoming conscious and trying to kill us all because
we ask such inane questions all the time.
It's people building applications that have a degree of autonomy that is not
warranted and we know that complex systems are difficult and this can go off
the rails quickly so that would be my worry.
Yeah so i do think that around this technology we
have um obviously a big problem with what you could call media literacy right
as you pointed out users need to have a good model of how these systems actually
work what their limitations are where your responsibilities as a user lie also.
Perhaps whether it's actually good for you to use it or not and a lot of the
commercial vendors in in this space are not incentivized, to be honest,
about many of these things.
Only to the extent that they are worried about liability will make them do it.
I think for us, because we have a lot of freedom from those constraints,
I mean, surely we also worry maybe about liability, but we get to worry about many more things.
I think KDE broadly is in, our mission is to figure out what it means to make
socially responsible software.
And licensing for us is an important aspect of what socially responsible means.
We have very strong ideas about that.
And for example, we should also delve into what we think that means in the AI
model space. place. But socially responsible also means sustainable,
for example, or protecting privacy.
And it can also mean giving users a realistic picture of how much AI is good
for them and transparency over whether it's used or enabled or not and things like that.
And I think that is what I expect of ourselves, that we should pay attention
to this to avoid the doom perhaps.
That brings us right back to KDE's role and maybe one last question before we
go to audience questions.
So are there areas in KDE where you see positive potential for using generative AI,
in applications in the development in our community?
So, yes, I mean, I have a laundry list of little like wish list items where
I think even the current level of technology would enhance our applications.
Maybe something that you would benefit from as an academic who has to read a lot of long PDFs.
I would love our document viewer to be able to answer the question which page
of that long PDF particular topic is being talked about rather than having to
do a keyword search, which is very clumsy.
So little things like that. But I think, again, it's not just the features.
It's also the fact that we have an opportunity to do it differently from the other players.
I mean, we have a goal, for example, to protect the user's privacy that makes
us much more interested, I think,
sort of in running these technologies locally rather than on a server.
And the other players are not doing it that much. So I think there we can offer
something substantially different to the user as well that I think a lot of
users want and I'm hoping that we get to do that.
Maybe one shot. So I think you have an opportunity since you don't have commercial
pressures so much at all, you have an opportunity to also lead in what you're not doing.
So I think a very lazy way of integrating AI or LLMs is to have a button that calls ChatGPT.
And that obviously requires no thought at all and does not address any of those
problems that I tried to highlight.
So I think you have a way of doing things more clever than this.
And as I said, my guess would be that trying to cut down on the generality and
really investigating a use case and making sure that this one use case is actually implemented well,
that's an opportunity, I think.
I agree with you. I think one of
the interesting things about KDE's software and its technology stack is that
we've always been very driven to create sort of libraries and frameworks that
are used all across our applications and that make them able to interact with
each other to some degree.
And you can query a lot of information about these applications as they're running.
And I think there's a lot of opportunity there to find sort of these more surgical
places where can you conceive of a semi-intelligent function that you could
sort of lock in between it, it would do something useful.
That is not just bring up text box and stuff like that, but that selects usefully
among things it's exposed to, for example. and things like that.
I hope that, and this is a really interesting developer problem,
what would be a good API for developers to run AI jobs like that?
And how can they do things together? That's very interesting.
All right, thank you so much. Then let's take some audience questions.
Who has questions? All right, let's start with you.
Oh, is this working? Okay, so when it comes to large language models,
this does not happen to all fields of the AI, but it does happen here.
One of the most serious problems there is that it needs a lot of data.
It needs a lot of computing power.
And we, as free software developers, we really don't have the resources to purchase them.
So, and also, we probably won't want to use a service that is proprietary and hosted by others.
And, you know, although they might have some privacy policy,
we might still not like it. We don't want to give our data to just some random third party.
What do you think can solve this problem?
Yeah, this is a real problem. These models only are as good as they are because
they have ingested a lot of data.
And it's not a linear curve in the capabilities, right?
You just need a lot of data. It's not that if you use half as much data,
it's half as good, there is a certain amount of data you absolutely need to
get the basic capabilities off the ground.
And that puts the training from scratch of these models out of the hands for
the foreseeable future, out of the hands of...
Of organizations without a lot of access access to a lot of capital um yeah
there's a problem there are some um some organizations for example allen ai
has just released the new version of the olmo model they are quite open about
the data they're using and they've released all the
checkpoints and and and so on um but there is of course llama by by meta which has a,
slightly weird license but it might be possible for for you to to use it but
then you have to live with the fact that this has been paid for by horrible
uh deeds uh done by meta so yeah this This is a problem.
I don't think training will ever become a possibility for normal people or academics,
for not otherwise funded entities.
If that rules it out completely for you, then that is a stance as well that needs to be discussed.
But yeah, this is a deep problem at the root of it. So I think the provenance
of the training data is for our community.
Topic that we think about a lot obviously because um
again licensing is a very important
topic for us in free software you could say that creative use
of the copyright system is uh one of the cornerstones of how we were going and
therefore we feel that and from our hearts i think that that if you don't display
integrity about protecting the rights of authors,
then you're doing it from a free software person, since we rely on that mechanism so much ourselves.
At the same time, obviously, a lot of us feel that we very intentionally release
our code as open source because we want others to have access to it.
So to some extent, we also care about there being a public domain.
Our licensing doesn't correspond to the public domain, but we're adjacent to it.
So, given all of that, I think what a lot of people in the free software community are looking for is,
is there anywhere credible activity to produce a clean training data set with
known provenance of the data that you can sort of use guilt-free?
Presumably, as an academic, that's also something that you would have massive interest in.
And I'm not, are you aware of any sort of solid effort to curate a sufficiently
large training data set that produces a model that would not give us sort of doubt and worries?
Yeah, as I said, there's this LNAI initiative, there's Olmo initiative.
As far as I'm aware, they are documenting where the data is from and trying
to make sure that at least under...
Under generous interpretations of the OR.
Anyway, it's still contested what copyright law says about training.
But anyways, so they are at least transparent about the data.
But this is only one part, right? I mean, having the data is great,
and you have several terabytes of data, that's great.
But then training a model on it
that requires several million dollars in compute is the next step, right?
So that might be a barrier as well. But starting from data, people are trying to address this.
Yeah, but I think we're more fine with someone sort of donating the compute to us.
But the data is, I think, bad. That gives us more thought.
Maybe to answer your question or to do something with your question a little bit more.
So you mentioned that you need a certain minimum amount of data to produce a
model that is useful but as a sort of non-expert looking into the various releases
that come out there certainly seem to be.
A lot of people who are trying to build smaller models that maybe show much
poorer performance and sort of knowledge queries but still have better reasoning
performance than you would expect.
From scaling the data down and when it comes to integrating a model into our
software that can sort of act as a function and usefully select among things
that it's sort of zero shot given as a prompt,
maybe sort of a small enough model would actually be good enough for us because
we're not looking for that text
box chat interface or we don't aim to be a Wikitalia replacement, right?
So how true is that notion that you as a model designer get to pick and choose
a bit as in, yes, your training data set is smaller, but the model architecture
still makes it useful as a function approximator?
You have to be careful there. There are two different dimensions to it that
are somewhat orthogonal or somewhat independent of each other.
One is the size of the model in terms of parameters.
And you can get smaller models that are quite okay, that have capabilities,
but they still need a lot of data.
And it turns out that over-training, the training with a lot more tokens than
people initially thought would be needed, even on smaller models, creates better models.
So these dimensions don't go... You still need a lot of data,
even on a model with fewer parameters.
I think we have two minutes, according to the sign.
David, thanks for doing this. I would like to draw attention back to this slide.
And I noticed that you talk about cultural sustainability, but you don't talk
about environmental sustainability.
Why is that? Is that sort of like, oh, yeah, we're going to have these...
That's meant to be in cost externalization. That's meant to be in the environmental sustainability.
Sustainability right okay either way you
know that all this is wishful thinking right nothing of
this is happening no there's any sign that this is going to happen doesn't this
give you a kind of a pause of well i mean i didn't give you my negative vision
for ai which is kind of which is kind of the inverse of this but um yeah but
we have to fight for it i mean we have what What can we do?
We can't just resign. We can identify areas where we would like to intervene.
Yes.
I'm here. That's my attempt at telling you to be aware of these issues.
I talk to decision makers. I talk to politicians who have dollar signs,
who have euro signs in their eyes when they talk about, think about AI,
and I tell them, okay, so here are areas where you can really do something.
Yeah, yeah, yeah, listen to their fingers, that is what's happening,
but you are kind of enabling them, right?
Yeah. I don't know, I...
I mean, exactly, that's the thing. So I think the thought...
Progress at any cost. No, no, no. What I mean is that I think,
for example, now we're at KDE, right?
I think the worst thing that we could do is ignore the topic because it is fraught with problems.
And through a combination of hard work over 30 years and historical happenstance,
we're one of a half dozen ways to use a computer, right?
There aren't that many ways to interact with a PC. We're one of them.
So I think that means we get to think about what stance we take and how to do
it according to our value system and hopefully show people a better example.
So I think you're very appropriate, right? We have a KDE Eco initiative.
What does that mean in the AI context? And what does it mean to using AI and
KDE software as a very good thing for us to think about?
And we will continue I see the sign you know I'm ignoring we will continue this
conversation in the hallway I'm sure we will thank you so much everyone and
thank you for inviting us.