I'd like to welcome you to the Trinity Church, Straight G lecture,
the Straight G lectures O the distinguished lecture series from the Department of Computer Science.
We have a hash tag which is in front of me. Hash ox, straight Tilak.
Please do tweet at me. Nice about it. If you are if you are going to tweet.
Before we begin, I would like to acknowledge our sponsors.
Oxford Asset Management now for a number of years have been sponsoring these lectures and it's been a transformative thing for us,
enabling us to bring in speakers and to put on a show that we simply wouldn't have been able to do otherwise.
So we're extremely grateful to Oxford Asset Management for their continuing support.
I I'm sure they would like me to point out that they are hiring.
So if you Google Oxford Asset Management, you will hire, you will find many job opportunities.
But thank you to Oxford Asset Management. So now on to our speakers and I'm delighted to welcome Neil Laurence.
Neil is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge, where he leads the university's flagship mission on A.I.
A.I. At camp. He's been working on machine learning models for 20 years,
and he recently returned to academia after three years of director of Machine Learning Amazon.
His main interest is the interaction of machine learning with the physical world,
and this interest was triggered by deploying machine learning solutions in the African context, where end to end solutions are essential.
And this has inspired new research directions at the interface of machine learning and systems research.
And that work is funded by Senior A.I. Fellowship from the Alan Turing Institute.
He's interim chair of the Advisory Board of the UK Centre for Data Ethics and Innovation and a member of the UK's A.I. Council,
which advises government on all matters. A.I. Neil is a visiting professor at the University of Sheffield,
where he was for a number of years as an academic and is the co-host of Talking Machines.
And the title of Neil's lecture could hardly be more contemporary use or be used.
Regaining Control of Artificial Intelligence. Males, The floor is yours.
Thanks to this chip that's working. Okay. And thank you for that wonderful introduction.
And thank you, Mike, Leslie, everyone, for the invitation and an apology.
This invitation was originally for a year ago, and I caught COVID just before I was due to give the presentation.
The title is actually the same, so imagine how much better the world would have been had I been able to give this lecture a year ago.
Look, another another consequence of the pandemic.
Okay. So I try and use historic.
I'm not going to give a technical thought really today. I'm just going to talk about issues that lead to technical questions.
And when I think about artificial intelligence, I like to sort of use these three apocryphal quotes that will be in the talk today.
So the apocryphal quote about Henry Ford is when?
And then he says that if I'd asked people what they wanted out of a car, they would have asked for faster horse.
And that provokes in my mind the question. So if you ask people what do they want out of artificial intelligence, is the answer a smarter human?
And then the question that follows is, is even what does that mean?
I think, by the way, if we had a faster horse, we'd already have autonomous vehicles.
So that would have been a pretty cool thing. So maybe the customer's always right.
Now, I talk about this a lot and I'm only going to go through it very briefly because I just want to contextualise the way I think about it,
which is when we look at humans in human intelligence, I've been trying to do public understanding, machine learning folks for about ten years,
and I wanted to capture why machine intelligence is so very different from human intelligence and
simply thought at one level it's because I'm speaking to you using sound and machines communicate,
using radio waves or light speed and cables.
So they communicate about a million times faster.
And this leads to all these sort of consequences that for me to talk to you if I'm talking in a good rate of speech,
if we look at how much information I'm sharing, it's about 2000 bits per minute.
So that's a bit as defined by Shannon, an information theory that's the equivalent information as 2000 coin tosses.
So it's about telling you the result of 2000 tennis matches where the odds were even every minute, which is pretty good now.
But to do that, I'm sharing with you the thoughts are inside my head, and it's very hard to estimate how many calculations per seconds we're doing.
But if you look at the best estimates I could find for simulating a brain,
you would need a machine about as fast as the Met Office supercomputer down in Exeter,
which can do about 1,000,000,000 billion calculations per second.
Now, if you compare that to a simple computer, these slides actually date back a few years.
So maybe they're a bit faster than this. Nowadays, a computer can communicate at 60 billion bits per minute if it's using gigabit per second Internet.
So that means if we turn out, that's a nine to salary.
So imagine your university student stipend is $20 a month.
It's not great money, but it's kind of maybe enough to survive on.
And then imagine your university stipend is $60 billion a month.
It's actually closer to the budget of a country, so it's closer to what the UK spends on the whole of health and Social Security $60 billion a month.
So it's operating in terms of this information exchange, much, much faster.
But then our best estimate is that a typical machine in terms of number of calculations, it can do per second.
And I've done discriminant calculations a second distance. You see there are about 100 billion calculations.
So actually a typical machine is doing a lot less compute per second than our brains.
Of course, we can't access all that compute. Neither can the computer access all the compute it does.
So imagine if I wanted to share with you 1 seconds worth of calculations from my head.
If these numbers are right, it would take me a billion years to tell you all the calculations.
5 billion years to tell you all the calculations that had occurred in my head in 1 seconds.
So while my battery is running low, this is an. I'm not sure I was referring to my brain or the machine.
Not me. I can see a blob that's not strong. So I meant to infer human.
Human intelligence. So, 5 billion years.
What is a computer? What did you share? 1 seconds worth of computation.
You would take it about 20 minutes. So this means your intelligence is locked in.
So what I mean by locked in is you can hear these stories.
And sometimes in a longer version of this section, I would talk about a guy called Jean-Dominique Bobic,
who was the French editor of Elle magazine, who was locked in, could only communicate by winking.
They made a film about him. He wrote the book about the use the film after he was locked in.
And I think that's a state we all think about. We all think, oh, wow, what would it be like to be in that state?
Well, that's the state you're in, right? You just evolve that way.
You don't notice. But compared to the machine, that's where you're at.
So in some sense, it's already a nonsense to talk about a more intelligent human being,
because the human being is defined by constraints, not by capabilities.
We're defined in terms of our intelligence, in the way we share information by a limited ability to communicate.
And we've evolved in that state for hundreds of thousands of years,
as Homo sapiens should say that in the Natural History Museum and for, you know, millions of years as primates, etc., etc.
And all animals do this to some. Well, mammals, higher mammals do this to someone great, to all that sort of thing.
So second apocryphal quote, the six word novel never written by Ernest Hemingway,
but apocryphal written by honest Hemingway for sale baby shoes never wore.
Now, so that six words, 7 to 2 bits of information on average, 12 bits per word we have.
But there's so much more meaning in those 12 words than 72 bits of information.
And that's because you exist as humans. You sit within a wider culture, a wider society.
And you can imagine what it would feel to be the human that has to place that up.
So we use this information bandwidth in a way that allows us to lean on the fact that we are all human and we understand what it means to be a human,
to communicate things that feel like there's a lot more than 72 bits of information in there,
because we can all imagine what it felt like to write that advert.
So although the information but I'm sure our context is very important and we
can extract a lot of information from very few provided pieces of information.
So does anyone know who this apocryphal quote is? Apocryphal divine.
There are three types of three families and statistics. It is Mark Twain.
Not apocryphal in Mark Twain, but Mark Twain appropriately credits it to Benjamin Disraeli.
But it was said by Lord Balfour, the foreign secretary tried to do it to someone else at some point.
But the point about this quote is that one of the reasons that we end up misunderstanding and misinterpreting
statistics is because we're so information constrained that when we get little bits of information,
we tend to overinterpret. We tend to see more behind that data than we have any right to.
And that comes out in a bad way in statistics.
It comes out in a good way when you're imagining what it felt for the person that had to write that advert.
So I kind of apocryphal.
Benjamin Disraeli I tend to think one of the the modern equivalent of these quotes is this There are three types of lies, lie families and big data.
Because what's happened in this modern data era and that's being driven by the machine interconnection,
is requiring more and more information, more and more data.
And we're basically forgetting the hard lessons of a field known as mathematical statistics.
So that quote from Disraeli predates the mathematical field of statistics.
So I'm quite close to computer science, government statistics.
They study mathematical statistics. They just don't say mathematical statistics.
They study that the they say the process of understanding whether whether you can believe what the statistics is purporting to say or not.
And we don't even say mathematical statistics anymore.
But it was a new field that had to come in to stop people misinterpreting statistics, which is the sort of science mistake.
So that's Karl Pearson, of course, unfortunately associated with that field is a lot of fairly unpleasant things like eugenics.
Most of the early mathematical statisticians were also involved in eugenics to one form or another, because they believe once you could tell this,
we should be looking to improve the human species in some way, and that led to some quite problematic results.
So that is still an open question about what does mathematical data science look like in our era.
And it's being triggered by this sort of new image because we now have an evolved relationship with information.
So in the past, very, very far past 100,000 years ago, we could go in the museum and check,
but I think we would been in groups of about 30 to 50 operating together,
hunter gatherers, maybe slightly larger, trusting each other, basing sort of inferences about what's going on.
Those relationships of trust and understanding who each of us is. Since then, we became we came into cities.
We've now grown things. We have to introduce laws to govern us.
And we also ended up with statistics to look at how those cities are being managed.
And we've had to teach ourselves not to misinterpret those statistics when making decisions.
We removed ourselves from that direct contact of person to person, and that's led to this evolved relationship with information.
So in the past 100 years, you know, this is the situation that you had humans with this very low bandwidth connection.
And a lot of what we were trying to do was manage that low bandwidth connection
to ensure that we weren't misinterpreting the information we were receiving.
But now what we've got is this We've got an enormously high bandwidth connection between
the machine and data and then a low bandwidth connection between us and the machine.
And this is leading to all sorts of problems. You know, and I we can we can look back over the last ten years in terms of election manipulation,
in terms of social media, etc., etc., and what that's done.
And you can put that down to the fact that the machine is now mediating what information it chooses to show.
I mean, it's actually using classical statistics to work out how to draw us in to our engagement with the machine.
But it's not using classical physics to say, how can I share the truth with this individual?
It's using classical statistics to say, how can I cause this individual to engage with me?
And of course, the answer turns out to be share information with them that, you know, fulfils their prejudices, that type of thing.
She's not great.
So what we have is this as the challenge for data science, that we're trying to deal with this new high bandwidth connection to the machine,
which is then presenting information to us in the low bandwidth way, and we've got classical statistics being the traditional way now.
The first thing I want to emphasise is when you see this connection, you realise, well,
the statistics have a massive role in mediating these new connections and understanding what's going wrong.
But it's not everything. But there is enormous role of computer science as well.
You know, mathematics just in understanding this, this new flow of information.
But then the thing I think we need to also be talking about on top of that is this new wave of movements which are actually,
I would argue, operating almost directly on that link between the human and the machine.
So forming new ways for us to communicate with the machine.
So in the past, what a statistician would do if they wanted to represent data is they would train the human to
understand what a P value is or apparently misunderstand what a P value is or what an error bar is,
or how to interpret a graph.
And then the machine would share, you know, if you were using the machine that information in the form of a graph and provide that information to you.
What we've got now is machines that will just talk to us and when they talk to us,
they are going right to the depths of that desire to overinterpret because that feeding into our desire to anthropomorphise eyes.
And I can't say anthropomorphise very well. So I just say anthrax.
So we all tend to treat complex things as if they are human in some form, whether it's our cat or whether it's our car or whether it's our computer.
And when machines are starting to exhibit language, that tendency is going to increase even more.
But the problem is that we mustn't forget this fundamental difference, that these intelligences are quite different from us.
They are quite alien to ours. They can augment us.
But the point in this tool is they can never replace us and they should never be allowed to replace us,
because at the core of our intelligence is our limitations.
And those are things that right from its birth, however capable the machine has, it will never have those limitations baked into it.
Okay, so when I think about the scale of what we're experiencing now,
I was giving I was convening the UK, AI Fellows, students and postdocs at a hackathon,
partially because I strongly believe that we are so dependent on that generation
of people to deploy and use these technologies in the best way possible,
because it's not going to be the old people like myself who understand the ways in which these things can be deployed and used.
We can see that in every previous revolution it's young people. So I've tried to come up with analogies for how big I think this revolution is.
And sometimes I talk all right about the printing press and the sort of extraordinary effect that had 500 years ago leading to the Enlightenment,
but also sort of 500 years of war around religion and all sorts of things going on in Europe.
But then I kind of caution to the printing press doesn't go far enough.
So the revolution I think we're engaged in at the moment is arguably akin to the revolution of rights.
So this is a sort of early cuneiform tablet that if you go back to well, I started working with a colleague,
geologist in Cambridge who works on Nepal from 1500 B.C. to 1000 B.C. and reads these tablets with,
you know, court cases written down this time that as far as I can make out,
the Greeks were some form of barbarians going around the Mediterranean, attacking cities.
And at the same time, the city of Nepal has 2000 years of written history going back to, you know, before Gilgamesh existed.
So this is a society and a culture that existed some 3000 years ago that had 2000 years of written history,
written in the form of these sort of clay tablets.
But then the thing I sort of think about is what was it like for the first person when when the Epic of Gilgamesh, which was an oral tradition,
like presumably most of these traditions were like The Odyssey and everything else was suddenly written down for the first time,
and the sort of bard who's learned this whole epic and can sing it and do everything else.
He turns up and says, Now this or I may I put it written down, Now don't need you anymore.
We don't need you to remember things because we can remember things.
All a piece of tabloid. And the first form of usage for these tablets was not to write down poems.
It was to write down accounts. So-and-so owes me three sheep.
I owe them four lots of call. And I think there's a tremendous parallel between that and the computer,
which so far has mainly been and somehow a tool of the accountants excel spreadsheets.
It doesn't and can't interface with us in the poetic language we might like to use and what it suddenly gains.
I mean it. Pearson But it's not something.
This has been going on for a number of years, but it's crossed the threshold into, Oh yes, you can talk to the machine.
And I think that this is really one particular reason why this is sending society into these sort of spasms of
excitement is is related to an experience I had in 2017 where we were doing a Royal Society report on machine learning.
So people were thinking about this a long time ago and worrying about how technology would change things.
And we were very focussed at the time as it was on my Richard and Daniel Susskind on the future profession.
It's a very different technological transform, the work of human experts,
and we were doing this work from 2015 to about 2017 and about midway through the work when we were coming up with
a lot of conclusions about how the lives of the middle classes were going to be affected by this revolution,
we had a particular occurrence that really stuck with me and that was the Brexit vote and we were in the wrong society.
About a week later and we were sat around a table and we were all digesting the consequences of this vote.
And there were employees from all parties and there were policy experts and there were domain experts.
And one of the MP said, Well, I bet no one in this room voted for Brexit.
And it suddenly occurred to me, I'll bet your rights.
And isn't that the problem?
Because the people who voted for Brexit are the ones who were disengaged from the decision making, and that's why they voted for Brexit.
And you can see that in populism wherever it exists. And that gave me a great fear because this book speaks to the empowerment.
It speaks to all of us that are already engaged in decision making.
And that's also what you see going on now. Now, I'm not saying the allies aren't going to be transformed as educated people,
but my experience is that educated people are amongst the best equipped to handle such change.
And mean what I have in my head is that of the coin pushing machine.
So this is a coin which I used to love before I as a kid, I don't know if anyone knows what they are.
These are the more fair grounds. You've got to bring the Blackpool or whatever else in you.
You've got these two pennies and they go on the sets and the sets move in and out and you draw the
coin and the coin sort of goes in the sand here and then it lands and it should push these coins off.
You can see these coins are deformed and then when the step pushes out, some coins will fall off and hopefully you'll win.
Now, which coins fall off? Is it the coins that move? No, it's the coins on the edge.
Because those people who are already on the edge of society, who are already challenged by things,
are the ones that are going to be affected by this form of disruption.
And my sense is, yes, those of us in the professions, those of us who are educated can do things well.
We'll have to learn. We'll be disrupted in a major way. But we actually have the intelligence and capability to do your jobs.
It's the knock on effects that it has on those people that have already given up
that I met a guy in Lancaster once who used to who decided not to go to university,
wanted to be in a band. He went catering, called into catering. He worked at the prison.
The president got shot.
And then, of course, I met him because he was driving a taxi, because that's where, if you ask any taxi driver why they're driving a taxi,
you'll always get this sequence of stories about, you know, because anyone can sort of drive a taxi.
But there are lots of people's where there isn't much demand for taxi.
So when we did have a call for the Royal Society back in 2017, it was called machine made the complex computers that lead by example.
I still think it's a really valid report that is actually still referenced today,
but one of the things we did is we spoke to people and we asked them what they would like machine learning solutions to do.
And so we did a it was the Ipsos MORI poll on public views, machine learning.
So this is qualitative research, which I think is going to be increasingly important in this domain.
And I understand in qualitative research, even if you're a quantitative scientist, needs to improve dramatically.
So these are the sort of things they said, and I'm not sure if you can read, but these are sort of areas we looked at.
So we asked about health and health and they can see the greatest potential benefit to individuals and society.
We asked about social care,
so they saw the potential for resourcing issues that they fear to mobilise machines with due to involvement and emotional contact.
Right. So that's clearly a worry and sort of thing. But they could see that there was benefit, the best case scenario, unachievable.
She and they were keeping carers to spend more time with patients.
I mean that would be brilliant if that's what we've done in any of our hospitals or social care.
I don't think it's a very good record.
But marketing maybe even thought marketing was, Oh yeah, we could be used to tailoring marketing and that might not be a bad idea.
Transport driver's cars could have benefits.
These are just regular people.
They were sort of in Birmingham in a sort of like small groups finance universe and thought about our to monitor potentially fraudulent activity.
People are really, really smart about what they want technology to do.
Participants tend to think machine learning and crime the thought patterns in.
Good idea. So let's see how might work accurately in practice.
Smart people, they saw it as a useful tool to aid the military police resources but concerned about consequence to stereotyping.
Individual people are smart. This is why at a time, by the way, when most people had not even heard what machine learning was,
that was the first thing you had to do is educate them what machine learning was.
Public education partisans are concerned that tainted education based on machine learning
would result in killing and limiting people to some career path that too young and again,
really, really smart. But they also sort of see benefits in that.
What is the one area that all participants thought was ridiculous?
That we should not be trying to do that? They could see no point in it whatsoever.
RS They still fail to see the purpose of machine learning and read the poetry for all.
Other case study.
Participants recognise that machine might not do a better job than a human, however, but they do not think this application creates art,
as doing so is considered to be a fundamental human activity that machines could only mimic best.
Where have we made the greatest progress in machine learning in the ensuing six years?
In mimicking what humans do.
So in honour of that, I often get to go right to Silicon Valley That introduces a large language model us spoken by Mickey McHugh.
This was chapter 54. Chapter 3.5 didn't do quite as good a job, but yes,
I don't know who pockets to enable new POC is as if woven by is POC inappropriate person to create?
Largely because of different play, but it's not a fixed.
Right. Well, is the crossover plan. It's the one where they create large language models and there's roaming now.
And you know, it's in nowadays. You know, you've got better call Saul, all sorts going on.
The characters appear in other things. Digital Athenaeum.
My goodness. But what would that fundamentally be?
So I want to say, does anyone recognise what this map is of?
You're not. What's. What's the map of. Western Europe.
Yeah. And can you guess what year it is and what the circumstances of the matter.
Well, go to that site and can you guess the date? There's only a few significant dates in World War Two before, but again, in 44 June, Portugal.
Yeah, June the fifth, 1944. And can you guys see my penis?
As a former teacher, it's a job and not of the weather stations.
Yeah. So it's the pressure and the different motivations. And and actually, you know, I get it from the ice baths.
So what was going on in June 44 was that there was a storm in the channel.
What was also going on was that the tides were at the right level for an invasion of Europe to occur.
What was difficult for the Germans is that the weather comes from the West and that way the stations were on France.
Does anyone know where Rommel was on the day of the invasion at his wife's 50th birthday party in Germany, because he got the weather report and said,
Well, there's no way there'll be an invasion tomorrow so I can go home to my wife's birthday party and everything will be fine.
Of course, he was kind of rudely awoken when I say and had to drive rapidly back to the front.
Now, if we look on the alternative map, this is the allied view of the weather on that day.
And the allies actually on the same day, the front says, and they understand that there's going to be a gap in the weather.
And that's actually multiple weather reports that are in conflict in this even controversy
today over who agreed that there was going to be a significant gap in the weather.
But the interesting thing about weather forecasting in both these cases is it was being done by interpolation.
So you had a number of weather stations spaced across Britain.
I mean, actually it's interesting to me. Martin I'm not sure saying we should have had weather information from France because of Enigma decrypts,
but whether they would put it on the map or not.
So we could actually interpolate across the channel when we did our weather reports, whereas the Germans,
I don't think, could have some weather information on the previous map, I think in Cornwall.
So I'm not sure how they're getting it and what the CORNISH doing.
But basically you're having to interpret. But for Britain, you're interpreting in the right direction because the weather is moving in the right way.
A modern weather forecast is, of course, done using Navier-stokes equation on that big computer done in Exeter,
and they use Navier-stokes equation that allows them to extrapolate. And that extrapolation is sort of key in giving us these long term predictions
up to five or six days because we understand the physics of the weather.
We're not just looking at what the pressure is instead of having a stab at where it might move through some minimal sort of examination over time.
So that's why the Germans got the weather wrong. Now, what's that got to do with TATP?
Well, fundamentally, all it's doing is it's operating. It doesn't have access.
It's in this position. It doesn't have access to the future where humans are given this tool and they
start to work with this tool and they start doing what they will do with that.
Why does that come about? Well, the enormous quantity of data we fed these machines is basically at the primary level.
It's giving a feature space in which interpolation works.
And all these capabilities like logical reasoning stuff are emergent because that
feature space is emergent by just looking at enormous quantities of our data.
But it like in the final layer of this thing, it's still having to interpolate.
It can't build that feature space and extrapolate in such a way that says how well, how would McCue show actually react?
You know, what would they do, first of all, which you could see it could say something,
but we'll only know what McCue would do when the modern McCue shows get given the tool.
And so at this very key point, given that humans, whatever their limitations, are coming out, this problem from particular perspective,
you can never replace them because as soon as you augment them with the tool,
they are something that is far greater than either themselves or the tool on its own.
Just like we became far greater than ourselves by writing things in clay tablets, you know, for a large amount of time.
That was how we shared knowledge and built upon knowledge and understood geometry and all sorts of other things.
So those models are only interpolated now.
This is something I talk about a lot, and I should not question what I would have said about this a little bit.
Because when people talk about artificial intelligence, a question occurred to me is what do they mean?
Because different people mean different things.
I was at the Natural History Museum in London talking with some of the scientists there the other week and on my way,
and I got chatting to the security guard. And I always like to ask people, what do you think it is?
And it's quite hard to understand. A common thing is behind this because intelligence is such an emotive word.
So they sort of think it's like them. It's like, well, it can't be quite like you.
But I do think want to say to you that this technology might and could do is be the first technology that when it automates,
tries to adapt to who we are or I think that's what it purports to do.
Right. So all previous generations of automation, if you invented a weaving in a room or if you invented a spinning journey,
it required everyone to turn up at the factory and service the machine.
So Samuel Butler. It's about this in the 19th century,
the way in which these forms of automation actually enslave us to the machine because we're adaptable and the machine isn't.
So if you look at railways, you know, you all have to turn up on time station.
You didn't even have universal time across the United Kingdom until they had railways.
So every previous generation of automation requires us as flexible entities to adapt to the machine.
And I think what people expect that they're getting from an AI is an entity that will adapt to them.
I think it's a fallacy, but maybe I'm wrong.
And maybe what we're seeing now is when you have machines that can understand the whole of human history and culture,
they can mimic adaptation to a certain it will feel a bit more.
I don't know if we'll truly feel that in two, three years time. I think people are feeling that today.
I can have a conversation with a machine about my software that feels like it is adapting to who I am and what I like.
But when it comes to then applying that in critical decision making,
so a lot of what we care about in society is what one might think of as consequential decisions being made by people about us and who we are.
I think there's kind of a problem with this point of view because the thing I sort of started noticing,
and I think it's also written about I'm not the first to notice it and people use different words,
but everyone in machine learning talks about we want decisions that are fair.
But when they say that, we want fair decisions. I've noticed that people mean a couple of different things.
And in fact,
it was Karl Rasmussen that he went out for a run once because I was talking about the complexity of a fair decision and how difficult it is.
If I'm going to make a nuanced decision about some individuals in front of me from
different backgrounds who all appear qualified to enter the University of Cambridge,
and I'm taking into account all their different backgrounds, what they've done up until now, etc., etc., because they can all manage the degree.
I'm using an immense amount of my understanding of these individuals as a human being and trying to find out about who they are.
And it seems fair that I should do that because I'm taking into account these individuals
experience of life and the struggles they've had to get through to get there.
But it's another form of fairness that says, no, the rules should be clear. Now.
Basically, you should have to get these grades and then you should be accepted into university.
And the same thing applies for any consequential decision making, like giving loans or, you know, court cases.
And this is now reflected in the General Data Protection regulation,
which says that there are certain protected characteristics and you should not ever decide about someone on the basis of those characteristics.
But what I find interesting about this is it's always like this is political alley.
We decide which side, which politic politics is, errs on which side.
To me, both of these extremes are dystopias. The extreme where all you're doing is considering everyone's history and and if only they be given
the chances and giving everyone the same opportunity is one dystopia and the extreme way you say,
no, this is the fixed rule, and only if you pass this rule do you get to have the beneficial outcome or the negative outcome is another dystopia.
And in practice, we're always operating somewhere between.
And it was sort of struggling to think about this because it's really important.
How can you have something that is procedurally fair and simultaneously nuanced and substantively fair?
Because it's something to be perceived.
To be fair, we all have to understand, like, I don't think the tax code is fair because no one understands the tax code.
So if you pay someone a large amount of money, they can find a hole in the tax code and you can pay less tax.
So that's not procedurally fair, even though it's a process. Right.
And it's not procedure fair because it's difficult to understand. So for something to be perceived fair, it has to be clear to everyone.
But simultaneously, we're going to take into account all these nuances.
How can we have something that is taking into account nuances and also be simple?
Well, the answer goes back to that word.
And folks, if you gather a group of people together, if you convene them really well and teach them how to make decisions,
you can have a decision making process that is simultaneously nuanced and procedurally fair,
because you can have people I mean, the most complex things in this room are the other people in this room.
But we all think we have a deep and intuitive understanding of those people.
I mean, when we do action because we co-evolved together, right? So that goes right to the heart of who we are as humans.
So I call this a marvellous resolution between these two extremes that the only way you
can break these two is to have a procedure where you've got well trained human beings.
And of course, very often we're not well trained with bias or whatever.
If you train us humans well about good decision making and then you also have a procedure that sort of says, yeah,
we can how to get these marks, and then you'll be before a committee, and then that committee will make the final decision.
Everyone says, Sure, that sounds fine if you say deal terms of.
Say. And then the machine looks at your entire history and does really complicated calculations
about what's going on that that's very hard for people to understand that sort of procedure,
because it's a procedure, but one that we don't feel an intuition about unless we're assuming that machine is operating like you.
So for me, this says that for consequential decision making,
in order to have decisions that can balance between these two forms of fairness, we must always have humans in the loop.
But what does that mean? Because we know machines can be a great help in these decisions.
Okay.
So I kind of think that the other side to that and I love these lectures, I listen to them recently, these are the best lectures on air quality here.
And from 2002 and then on Al, because what I, Norah O'Neill is talking about is a question of trust.
And she's talking about a world where everything has become more procedural and we're calling everything to account through processes.
And she's talking about the way that erodes our professional faith in individuals who are paid to make good decisions.
Now, there are clearly things that we all know about professional individuals who have made corrupted decisions that are in the papers all the time.
But what Neil is saying is that by increasing accountability in terms of scoring them and marking them and making measures,
you're actually eroding that professional duty.
And what she talks about a lot is that all rights come with duties and that you can't have rights with duties.
And what you will increasingly here, certainly if you hear this, is lapses again,
is is that that's kind of the way that people talk about things like certainly even in academic politics,
I very often don't hear my colleagues talking about what their duties are towards their students,
but I often hear them talking about what their rights are around their intellectual property.
And it would be good if they were focusing a bit more on what their duties are.
And the point O'Neill is making is you can't have rights without duties now.
I mean, she can't have duties because the machine itself does not participate in the society.
It can't be trusted in the same way. And that's the point I Nora O'Neill makes.
And it's a very strong point that sticks with me,
because the first time she made that point was when I was interviewing her for the Royal Society Report,
and I asked her if we could trust the machine. And she told me very specifically, you couldn't.
But listening to her ideas of why that is the case is really about this is the same thing.
You need humans with a human understanding of what professional duty is to be in the loop around this decision making.
So what does that look like?
So they actually she found a quote that relates to this example that I was using before I even commissioned for the elections.
So again, universities are, I think I could say, on the basis of ability and promise,
but they're supposed to also administration will represent the intent. There's no guarantee that the process meets the target.
And I mean, she doesn't say this.
What I'm trying to say is that the only way you can do that is if you have trusted individuals who are balancing those things as part of that process.
And the only way you can trust those individuals is they're well trained and well convened,
etc., etc., and they're held to account by their position in society.
You can't trust the machine to do that because it doesn't have a position in society.
So what does this mean for in terms of what we're doing with artificial intelligence today?
Well, I kind of think as a result, artificial intelligence could only ever be seen as a tool.
And if anyone is telling you it's a replacement for us, I think that's a ridiculous notion.
That always has to be a ridiculous notion. Apart from the following, what does it mean for it to be a tool?
I mean, I that's I want to talk about is the National Advisory Committee of Aeronautics in Langley Field,
who participated in the Second World War and before that in testing aircraft and when they would test the aircraft.
One of the things they did, a guy called Bob Gilruth wrote, is the handbook on how an aircraft should handle.
So what he did and he's a member, the test pilots,
they had what was called spectrum came out of I don't know which one, but I read a lot about seven come out race 47.
When they got a new plan they went up and flew these plans and they moved the
stick around and the plane did things and pilots would say flies like a blue,
fly like a brick, whatever. But they didn't know what that meant in terms of how they should change the plane to fly that, sir.
So what Bob Gilbert did is he wrote a paper that you can go and read it online somewhere
that talks about how a plane should respond when you put £10 of pressure on the stick.
Right. So if you put £10 of pressure on the left and it's a fighter, it should roll at a certain rate.
If you put £10 of stick pressure on the stick and it's a bomber, it should roll at a slower rate As you approach your stall,
the section JUDDER on the run into the stall to warn the pilot.
He quantified all of this. Right.
So when these pilots were flying planes, they could feel the plane and the plane was communicating in a way that was well understood.
Now that control of a stick. Well, the other thing about nationalise the Commission Aeronautics, it became a founding group of method.
And the people that did this work also designed the crewed capsules, the Mercury capsule, the Gemini capsule, and the Apollo capsule.
The actually indulged in providing that feedback to pilots and the control to pilots on entities that were entirely controlled by computers.
So the Apollo guidance computer was the thing that was firing the thrusters.
But Neil Armstrong is moving the stick. So in that case, you've got this really interesting situation where the.
Neil Armstrong is not in control because the computer's in control, but Neil Armstrong feels like he's in control.
And that's the kind of balance we're looking for, because for the things that Neil Armstrong cares about, he is in control.
And the same is true for the pilot of an A380 today.
When they move that stick,
the plane that's going into a computer and the computer is deciding what to do and sometimes the computer overrules the pilot.
It's also true for flight fighter pilots today where fighter pilots would not be capable of flying, obviously, without the computer systems.
So the notion of control you're looking for is something that's akin to this,
where you want the intent of the human being to be understood and projected and enhanced by the machine.
But the machine itself is doing things that are beyond the capability of any given human.
Now, this is incredibly difficult,
and I find this is a really ironic that the control stick was first developed by the Wright Brothers in 1904 or whenever they flew for their flyer,
and it just went forward and backwards because they could only go up and down.
And within 36 years we characterise that interface.
The interface we're now faced with now is one, two.
Let's just talk to our machines and our ability to characterise that interface between the computer and the human is minimal,
like in terms of how we can be manipulated or how we should present uncertain information or how
we share an understanding of a patient with a machine or how it should share it back is minimal.
We don't have the equivalent of £10 left should lead to this and we urgently need to
get on top of that because so far we've been manipulated in fairly simplistic ways.
Like here's a like button or here's an image you'll like or click on this, or here's a question about We'll never believe what so-and-so looked like.
Now, when you knew them in the 1980s, apparently you will take on that.
That's simplistic.
But now there's all sorts of opportunities for machines to interact with this in this highly complex way that we haven't quantified.
So I kind of think that the main thing that we need is the equivalent of a proving ground where when we're looking at these models,
we can understand how they are being deployed in practice.
So understand the nature of the tool, what's the potential, what it pitfalls.
And that's the way we can build cyber capability.
There are going to be loads of disruptions, but there's loads of things we need to do and loads of ways we can be empowered.
So the question is how do we get this understanding as widespread as possible?
Like I said in the introduction, one of the things that has been enormously inspiration to me is working on point data science solutions in Africa.
So Data Science Africa is a bottom up initiative for capacity building.
They've signed machinery and they are on the African continent. And what I love about it is you're always trying to deploy end to end solutions.
So you don't sit in an office and imagine what a farmer might want or a clinician might want.
You actually go out to the health centre or the field and you don't imagine
what the Ministry of Health might want or Minister of Agriculture might want.
You actually go and talk to the Ministry of Agriculture and you find most things that we think about machine learning are utterly irrelevant and the
most of the challenges that people are genuinely experiencing are not being properly researched because they're all at the interfaces with people.
So the thing we need to urgently do is to get on top of working with those people.
So there's an awful mess. In 2015, when we first set up weeks of talks about how we got in and finally get some key facts on the alarm.
So you'll find all these notes and everything. Another aspect to this is how do people maintain power?
How do people maintain control of the system in a societal way?
Well, actually, all this information is coming from us as individuals.
It's our collective eyes data that is being put into these machines.
And we have rights over that data,
whether they are intellectual property rights or whether they're coming from the general Data Protection regulation.
But one challenge is that there's an asymmetry of power.
So if I want to call up Google and say, Can you stop doing this with my data, they're not going to answer my call.
So the notion of a data trust and there's a whole family of mainstream media is now is an
entity which collectivise these people's data rights and operates on this ecosystem to say,
well, basically you can't do that with these models because we want you to delete our data if you're going to do that.
So there's a lot of work that's been going on. We've got some in this area reports 18 months of their trust initiative, 200 experts,
some 70 or so and exciting free real world data transfer funding as pilot Wonga,
which is a sort of local data trust people around bricks in the definition two of which are associated with medical data,
one with people who opted out for the general practitioners recent data sharing scheme
and another one for the Born in Scotland cohort saying which is just starting out.
The other thing I think is vitally important and this is our funding,
we also have to make funding here from Schmidt futures for a project money for four years now is the accelerated programme for scientific discovery,
where the main focus is to go out to data. Science to go out to scientific domain experts and talk to them about their scientific problems
and try and work out what the solutions they need from a machine learning perspective are.
Because only by understanding the problems they face is it's entirely inspired by the
talk to the farmer in the field approach that the DSA has to take the South Africa.
But working with scientists and we go beyond science to engineering and humanities got project,
as I said in my serology friend to try and understand what is it they're looking at?
How how would they like their lives to be better?
Rather than building a neural network and imposing on people, talking to people about the war, the problems you are facing.
And it's exactly inspired by the fact that nurses aren't getting to spend more time with patients through computers.
They're getting to spend more time with computers. And the reason is because that computer solution is imposed from the centre,
rather than by talking to the nurse about what they need and these new capabilities in terms of our ability to code and communicate with machines,
offer an opportunity to turn that around. And this stuff about there won't be any jobs, there's a lot of jobs doing that.
We are utterly failed to deliver on this for like the last eight years, as we've just seen from what people ask for from the Royal Society poll.
Of course, jobs will be different and of course it will be disruptive.
So the final thing is that we're trying to sort of bring that together with the CAM flagship mission.
And if you read and do feel free, we published this report before we got the mission approved,
because the thing I hate about grant proposals is when they've rejected it and you don't get to do anything with your proposal.
So I said, Well, well, we're going to write a report first, publish it, and then you can decide whether to fund it or not.
Part of the reason for that is because, rightly or wrongly, people look to institutions like Cambridge and Oxford to provide leadership.
So this report brought together a number of these strands of thinking communication across the university.
What people expect to see from our and, you know, we hope other people will copy it,
because what it's trying to do is start from what the societal challenges are at MIT,
that Cambridge is not positioned to solve all societal challenges,
but it's position to be a partner in helping people solve those challenges and try and get people to think about how we contribute in that way.
Because it's up to leading institutions like Oxford and Cambridge and Imperial and UCL and
Sheffield and Manchester to really stop all those coins falling off the side of the step.
So Professor following that is just convening and I love it.
To see what's so cool about this is relatively small amounts of funding.
If you're funding the right people, it tends to be young people in an interdisciplinary way.
You can just see beautiful things happen. I think we had this whole meeting where we just funded these groups to the tune of £10,000,
and I was thinking, Yeah, that's about the cost of a business class flight.
And yet some of these groups had produced like workshops, working papers, discussions that were just thinking in ways that only young people can.
The old people who've been stuck in this area for too long are just not seeing the possibilities.
And so it's really vital. We saw funding at that level.
And with that, I'll just say thanks. I don't have any conclusions other than as the enormous amount of work to do, but we're going to do it together.
And if we do it together, well, I think we can lead to a society that is much better for everyone,
not just the professionals, but also all those people on the edge.
Thank you very much.