What should we build AI for? An insider’s view
SUMMARY KEYWORDS
ai, big, data, good, companies, function, learn, technology, bit, happening, point, problem, claire, question, complicated, machine learning, create, decisions, neural network, machine

Claire Vernade  00:03
Okay, thanks. Well, I'm gonna share my screen. Hopefully that works. Great. And then I can just do this and cool. All right. That's it. I think it works for everybody. So Well, good evening, everyone, it's good to be here, good to see all the familiar faces. So I'm here to talk about AI. So perhaps a little bit less about, about, like, union stuff as I would be coming to speak last year, like a like, a couple of years ago. So maybe for those of you who don't know, me, I'm going to just reduce this because otherwise, it's gonna hide things, okay? So it means that I don't see you. So like, if you're, if you want to raise your hand or say something, please just like shout from like, unmute yourself and say something. So yeah, so for those of you who don't know me, so I, I was in London for for a while before coming here to Germany, where I'm speaking from, and I was working at DeepMind. Now, Google DeepMind, as a research scientist, so before that, I did a PhD in machine learning, and I worked for Amazon for a bit in Berlin. And then, you know, about last year, a year ago, roughly, I decided to, to go back to academia, and this is, now I have an assistant professorship in Germany, and I'm trying to, you know, learn research about AI and teach it to students. So for the talk today, actually, I decided that it would be interesting to start maybe with a little bit of a crash course on artificial intelligence, or maybe telling you a little bit more what is AI? Because we're gonna then talk about AI, you know, in what we, you know, what, what are the what we should be lit for, but maybe it's better to first like, be on the same grounds. So, let's get, right. Okay. So, AI is also called machine learning. So machine learning is perhaps the most common research term that we use in between between US researchers. So machine learning is an input inductive problem in the teeth problem means that we try to learn more general functions from a finite set, or possibly very large set of examples. So this last set of examples, we, we call them data sets. So that's, that's the data. And from these examples, we try to learn a function that is going to be that is going to try to explain or predict something for the for the future for examples that are not seen for data points that we've never, never seen in the data set. So for instance, I mean, you might be you might have done that, at some point in, in your life, this this idea of linear regression. So here I have the data are these little dots, which are, you know, an X and an associated y. And given x y little data points, what you want to do when you fit the linear regression is you want to find the straight line, that is the best the closest to all the points in your in your data such that when you give a new X, you can use the right or the line to predict a y. So that's already AI. It's a very simple one. But it's already what machine learning is about. Another very common problem in machine learning is to actually do classification. So it's an old data set that was actually collected by Fisher, I think. And it's like species of iris, so Iris, the flower. So here each dot is an Iris flower that has been collected and measured. And we represent the sepal width and the sepal length. So it's basically the size of this little part of the flower that is this is represented. And the purple dots are Setosa species, whereas the wild loop, the yellow dots are these vigilent virginica, and then the green ones are versicolor. And here, this general function is mathematical function that we're trying to learn is this kind of borders, we try to separate the space into regions so that so that we can classify this point. So if I give you a new point, I pick a new flower. I don't know what is this species, I can measure the sepal length and sepal width. And I can you know from my mathematical function from this from this classifier, I can decide that it's a virginica species. Maybe I'm making a mistake, maybe this border is not very accurate, but this is at least you know, what we've learned from the data. But so okay. One thing that is clear is that when I give you this data set here, for instance, this these few points, where you have to make a decision how you're going to what what are you going to try to learn you could very well decide to try To learn a function that looks like this, because obviously it fits the data very well. And it doesn't make any mistake on your data set. But it will predict a different value for this new X, it's going to predict a different value than if you had used a straight line. It's not going to be it's not going to be the same predictor. And it's like a more broader question like how should we learn from from data, like there's many ways we can learn from a similar data sets. And so we need to make decisions. And recently, you know, this decision has been these artificial neural networks. So this is where I want to get that before we get to neural networks. I want to show you how we do this, how we learn these straight lines, how we learn these functions. So is it all clear so far? It has no question. I continue, because I don't see you. So. Okay. So how do we do this? How do we do AI? How do we do linear regression, so we take a family of functions. So here this family of function is going to be it's going to be straight lines. And these straight lines are super easy to describe mathematically, because they are functions of the type a x plus b, where a quantify is the slope, the higher A, the steeper is the slope of the of the line, and B is simply like, the height of the slope, like how high it is, the higher the bead, the larger the B, the higher the right line. And so what we define from, you know, for for a certain function, a and b, so for certain choices A and B, we actually define the error that it makes as the sum of these distances between the data points, and the actual value that the line is predicting. So for all my data points, I make a certain error, and I can sum it, the sign here just means that we are summing we are summing the error for all the data points. And so we are obtaining a function here a new function that I called error. And this function quantifies how good is my predictor or how bad it is, actually, and to learn my predictor being the straight line. And to learn a good AI is a good representation of my data. I want to minimise the error that I'm making over, you know, over my data set, so I choose I try to choose a and b such that it minimises this this big L. So it's an optimization problem, as we say. And turns out that machines are pretty good at optimising things that minimising costs. If you give a machine costs and data like this, it's quite easy to make the machine find what is the minimum here. And so here, you know, I just did, like, I just did this, I had moved my my straight line. So it moved from here, you know, to here and like the sum of these costs, is smaller is the smallest that I can make with just choosing a and b. Okay, so that's already AI. And you see, it's not very complicated, this is actually fairly easy to do. And we've been doing that for many, many years now. But it turns out that when you make the function very complicated, things get harder. So this big drawing here is an artificial neural network. And it's basically the same thing as as this function, but very, very complicated to the point that I cannot actually show you what is the exact mathematical formula f of x equals blah, blah, blah, because this f of x is like a big complicated combination of all these parameters down here. So like, all the dots, and all the lines are a parameter of the function that is going to describe its shape, and how you know how it's like curved and, you know, kind of broken in some pieces and how it adapts to the data. But so it's a very, very complicated function. And each of these parameter, we need to decide what it is we need to optimise it to by minimising some sort of error. And now this type of function can actually deal with much more complicated data than my simple like x, y, y little points, or rather, now exci is going to be a very complicated object. So typically an image. So an image if you take each of the pixels one by one, and you make it like a long, long, long flat vector, or like flat little sequence of pixels, you can give it to the neural network as an input. And the neural network is just going to compute what is the output and compute the error that it makes. So if it actually you know, outputs, y is cat then it doesn't make an error. If it computes. Why is dog then it makes no and I'm going to compute this error and try to minimise the error over like over the entire data set. Okay, so that's roughly the that's roughly the page. This is AI, right? Like, there's, it's not much different from linear regression, except that we've made the functions so complicated that the whole process of making this computation, you know, efficient, and that and doable for the optimization doable for the machine has just become really difficult. And I think you can already grabs grasp this. So I think, amongst the things that we know is that for for to optimise neural network, to actually learn an artificial intelligence, you need to you need a lot of data you need because the function is so big and so complicated. To really be able to learn all these parameters, you need to feed it, a lot of data. But more than that, the choice of the architecture of the choice of how many hidden layers there is like how many of these dots we have, how many of these links we have, maybe not all the dots are connected together, maybe some only a subset of them, like all these choices of architecture, are actually require a lot of knowledge and a lot of experience, and a lot of technical engineering skills, and a lot of trials and errors. So it's not like you can just guess what is the right neural network, you need to make trials and make the computer compute it. And if it's not good enough, then you try again, you try something else. And every try, every time you want to try something, you need a lot of compute, to treat all this data and minimise the error and see if your choices were good. So like it's a long iterative process like this, that's that really, that is costly. So it costs money, and it costs energy. And so you know, that's, that's what AI has been for for a very long time. And I think what has made a big difference over the last few years, is that there has been a lot of open source software, a lot of development of AI that has allowed people to learn about it, not just in universities, but you know, we've seen a lot of people with engineering skills, just learning about AI just by by doing basically. And I think I will come back on this point, because I think it's perhaps a bit underestimated how important it is that that AI is open, and that people can learn about it. So how does it work at Amazon and Facebook and Google, because I've said, you know, it's very hard, it's very expensive, but yet all these tech companies, they basically, like, do a lot of it, maybe they don't fully live on it, but you know, they do a lot of it. So the way it's done usually is that the projects are separated into like, teams. And what usually, like a leader who might be like a more, you know, more senior person in the company, someone who has a bit of experience is going to define some projects, and I will say a few words about what are the projects afterwards. But like, they will define a project and then they basically have a quarter to come up with like a quick solution and implement it and test it on some data and then prove that it's going to be doing okay, test it on some live data and then deliver it. So like in in three months time, you have to like go from some existing system that was you know, identified as underperforming on some missing feature and deliver something. So sometimes it might be actually two quarters, but you know, the times the time is very short. And so you need to really iterate quickly, quickly between between the teams, and for that they rely on a lot of like, engineering processes, and you know, people like people doing splitting the work in small tasks that are that can be like spread the speed between people. So it's, it's very, very organised. And I would say like, to some extent efficient, I mean, I think we can also like discuss how, if that's efficient, but like, that's, that's the choice that has been made in a lot of companies. And that means also that like solutions cannot be like thought through too, too deeply, because the timescale is is very short. So, you need to go for for a relatively simple next next idea. So yeah, simple ideas. But, so, what are the projects? So, one thing that I found quite striking is that when I got to work with with this engineering team, so I, I did partly this, this type of work, and partly more researchy work at the Department, like part of my work was was to work with engineering and software development teams. And what's very clear is like the project are super siloed and are aimed at optimising a very specific metric. So, typically, you know, if you take the YouTube recommendation system or you know, the Netflix recommender system or whatever, What's happening on your web Netflix page, when you open your Netflix account, it's actually a lot of things. It's like a long series of software bits that are gonna take, you know, your history and, and your, your, you know, maybe or your connections or on and you name it and are going to output you know, your, the end of the set of recommendation of movies. But in the middle layer, let's look at lots of little parts. And often as a team, you will just be given this little part. So you will be given as input some things and blackbox something input that another team has worked on. And you will have to output something that isn't going to be given to, you know, the rest of the pipeline. And in the middle, you know, you you you have your own feature. And when you test you test on the entire pipeline pipeline, and you try to optimise for this metric. So yeah, metric could be like the time that the customer has spent watching or the number of flags that you have received, if you're trying to do some violent content detection. You can you can you can imagine this kind of metric that people are optimising for. And I would say that, like it has at least two consequences, it's like first, I think it's it's very difficult for a team to really see what are the long term implications or don't downstream applications of what the optimise. So you try to optimise for a specific metric, but like what are you know, we've talked a lot about how Facebook's recommender systems are alienating people by forcing them to watch more and more things. And it's not one team at deep at Facebook that has implemented the bad recommender system that is, you know, that is forcing people like that incentivizing people to watch the wrong things, or too much of the same content. It's many teams that didn't really probably didn't really notice that they were doing this, you know, it's, that's, that's one part of the silo effects of the of the tech industry. Another another consequence that I've observed a lot at Google is that the well, a lot of you know, these features, and these little bits that are implemented in three months, they actually often become kind of how do we say it? Yeah, to tool or in depth, they are deprecated, after a few months. So the reason is, either because research has progressed, and we believe we can do better, or just someone has another idea, and they just want to do it over again. So I've observed, you know, from a lot of colleagues that like it can create a real sense of alienation or insignificance of one's work, when you spend, you know, three months to work really hard on an idea. And then like six months later, this thing is completely, you know, this completely deprecated or it's over. But this is perhaps a, you know, another remark or something I've experienced, okay. But today, I feel like something, you know, something more is happening with AI, that I, you know, I've vaguely mentioned with this, this new neural network, these artificial neural networks. But when when, I mean, the new the new technology that we're observing now, these generative models, these these Chat GPT, and other DALIS, and image creations, there are more than just neural networks that try to predict a supervisor try to predict a label or y given an X, they actually take, they are able to take all the data that has been created by humans, and learn how to imitate it. So they will learn how the texts in a generic text can imitate a certain style or, you know, in mathematical terms will be how to imitate a distribution, distribution of the data. And so this imitation of human generic data can really like it's a whole new dimension that that AI is going towards, and that is not going to stop tomorrow, if anything is rather the beginning. And it will definitely, you know, change, you know, change a lot of our, our practice with AI. So, typically, you know, a lot of people have been wondering about how to build autonomous cars. Well, it so happens that it's very hard to teach one car how to drive. But we do have a lot of data from drivers, or at least it's not very hard to collect data from drivers, then if we wouldn't be able to imitate drivers, then you know, maybe it would solve autonomous driving. I'm not saying that this is happening right now. I'm just saying there are probably a lot of people that that are thinking about it. But there are other things like optimising chemistry processes and any sort of things where humans do it all the time. And you could just record it could be mutated by AI, with a lot of risks and a lot of a lot of, you know, pitfalls that I will mentioned just afterwards, but this is really what is happening now that AI is not doesn't need supervision anymore, it doesn't need a data set that has been nicely crafted by a human with collecting images and putting labels like this is a cat, this is a dog, no, this is no longer the problem. Now we can just generate a lot of images and like the machine can just imitate it. But so we've we are building AI and AI has already or is already out there, and a lot of people use it. And some of the some of the applications for AI some of the reasons why we build it are actually I would say good. Like, for instance, we have, we have improved discovery in science, we have also improved how we use renewable energy in complicated complex, like electric electrical networks, it can improve the life quality of differently abled people by like helping them to see better or to hear better, or, you know, give new solutions for education and personal development. So like, there are lots of things that we can do with AI that are, I would say, positive. And, but they all come with risks. And I think some of the risk, at least here like the ethical risks, societal risk, or economic and technological risks are all, you know, ubiquitous. And I think the community, the research community, at least the people I meet at conferences are entirely aware of what is going on. And some people even dedicate their entire career to studying you know, how to mitigate ethical or privacy issues with AI. And I think we are only, you know, we are only at the beginning in a way of of understanding how this works. But it's also a big collaboration, we need to we need to talk to economists, we need to talk to social scientists, and I think this is only beginning to happen. And, you know, I think one one of the key points that I want to I want to make it I will conclude with this is that, I believe that there are plenty of applications for AI and use use cases for AI that are still open. And that one important, one important way to make it, you know, accessible and to mitigate the risks and make the you know, our society adapt what AI will be, is to make it as open as possible to allow everyone to learn to I mean, my little 10 minute talk or 10 minute introduction to AI is is very simple. But I think a lot of this, this material, and this is common knowledge, which is B should be more and more, you know, like everywhere and available to everyone so that people can start making AI for what matters for them. And, you know, I have put some ideas here, you know, sometimes I think about an AI that could you know, detect tax fraud. I think about you know, how AI could actually support collective action by helping helping us predict what's the best thing we can do all together and helping us coordinates. I think there's a lot of ideas out there of what AI could do. But for now, the thing is that AI is only in the hands of a few people. And then there is only a few companies in the world that have the few people that actually know very well how to do those things. And it's really important that that AI remains open. And the reason why I'm bringing this up is because there are today, a lot of people that are very alarmist about AI and others say that is going to basically kill us all and that we should immediately protect it and make it make it a secret secret science and make it more like a military a bit like the nuclear bomb. And if you do this, then like that is the end of the game like the AI will be in the hands of only a few people. And I don't see how this will resolve the problem. So these are my thoughts. I'm really curious to hear what are your thoughts what you think about this, his openness of AI and also what you would like to do with AI? What you you what you think that we should build AI for? And with that, yeah, I will. I will give back the mic to my to Well,

Will Inskip (chair)  24:20
thank you, Claire. That was really good. Thank you. So anybody got some questions? We can open up the floor for some questions for Claire. That'd be amazing. Can I'm gonna be a bit cheeky and abused the chair and I'm gonna get in there first if that's okay. I keep asking this question on lots of different people because it's something that concerns me a little bit is and you actually demonstrated it in your introduction. The blackbox query about not knowing how the AI comes to conclusions. That's that's always something that's concerned me about back to the ethical concerns and things. Are they those decisions? Good ones or not? If that makes sense?

Claire Vernade  25:14
Yeah. So explainability. So we're trying to train to give a reason why AI took this or that decision. So AI, normally should not be taking decisions. AI is here to, like, predict something. And then, you know, eventually the decision should be made by someone else. But like, let's say that like, you know, that for a reason, X or Y, like the someone else has been skipped, and AI makes decisions by predicting something. So it predicts, you know, classifier and say, Oh, you're a criminal. And boom, you go to prison. And then we don't know why. So this is this is, this is obviously like a, you're right, a major, major pitfall and major problem. explainability is a research topic. There are a lot of people in my community that work on this, try to see whether we can actually find in the model what what has led to this decision, or to this prediction, but it's so far, as far as I know, still relatively open research topic. So definitely a problem out there. How do we explain so there are models that are. So neural networks are the big, big problem, if you there are plenty of AI, functions, type of functions, or their families of functions for which is extremely clear. What is the explanation? The problem? Are those big neural networks for those the mysteries did open.

Will Inskip (chair)  26:46
Thanks, Claire. If anyone's got any questions, please put your hand up. Peters the first one up, although there's some questions in the chat as well. But we'll go with Peter first, please. Hey,

Pete  26:57
Claire, can you hear me? Yep. So I've got a question. And a comment. When you said you think AI should be open? I'm asking what? What do you mean by that? So that's my question, what what, what are the aspects of it that are open, should be open. Number two is what it should be used for? Obviously, the Well, one of the biggest crises that we have is the climate crisis. So I would be looking at things like, you know, sea levels, cleaning up the sea, clean up the air production, and so on, and so on. So I think that would be a use, which would benefit the whole of humanity, providing the production was owned by the workers. And my third point is, I heard this week, quite unbelievably, that the, the AI companies in the states had got access to copyright copyrighted material for their training. And they did all their training on copyrighted material. Now I'm formally a researcher. In my own Imperial, and access to learning, you know, the materials of the other companies, it's very, very difficult. So my question is, how is it that they thought they could get away with taking all this copyrighted material and using it to do training? It's crazy.

Claire Vernade  28:34
So a very good point. Thanks a lot. So what do I mean by open? Many things? So it's a very good question. First, I think so first code, so code, code should be like, there should be code that's available that's clean, that's usable, that's readable, that's testable, you know, that's like that people can can have a look at. And there are already many libraries that are doing so for example, one of the tools that we use the most is scikit learn. scikit learn was created by you know, a group of scientists. One of them I think the Leading the Leading author of scikit learn is a French scientist, but there are lots of lots of people from from a lot of countries that collaborated on this and this is a tool that is so precious that like literally everybody uses it, even in companies like a lot of companies rely on these open source software. So open source software, having reproducible code and easily readable and accessible and testable this is like super important, but also these days, you know, Chat GPT and other you know, the big language models, they basically are they are black boxes, and we really don't know what they are and like making them open which would mean that like having language models for which that like the the architecture, so the In the form of the function or the parameters that have been learned on this, this, these dots and rows and edges, they should just be revealed to people. This is how this is my machine, you know, this, this is my function, like transparently. And there are there are a few companies. So there is one in France called Mistral and there's one in Germany, I think it's called Aleph Alpha. These are the two big European language models that are currently little startups, but they their their goal is to have open source release, like open language models for, you know, for the public. And I think this is this is really important as well that like people released that show us what is this black box that is making the decision so that we can test it, we can do things about it, maybe retraining, change it a little bit, you know, do a lot of things. That's another part of open. And then. So missing your second point. The third point was about the copyright content. The second point was climate change.

Pete  31:09
The application Yeah.

Claire Vernade  31:11
100% Climate said climate change and predicting, you know, helping helping scientists. I think it's already happening. People that you know, have improved significantly weather forecasting. With AI, but you know, that's that's one. That's one thing, if you were predicting the weather better, maybe you can also like anticipate some some events and do some things, but like, more generally long term for for climate change. I think there is definitely research about this. I do not know the details, but I agree with you. That is definitely one one good, good application area for AI. And regarding copyright, so I don't know how they did it. But how they how they thought that they could get away with it. But what I would say is that they thought that nobody would find out. And that was naive. Because we you know, like what the New York Times so the big the big trial that's going on is the New York Times, but maybe there are others, but the New York Times is going to be really the, you know, the stepping like, the biggest stone in in the calendars for a long time, because because the New York Times has put together a huge file of 1000 Page of like justification of how they prove that, like their content has been stolen. And it's obvious because if you query the GPT, well, you can get out of it. It's like copy paste from the New York Times. So like, that's how people found out and I think it was very naive of open AI to not to not imagine that their language model to just copy copy paste, like they thought it would be that they people would never find out. And so I think that's a good lesson for for this tech community. And is these people are training language models. And that's linked to like this, this remark I had on, on like innovation being prioritised over safety and robustness and privacy and other criteria. Like companies tech companies are racing so fast to get their AI firsts, you know, out first before the others that like they just overlook things that are you know, that are crucial and that like as soon as people have this technology in their hands, of course, they play around with it. Of course they test it, you know, it's like, of course, there's going to be a lot of things going on there. And they just, yeah, they I think they just went too fast to get it out. And that's why this happens. But it's gonna be it's gonna be quite a lesson.

Will Inskip (chair)  33:41
Thank you, Claire, we've got a couple of questions in the chat for people who can't use the mics at the moment. So John's asked question, what do you think AI should definitely not do? And there's another question a bit further down, saying why did the tools for AI primarily Nvidia graphics cards appear to emerge out the video game industry

Claire Vernade  34:08
Yeah, so not to I would say the big big red flag for me is like weapons like automatic weapons that you know that have a drone that has an AI on it and that just shoots people just based on in face recognition without any supervision of a human I think I think it's probably already happening but you know, regular regular regulation for weapons are is very special thing because obviously chemistry chemical weapons are forbidden doesn't mean that they don't exist. Right. And and I think regular regulating AI based weapon is extremely important and it has to be really clear and it has to be clear region, but as for everything with regulating weapons, is going to depend a little bit who uses them In the end, and so that's that's more like a diplomatic question afterwards. But like, I think it's it's very, you know, it's one of the things that that a lot of people say that AI should not do. There are probably like, I would say, other, you know, eventually, like we were talking about decisions earlier, I think in general AI should not be used to make decisions, be it, you know, on the weapon, but also like, as you know, if we talk about health care, it can help it can give guidance. But, you know, there are lots of kind of counterintuitive effects that happen when you give guidance to a doctor, it's easier for the doctor to just say yes, yes, and not actually look clearly clear carefully into the file. So like, when to ask AI for guidance is also a very good question. And this like, this, this idea of optimised deferral to AI or like, it's also a research question that is open and like, you know, not not, I think yesterday, I was hearing about some papers, recent recent research done on AI assisted l healthcare. And it's really difficult, I think, in in this kind of very crucial decisions. We should be very careful what AI actually does. And you said they're a bit further down, there was another question

Will Inskip (chair)  36:35
about the tools for AI, principally, Nvidia graphics have emerged out of the game industry.

Claire Vernade  36:42
So the thing is, work for video games and graphics, like we do have a lot of data. So it's easier to make API's that learn from all the data, like I said, you can feed a lot of data, and then you can learn models, and it can help you generate very, very realistic images from all this data that it has that it has processed. And we are very good now at computer vision at making those machines that are creating visuals based on a lot of visuals that have already been made. So that's why I think it emerged a lot in, in video games. I think there was also like, at some point, some people trying to play video games with AI is you know, to teach an AI to play Starcraft or these kind of things. So that's like DeepMind has tried to do this, Meta was playing something else at one point. I think these trends are a bit over. Because eventually, you know, after you've played around to how to play Starcraft, well, then it's not so clear what you gain from it. From, you know, like a science perspective. And I think some people got tired of it. So we've, we've tried, it's fun, we can do some things, but it's doesn't doesn't solve much problems of our actual, you know, world. So yeah, I think it's more anecdotic. But like, yeah, content creation, in terms of visual creation is is a big, big thing. And it's going on fully right now.

Will Inskip (chair)  38:08
Thank you. Put your hand up my friend.

Matthias  38:14
Yeah, hi, err the biggest thing I had kind of like two different questions. The first one was only sort of briefly about what it is, I could have bought more ground up movements, so like social justice and things like that. I was wondering if there's any kind of things you think that could be used for in terms of like the workers movement? And then maybe like, some elements of like finding it could be utilised in with lots of different things there. And the second question was a bit more out there. But there's like some of the bigger names in the like AI. Kind of like, industry, like people like Sam Altman, Elon Musk, you know, currently quite obsessed with like, ideas, like the Singularity and all this kind of weird, esoteric sort of stuff. And it might have been, and I kinda think it's more to do with like, just an ideological excuse for them to do whatever they want, because they're the owners and other super rich, so they kind of wants to seem like they're clever about just manipulating stuff to make themselves richer. Does he think it's any like reality into their kind of thoughts that we're all going to end up in some kind of like, AI singularity and stuff like that? Because I think the implications that has quite bad for the planet and like, people around the world, like kind of like for people that they kind of seem like they want people to leave them behind and just do what they want with technology, I guess.

Claire Vernade  39:52
Yeah. There's always two very good questions. So I'd say so far for the first question. Um, so yeah, there are. So I mentioned the rest of it quickly, because maybe I wanted to not take too long and takes more time for the discussion. But I think regarding what what we could do for the work for the, you know, what, how can AI could be used by the workers? I think that was your first question like, or how, yeah, so one of the lines was, like, you know, AI could be used to synchronise or coordinate collective action. So this is actually a project that I've heard of, in my lab, like, where I have a colleague who has some work on this, where basically, he says, look, take the gig economy, take take Uber drivers, Uber drivers, their salary, their their the price we pay for Uber drive depends on something that is computed by a machine, like some AI, that depends on offer and demand. Right? So like, the more drivers they are, and the less customers that the less the smaller the price and you know, like, it adapts, like as with the place you are in the time you are, and all those things. But like what if you know, all the Uber drivers, they would have an app on their phone, that would also be you know, like, basically coordinating them. So like, for instance, if like the price are very low, maybe half of the Uber drivers should go for an app and should take a break. And that would make the price go up, go up for everyone else. And, you know, you could have, you could have a bit of like, an app that computes, you know, what's the optimal strategy right now for the Uber driver? Should they take their break now, should they take it in one hour, you know, so that it balances the prices. And that like, it could create a sort of, like automatic competitor by coordinating the workers. But that's like, maybe, you know, that's at this point is, is maybe a bit of an addict logic, and maybe not so well studied topic, it's difficult to, to make these kind of predictions, but I think, in general, wherever it takes, it will take a complicated analysis of data to help people make decisions together, we've all read we can do with communications and, and, you know, in phones and things like I think it could, there, there could be things, you know, to help people coordinate to to do corrective actions that work. I don't have a very specific example in mind, I think we've seen revolutions started by people coordinating coordinating on Facebook, but it's not, you know, it was not coordinated by AI, it was just a Facebook was just the place where people were talking. But you know, we could imagine that like that some, some some, some collective action could be facilitated or helped coordinate by like, optimising how people what people should do and when and where they should be. To better to have the highest impact, obviously, it's, it's also risky in many ways, but like, yeah, we can imagine these kind of things.

Will Inskip (chair)  43:05
That's the thing there was the next question was Matt.

Claire Vernade  43:11
Oh, singularity question was mentioned AI. Yes. Sorry, the singularity questions. Of course, the singularity question. I think so. Okay, so like I said, there is a lot of, like alarmist people who get out and say these things that like AI is going to, like, take over and become, you know, sentient, and you know, that we are creating some sort of weird, new superhuman, I believe none of this. And also, I would say that, like, almost anyone I know, who is serious in my field would not go for these statements. The reason I don't exactly know why these people say this, it's a sort of, I think, it's, it's a way to make what they're doing bigger than it actually is. Because of you see, if, you know, you just say I'm creating a little chatbots it's less nice. And if you say I'm creating a super intelligent machine that is going to take over the world. But I, I'd say that like, it also distracts attention from other problems that are, I think, more serious, you know, like the impact on the economy, the impact on like, the society and things like this. It's much you know, if you start talking about some sort of metaphysical we're, you know, we're creating some sort of God thing and it's a bit I don't know what I think it's a it distracts a lot of the attention from the real things. So I'm really not I'm really not a big fan of what's going on there. I think. I have, you know, it also divides a little bit the community I don't want to I don't want to hide that. Like there are some people that that believe that soon we are going to get AGI artificial general intelligence and I think that's what also deep mind was working towards. I think it's, I think it's misguided. And I'm not the only one to think, to think like that. And I think at the moment, we're far from having something like a superhuman intelligence that's going to do everything. It doesn't mean that there are no risk with what we're doing right now. But like, if there's a risk, I don't think the problem is the singularity and the sentience and all those things. I think the problems lie elsewhere. Yeah.

Will Inskip (chair)  45:36
Thanks, Claire. Matt, if you want to come in now, please.

Matt Whaley  45:45
issues there lowering my hand and unmuting my microphone. Sorry. Yeah, Claire just wanted to pick up on I'm not, I'm not suggesting one when any of these sort of Terminator scenarios are are going to happen. But we have seen with AI when it's been used, where discriminatory traits have occurred, for example, where they've used it to analyse job applications and that kind of thing. How does that happen? And how can that be avoided in the future?

Claire Vernade  46:13
Yeah, that's a hard question. So this, this problem of AI of fairness, or the biases of AI, classifiers are everywhere. The reason why it happens is, I mean, there are two there are several reasons and that combines together to you know, make everything go go bad. So the first reason is that the data we use, to train those machines has to be collected, and that there is discrimination in this society. So like, already, you know, it's like, the data has a bias. That is, that is the one we put in. And then there is a poem that like the data is the data you collect. And the poem is the people who collect the data are us. And people make choices when they collect data. And they also make choices when the design models and the design, you know, like I said, there are lots of choices, you make the neutral, you choose the type of your function, but you're also going to choose what you call an error, and what you called an not an error. And, you know, it's this, the machine is going to try to minimise the error that you put in. And there are lots of problem with this problems with this. The way people design is this, the system contains all the biases that we have the all the biases that are in the data. And it's very difficult to test, whether you're being biased or not, and what are the long term implications of these biases, you know, if you start giving more credit, so like one, one typical example is credit score. So because that's exactly what's happening, you know, you it's especially in the US, you you give like a sort of give all sorts of information on you, and they compute your credit score. And as a threshold, and if you're above the threshold, you get a credit, if you're below, you don't get the credit, then if you start to try to be fair, and according and giving credit, credit scores and fairway to different types of population, then like, you can, you know, you can imagine all sorts of things that are going to happen. So like, if you, if you lower the threshold for a certain type of population, then you're going to give more credit to them, but also many of them are not going to repay because they're just above the threshold now. And so if they don't repay, it's gonna make their life harder in the future, you know, these kind of things. And there's, there's a lot of long term and complicated implications of biases in data. It's a very big problem. And there is a lot of research. So since 2016, I would say, around 2016, 2018, it was a big peak of research on this topic. And it's, now it's going to going to be going down a little bit because there's more trendy stuff, but like, there is still a lot of people working on this problem of fairness. And it's very difficult. It's very difficult to really resolve it. And for one reason is that these functions are black boxes. This function, these big neural networks, we don't understand fully what they do. So it's hard to explain. So it's also easy to get away for like from from the bank, you know, it's easy to get away with like, I don't know, what your credit score is credit is like this, you know, it's and, yeah, I don't I don't have like the full answer. But I would say that, that this is an issue that the community is very aware of, that we as researchers are trying to do something. The question is Do companies banks, you know, all the people that use them want to do something that much I don't know, and that's that's, you know, that's another another problem. But yeah, good question.

Will Inskip (chair)  49:50
Thanks, Claire, as anybody else got any questions they'd like to raise or points they'd like to make. Hopefully I can see what's going on. Yep. Mark,

Mark  50:04
Helen. All right to me. So I mean, thanks, Claire. It's very, very useful. But I mean, we're back to the fundamental questions again, aren't we, it's, you know, we're trying to keep a technology available for everyone, which a significant amount of very wealthy people would like to monopolise themselves. You know, and we've seen that happening over and over again, you know, we can't, we can't invent this technology, and we shouldn't, you know, why should we only invent this technology has huge benefits to mankind? The problem is, is that, you know, we're already seeing these, you know, some of the AI tools, and, you know, some of the other algorithms that are out there, that, you know, we're taking away jobs, from certain areas, you know, certainly, for those, you know, worked in the helpdesk areas, you know, significant job losses there by using, you know, chat bots and other AI type type techniques to deal with calls, but they are, they are incredibly good at what they do. Yeah, I mean, the response times are very, very good. And the accuracy of the information is usually very, very good. You know, so you can't argue against that. And it can take away mundane work, you know, from people who are repeatedly answering the same question over and over again, reading a script, you know, so you can't argue against that. But the problem is, is, you know, we've had other speakers clear that you might not, you know, I've talked about how it can liberate people and whatever, but that's not how it's being used, it is being used to basically reduce the amount of people on on the desk, on the help desk, you know, in take away from jobs. And, and it's a, it's a different technologies, technology is now only attacking, you know, lower level jobs, can you imagine the impact of, you know, self driving vehicles would have on, you know, certainly the, you know, the UK workforce in the global workforce, the amount of workers that are fundamentally involved in driving goods from A to B is huge, but then the other side today, it could also have a massive impact on reducing the emissions from, you know, the way vehicles are driven, and the automation and better routes, you know, but again, you know, the impact to those individual workers, you know, would be huge, but also above is starting to really akin to middle class type roles and jobs, you know, you know, solicitor type, you know, legal secretaries, people like that, now, that some of these chat tools are being used to produce the letters and the documentation that was again produced by, you know, legal secretaries, or whatever, who were fairly well paid qualified people, and they're losing their jobs. So it's, it's an impact of both ends, it's an impact at an entry level. And it's definitely an impact, middle level, and it's, you know, we're seeing starting to see the impact on our industry as well, with that, you know, around programming and how it can be used to develop programmes and that, but, you know, the fundamental issue is described, you said, you know, we need to make sure it's available for everyone. But, you know, yes, it needs to be available. But, you know, we also need to understand how we're going to control how the technology is used to benefit mankind, you know, rather than to drive more and more people into, you know, absolute poverty is where it could could lead. So I don't, I don't see a danger in the technology. It's the classic thing, it's about how this technology is used to benefit us, rather than just to drive up the pure profits of some already very rich and very profitable companies.

Claire Vernade  54:03
Yeah, I am very aligned with what you said. I think it's all very true. So it's a big open question, or at least a big complicated question to which our economy the way it is right now. Do not respond like well, and it's, I would tend to think, you know, it's difficult. I don't maybe don't have the right background to answer very precisely, this question, but like, one, one thing I can think of is that like, basically, by using this technology, that has been developed by you know, a lot of public researchers but also a lot of like the rest of the society by using this technology, the company gets a real boost in the productivity of the workers. So this should be paid back in taxes. I think basically, usage of AI in the community in the in the company should just be taxed. Just like financial transactions, there was like a known for a long time, like there was this like pressure for like, taxing financial transactions to avoid like shock, you know, high speed training, trading and all those things. I think just usage of AI just like any other, you know, any other technologies like, it's like using I don't know, it's like using roads or using using public services you pay you pay in taxes eventually to, so that like so that money flows back. And so one, one idea would be to make sure that like, the tax system reflects a bit like, what's the cost for society of having AI around and the cost for societies. I mean, as long as we you know, that, that things work like this, that we're in this system is that they are like poor people that we're going to have to help more. And that, you know, that's, that's, that's what it is, I think, eventually what we should do is like, create jobs or create education, create or create jobs and in health and education in like all sorts of public services that were that are currently defunded. And we should just make money for creating those things. That's like, maybe the political sides of my of my talk that that I mean, like for which I don't really have the scientific background to talk about, and they are probably better people than me. But like I, you know, I'm really aligned with you in the sense of like, it's, it's yet another of these big technology that is gonna make a lot of few people much richer, and a lot of people are much poor. And like, the one way to rebalance things that we've used in the past was to tax them. But for that we would need to be all agreeing is like, they should not be able to go to Ireland or Luxembourg to you know, like, they cannot do it. So that's the problem right now, right? It's like, we have we don't have international tax agreements between even between Western countries like Luxembourg. I mean, this is, for me, this is like this is a problem. But yeah, I think we should not underestimate like, what's the productivity gain? And if you get if you gain productivity, then like, I think this should be taxed as part of, you know, the company's cost for society in a way. But I don't have the full answer.

Natalie  57:23
Also, I was going to say that the companies that are investing all the money in it, they're the ones that have the money. And also they're the ones who prepared to put money into longer term research problems. So it's the money is either going to come from business or from academia. And often academia needs business in order to get funding and grants because they can't get it from the government. So they're not going to get very much from their students. So then they have the money and, and they have, and then also, you know, they're going they need to pay researchers, they need to pay people, lifetime salaries, maybe for something the massive development that might only happen in the fifth or 10th or 15th year of that career. So there's a longer term investment there. And sometimes, unfortunately, we see so often that governments go, oh, no, we can do this instead, you know, we don't need to pay private companies to do it. And then they end up because it's not their specialism, they end up spending loads of money, and sometimes wasting loads of money in things that don't end up being as good, because businesses at the end of the day need something to work, whereas sometimes governments, you know, it gets overtaken by politics or not very, not people who people who aren't really specialists, like, you know, ministers might be in charge of education, but they might not necessarily be educational specialisms, and they might lose their job after six months and change for another one. And if you can imagine them taking care of really, really big research in AI, it might just end up being a massive waste of money of taxpayers money at that point, rather than another point. So I'm not saying big businesses should make all the money and have all the gains. But I'm saying that's why the power ends up in those hands is because they have the money in the facilities to invest. So if that's completely obvious, but that's no.

Will Inskip (chair)  59:07
James, you put your hand up and you're not unmuted.

James  59:26
So better yes. Sorry, sorry. A slightly different question for Claire. If, if we've got a couple of minutes. So you're you're teaching undergraduates now? How would you describe the type of student of machine learning? I mean, are they? Do they look up to Elon Musk? Is it all about individualism and entrepreneurship, or are they kind of socially conscious? I'm sure it's a mix but I'm just interested in You're the type of person who's on this course. Do you see them? I don't know, going into big tech companies naively or are they more savvy about kind of the realities of the working world?

Claire Vernade  1:00:20
Yeah, that's a very good question. So I would say that where I am the students, the students are, we're very socially conscious and not at all a little Elon, Elon Musk fans. Like it's a you know, I mean, a small town in Germany, it's a big university town, you can think of it as Cambridge, essentially, it's very similar. They even do punting on their little river. So like, it's essentially Cambridge. And, ya know, people are very, you know, very conscious of the risks, the benefits, the problem is they, I think, much less conscious of what are tech companies and what's the workload. And that's kind of the work I've been doing. Since the beginning of the year, I've been giving a few talks already, they've been that have been very well received. I've had a lot of questions from students and equities, you know, what it is to work for big tech companies, and how does it work? And then they're really curious and sometimes a bit surprised, or maybe disappointed by, you know, like, that it's breaking a bit of an ideal that they would have built, but they're there, you know, I think many of them still would like to work for these big tech companies, because, you know, it's, I think they've, they've made themselves very desirable, you know, Google has free foods, so it can actually be cooler. And so, I think, I think there's, they're just making themselves quite likeable. But there are lots of students also that just, you know, either want to, like, maybe create or participate in some little startup that would do something that would do something a little bit more conscious, or like, you know, students that would like to work on these open language models that the European Union is definitely pushing forward. I don't know. It's, it's, it's, I'm not exactly sure what the students what's the, I don't think we have a carry up poll of all our students, and we know what they are doing. I don't it is I don't have this data. But I found them very open to this debate and discussion. And it's, it's very refreshing, it's actually at least in my university. Now, I would believe that if you go to like, bigger university in the US, things are a bit different. Though one of the leader in leading university in this field is Berkeley, and Berkeley is very alternative. It's very, you know, they think a lot about like the the impact the future impact of AI, they have a lot of this community there that is working on what they call AI safety. So the idea of testing and trying to see how we can build AI that is not going to take over the world and become this superhuman, super intelligent, kind of weird machine. So I think these people have a bit of this like, weird, you know, alarmist mindset, but they want to do something against it. They want to make AI better and AI better fit for, you know, our human future. I think there's just a lot of good thinking going on in academia in general, that goes really against what's what's the most present in the media, which are these like Elon Musk's and Altman debates and all these things? I yeah, I think, I mean, the AI community is not, it's not perfect, but at least it has this very, very deeply anchored open source. mindset. So like, all our conference, all our publications are free, and open source. And, you know, they, they we don't, we don't pay for journal publication, we don't pay we it's all self organised. So like, there is no nature, there is no to all those things in computer science, at least not in AI. Right. So like, most of the publications that are actually impactful in AI are all publicly free available, you can just find them online. And this is something that's been going on for a bit about 20 years. And that I think, has contributed a lot to where we got and has built this mindset, this mindset of like we're, you know, we're not You're not giving all our you know, we're not giving up our you know, ownership of what we're doing to be companies or even not even publishers. I hope it stays like this. I hope that in we have to fight every day, right? Like because nature obviously created nature, machine intelligence. And so it's tempting for people to submit to nature, but I think for now The, you know, the most important research publication and research community gatherings are all our nonprofit organisations self funded by the community. So I think it's still there's still a good hope that we're on the right track.