So good afternoon,everybody
and welcome to our knowledge Science Festival at school Live Q&A session all about
Artificial Intelligence.
I'm really pleased to have with me here today to answer your questions,
Professor Detlef Nauck,
from BT and Professor Michael Luck from King's College London. If you've got a question that
you'd like to ask Detlef or Michael,
then please use the Q&A or the chat function in Zoom.
If you click on the button at the bottom of the window and type your question in, we will get around to it,
just what everyone's having a chance to think about their questions.
I'm just going to ask Detlef and Michael you both to introduce yourselves briefly.
So, Michael, would you like to go first?
Hi,
Carol.
Yes,
I'm Michael Luck,
Professor of Computer Science at King's College London and Director of a
Centre for Doctoral Training,
which is to train PhD
students in safe and trusted artificial intelligence.
I've been working in artificial intelligence for the last 30 years,
and I'm really excited to be here today.
Fantastic.
Thank you,
Michael and Detlef,
would you like to introduce yourself?
Yes, thanks, Carol
Hi, everybody.
My name is Detlef Nauck
I work at BT in the Research Department,
and I lead the AI and Data science research area for BT
Fantastic. Thank you. Detlef
So hopefully you've all had a chance to have a look at the videos and other content that we've had today on
the website around artificial intelligence and watch both Michael and Detlef's videos and maybe have a go
at some of the activity packs.
If you haven't,
then they'll still be on the website,
so you can catch those at any time.
The first question I have I'm going to start with you,
Detlef, is what's the most exciting work you've done on AI?
Oh,
interesting.
Yeah,
I haven't yet got,
uh,
had the opportunity to play with drones or anything like this,
but I think for for me it's always exciting to look at a new
problem because it affects people.
So people come to me with the data that they have some how collected
from some work that's going on in BT somewhere.
And then they come and ask,
How can we solve this problem and how can we predict something or find something out?
And this is what I find really exciting and interesting to,
dig into this data and find problems,
solutions,
Fantastic Detlef and the same question to you.
Michael.
What's the most exciting piece of what you've done on AI? Such a hard question to
answer,
and it's really hard to answer because there are different kinds of work that I've
done.
So on the one hand there's this work that's about some really fundamental
questions,
theoretical questions about the nature of intelligence and what it means to have
that in a computer, how to do reasoning.
So those are really,
really interesting and exciting.
But then there are also some very practical questions,
and I'd say that one of the things that I did on the practical side that was
particularly exciting was trying to model using AI
Technologies how aircraft and aeroplane
manufacturers and airlines manage the operation
of their planes when their engines need maintenance over time.
It turns out this is a really expensive thing to do to manage aeroplanes,
and so if your aircraft is on the ground for any period of time,
then it costs you money.
And so the challenge is to make sure we can maximise the time that aircraft in the air.
And that's a really interesting problem that required an agent based simulation,
various other kind of technologies.
And I think that was that was really exciting,
because it was it was so practical and you know that it was going to be applied in
the real world.
Yeah.
Brilliant.
Thank you,
Michael.
So I've got two questions here which I'm going to roll into one because they're on a similar topic.
And that's what are the benefits of AI
and what are the best uses of AI that you've currently
know about, that are out there in the world?
So,
Michael,
I'm going to come to you first on this one.
What are the benefits of AI?
gosh that's that's a huge question.
Um,
I think this is this is the benefits of AI are
I guess as much as your imagination can take you, So they are
they are wide reaching.
They are far reaching,
and they are they are fundamental.
They can make our lives better in so many ways.
So if we think about what computing has done for us over many,
many years.
You know it's fundamentally changed every aspect of our lives.
Artificial intelligence technologies have the power to do so much more.
It's another great step change in terms of the way in which we can use computers because,
unlike simply doing some very well defined,
simple tasks for us,
artificial intelligence can actually move us way beyond to do the things that
we thought would not be automatable, by computer alone.
That would require people to be involved.
And so it frees us up to to be able to do more creative and more interesting
things.
And I think that's the benefits are just just,
you know,
immense you examples might be.
Recently,
Deepmind was talking about trying to understand the human genome and
predicting the structure and function that there is different organisms.
And that's that's a leap that we have been trying to solve
with computers as well for many,
many years that AI has fundamentally changed the game with that and the impact of that in
terms of the health benefits will be immense,
and it's not just in that domain,
but some lots of others,
and Carol,
there was a second part. which I have forgotten as I have been talking so long
The second part of the question was,
What are the best uses of AI?
Excuse me,
What are the best uses of AI that are out there in the world that you know of?
So you know,
it's really hard to say there's one thing that's better than another,
because I think it affects us all in different ways.
But if I just get back to what I said a moment ago,
I think some of the best uses of AI are where we can
learn more than we could before.
So the example that I had of trying to understand,
the structure and function of particular kinds of
genetic sequences, that is something that I think
is so exciting and so huge
that in some way it expands our knowledge.
It allows us to do things that we could not do before and really has the
potential to make to make the world a better place.
So for me,
that's the best use of AI, at the cutting edge as well.
Brilliant.
Thanks,
Michael and left the same question to you.
What do you think the key benefits are or what are the best uses that you've seen.
Yeah,
I think Michael has touched on all of that.
So I agree with him.
I think the the main benefit is that AI allows us to make
decisions in areas where we couldn't make decisions before, that was really difficult
and takes a lot of time and training and learning and studying
in order to get to a level where we can make decisions.
And now we can use computers to help us so and that and
means that in areas where we have just a small number of experts,
that we can help these people to make more decisions and
that benefits all of us examples for me.
I always look to the area of medicine,
So if we can get AI to help us interpret signs
of illnesses and things like Xrays or that kind of imagery
that frees up the time of the experts who do this currently manually and have
to look at images one by one,
that means we can increase the throughput.
We can diagnose more patients and that's in the end beneficial for
all of us.
and,
the kind of really cutting edge things again Michael touched on
is being able to do research in the computer.
So having programmes that help us to do science in
the space of discovering beneficial drugs or
how the genome works,
this is something that we we weren't able to do just
a short while ago.
So I think this is where we can see a lot of benefits coming our way.
Brilliant.
Thank you.
So we've just had some heard about the benefits,
the good things about AI.
So I'm going to flip it with this next question,
which is another great question.
And that is,
can AI be dangerous?
Detlef,
I'm going to come to you first on that one.
Unfortunately,
anything that can be used beneficially can also be used in a in a
dangerous way.
And it's not different to any other kind of complex technical
system that can be abused or where we may not be careful enough,
and we don't fully understand how it works,
and it doesn't work the way we expect.
And we have seen a few examples of where AI has gone wrong,
and one example would be essential image recognition where we know that
these systems that are currently available gender biassed and,
race biassed and this is because,
when these systems were built,
we didn't really fully appreciate how they used the data they are trained with.
We didn't fully appreciate how the data was selected.
They were trained with.
We're beginning to understand this much better.
So that we are can put procedures in place to make sure that we have
a handle on how beneficial or dangerous and AI system would be.
And this is really important that we get to grips with this situation.
Brilliant.
Thank you,
Detlef.
Michel,
can I ask you the same question?
Can AI be dangerous?
Yes.
So of course I agree with Detlef.
But let me say a little bit more about it,
if I may.
Any technology has to be used well and
in one way of understanding it,
there's no difference between AI and any other technology except
that,
there are some interesting reasons why AI in particular has some challenges.
So the techniques that we're using now to do lots of really exciting things in many cases
are not different to the techniques that we had maybe 10,
15 years ago.
What's different is first,
we've got an awful lot more data.
So we were able to train our systems on masses and masses and masses
of data,
and that's one of the first important difference.
The second,
the thing that allows us to be able to make the inferences from that data is that we've got a huge
amount of computing power,
and so that combination of data and computing power
changes the nature of what's possible.
So Detlef was talking about making sure that we avoid bias.
And the difficulty is that we've got all these systems have been built on data that's been accumulated
for different purposes,
perhaps,
and used for other purposes,
and actually we sometimes might be making the wrong kind of decision.
So the danger is really and how things are applied in
understanding what's going on,
making sure that we don't do things inadvertently that we didn't want
to do.
Now,
of course,
there are with any technology you can you can you can put it to bad purposes,
and there are bad people in the world who will do bad things.
But I think what we're really talking about is not the malicious intentions of people,
but things that can happen without our intending to.
And that's really because of the data that we're training these systems on and the vast
possibilities that they might be useful.
And I think that's where there's a real,
a real need for us to pay some attention to the ethics of these systems,
how we use them and making sure we do things the right way going forward.
Brilliant.
Thank you,
Michael.
And I've got a couple of questions around definitions.
So I'm going to I'm going to go with one each to each of you.
So Detlef
Can you tell us what's the difference between AI and machine learning?
Mm.
Okay,
well,
the machine learning is a technique that is used in AI or
artificial intelligence.
So machine learning means that you have an algorithm that you haven't
built through instructions and have improved through instructions,
but that the algorithm becomes better at what it does,
by seeing more and more data that instructs it what to do.
So you could almost say you are programming the machines data instead of,
with instructions that you would normally write
And so machine learning is this idea that you don't have to understand how
to solve the problem.
You can just show the machine examples of how the problem has been solved,
and it figures out by itself what the solution is and how do you
create a solution?
And AI is,
the idea of that machine can do something that normally only only humans can
perform.
Um uses machine learning to achieve just that.
So in that sense,
machine learning is a part of artificial intelligence.
There's a lot of artificial intelligence that is not machine learning that is based on other
techniques like search and optimisation.
And so AI is the wider field and machine learning is part of it.
That's lovely.
Thank you,
Detlef and Michael one for you.
Now what's the difference between strong AI and weak AI
well,
so strong AI is sometimes known as artificial general
intelligence,
and that's the idea that what we want is a
computer, machines that essentially behaves like we do, that can essentially replace
us that can interact with the world in the ways that we do.
So there's something called the Turing test,
which is a test devised by the British computing pioneer Alan Turing.
And the Turing test was essentially the idea that if you have a if you have a machine
behind a curtain and a human behind a curtain,
can some human judges distinguish between the machine and the human if they're just
receiving type written messages?
If the machine can deceive the judges sufficiently so that
they can't tell the difference,
then you've got something that passes the Turing test.
And possibly you've got something that satisfies our need for artificial general
intelligence. A machine that can perform in all kinds of scenarios in all kinds of situations.
And we can't really distinguish from human intelligence.
The difficulty is that most AI today is actually weak AI
It's narrow.
It's focused on specific tasks.
So it's really quite difficult to solve the Turing test or to get to that very
general intelligence.
And we've got lots of machines that are incredibly powerful.
Lots of programmes that will do really good things for us.
But the learning is that one thing.
So one example is,
the game of go, another Deepmind example
A few years back
deepmind builder built a programme that defeated one of the world's
masters of go play.
And what was interesting about that particular examples that it was thought that go with
such a complicated game.
It would take years and years for us to be able to beat a human with a
machine,
but Deepmind managed to do it.
That programme could only solve that particular game and
only when it was in that particular shape,
that particular number that grid.
So 19 by 19,
if they changed it to be bigger,
it wouldn't have worked.
If they changed it to be smaller,
it wouldn't have worked.
And that's the nature of narrow AI.
That's the nature of weak AI,
which is really good at solving specific problems.
Not too good at kind of replacing us in a general sense in the world.
Brilliant.
That's great.
Thank you,
Michael.
Thank you.
Thank you both for those answers.
Now there's a question that's come in,
which I think is a really fantastic question that we have to ask now and that is could
AI
I'll try that again.
Could AI ever be used to mark my homework.
Michael,
I'm going to ask you to answer that one.
That's a really interesting question.
Actually,
I think AI is already being used to mark homework.
I think there are bits of software that will do that,
but it depends what the homework is.
Of course,
there are programmes now that will read text and try and interpret it and work out what to
do.
And there are programmes that are being used in law to trawl through legal
texts and to try to retrieve certain bits of information.
I have to say,
from many,
many years ago,
I was using not AI software but software to mark
programming assignments in universities,
and I think it's not.
It's not too far,
but it depends what the task is.
So marking homework in very well defined in particular ways is
okay.
Getting the rich creativity or the rich insight or the rich understanding that you might
want from a human.
Uh,
it's not where we're yet at to be able to deal with marking homework,
so I think it's possible to do some things now.
Even today I think will be possible to do more in the future.
But I think it depends on what marking the homework is really about.
Is it about spelling is it about punctuation, is it about the deep insights in the essay.
Is it about marking mathematics?
And I think it depends,
but yes,
absolutely.
Some of it can be done today,
and some of it in the future.
Fantastic.
Thank you.
Thank you,
Michael.
Detlef
This is this is a question and for you and that is was AI used to help with anything at BT during the
recent lockdowns.
We are using AI in BT for almost 25 years now.
And so a lot of our internal operations rely on AI programs
So one example would be,
software that manages our mobile workforce.
So they when an engineer has to go to see a customer to instal a new broadband
line,
for example,
then you can imagine that we have millions of customers and we get thousands of orders a
day and we have to make sure that the right person turns up at the right job at the
right time.
And this complicated organisation and scheduling of people
and jobs is done by an AI system,
but this we have in place for a long time.
But during the pandemic,
we actually used AI techniques to look at anonymous data
from from our network to see what is the behaviour
of people.
Do they stay at home?
Do they go and,
still use public transport and so on?
Just to understand how the lockdown affected the
behaviour of people and how that could help in planning what to do next?
Brilliant.
Thank you.
That's really interesting.
So 25 years,
that's that's I think many people might think that AI is a relatively new thing,
So to hear it's being used for 25 years might be it might be a bit of a surprise.
I'm gonna move slightly to some questions that we've had around getting jobs in AI
and the kinds of subjects you might like wanting to study.
So if I wanted to get a job in AI or work in AI in the future,
what sort of subjects might I need to study?
Michael,
Can I ask you that one first?
Yes,
so it's a really interesting question because the research industry questions
because one can understand it in so many different ways.
But if the question is about if people want to get involved in developing AI
Technologies,
then I think the kinds of background you want science,
scientific background.
So mathematics is always good.
Computer science is always good.
Physics. Those things are the things that underpin what's going on.
So if you think about the kinds of techniques that we have today with,
Siri, Alexa,
for example,
that are doing voice recognition or if you talk about things about the facial recognition programmes that Detlef was
talking about,
these techniques that are being used are typically statistical techniques,
so they have lots of mathematics underneath it,
and to understand that is really helpful.
But that's a very narrow view of what it means to work in AI,
and they don't just want to restrict it to that,
because the truth is that,
you know,
I work at King's College,
London and at King's College,
London.
We have people working in AI not just in our computer science department,
but also in our law department,
where they're thinking about the legal implications and the responsibility of the liability of AI
systems.
We have people working in digital humanities where they're trying to understand the social
implications,
and the creativity is to surround AI.
We have people working in the health medicine where they're trying to apply
AI in various,
their main personalised medicine for some of the things that you and I were talking about earlier.
We have people in philosophy who are trying to understand what AI means and
what the what the philosophical implications are.
So in some way.
It depends on what the question is.
But the traditional sense and the one that I come from is scientific
background,
computer science,
mathematics.
Those are the things that underpin what I tend to do and
how people develop these AI systems.
But it's really important to understand that working in AI is no longer restricted to
just that narrow scientific sense.
There are many more aspects that we must pay attention to,
and this ties back to the conversation we had a little while ago around ethics.
We do need to pay attention to what happens to these systems in the real world.
How do they work?
How do people interact with them.
Uh,
and how do we make sure we get we get the right things happen.
Fantastic.
Thank you,
Michel.
Detlef
I'm going to sort of ask a kind of a similar question to you,
which is kind of building on from that around,
what to study.
But if I wanted to learn more about AI at home,
how would how would I go about doing that?
Are there perhaps some some good websites to to look at, some good books to read,
maybe some equipment I could get to have a go at doing things with?
Yes,
Um,
there's a lot of material out there that you can find,
and I actually point out to some of it in in my presentation there
at the end,
there's a recommendation.
You can go on a website and try to build your own,
AI model through machine learning.
And so you'll find a lot of free software
that allows you to try out machine learning and experiment
with AI.
There are tonnes of books out there that give introductions to AI, one that I
particularly like.
It's called Hello World from,
uh,
written by Hannah Fry.
So I would recommend this if you want to,
read a little bit about what AI does and doesn't do,
and it's a good introduction to the field.
Fantastic.
Thank you.
And can I ask you both?
The next question.
How did you come to have the role you're doing now?
What's your sort of?
What does your career history look like?
Um,
Detlef
We'll go with you first.
Okay,
So I studied computer science after doing my A
levels back in Germany.
And while studying computer science,
I came across the field of neural networks.
It was,
towards the end of the 1980's,
when the field was,
booming for the first time.
And in my thesis,
I actually developed a neural network software programme that could learn from
data.
And this is how I got hooked for the first time in this kind of field.
And then I did my PhD in the same field around data
science and machine learning,
and initially wanted to have an academic career like Michael's.
I stayed university for a bit,
but then had the opportunity to come and visit BT for an extended
fellowship of six weeks and which then got me
interested in the research work that BT is doing because
kind of related to what I did at university.
But on the other end,
it's very applied.
So you get to work with people in the business who have real problems that need to be solved
and that always,
fascinated me.
And I enjoy helping people to find solutions.
So this is when I joined BT and this was over 20 years ago and I stuck
around. Fantastic.
Thank you,
Detlef.
Michael,
the same question to you. Like that Detlef
I studied computer science at university,
and this was about 30 years ago when it was still quite a new subject,
quite a new discipline.
and in my final year undergraduate project,
I was trying to work out what I wanted to do.
And I was talking to my personal tutor and he constructed a project
for me because I said I didn't want to do these other things.
They're kind of boring.
And he constructed an interesting project that was challenging in the sense of trying to solve the problem,
not necessarily in the programming aspect and the problem was solving a puzzle,
how to get a machine to solve a particular kind of puzzle.
And I was intrigued by this.
So I did my undergraduate project and I finished my degree.
And then I won a scholarship to study in the United States,
and I was able there to take a class that allowed me to do individual research
And I carried on with this idea. I carried on investigating and exploring and trying to understand more
about what could be done.
And I discovered so much more in that time.
And I came back after a year, to study for a
PhD, doing the same thing, broader,
bigger but essentially the same thing because this problem is so interesting to me.
And the generalisation of the problem was scientific discovery.
How do people do scientific discovery and how can we get machines
to do scientific discovery?
And so that was Detlef
talked about the relationship between AI and machine learning.
Well,
this was a very small sub area within machine learning at the time,
and,
it was really interesting,
and so I kind of worked on that and the idea behind what I was doing was
to think about,
not just the process of discovery,
but also what's in the mind.
What's the motivation of the person or the machine who's doing that
work and what are they trying to use it for?
Because what they're trying to use it for,
My might bias the process in different ways and that idea
of an autonomous agent trying to decide what to do and how to do it let me onto a
path for the next 20 years that was concerned with autonomous agents and
multi agent systems.
How you get computers to work by themselves,
to take control,
to decide what to do and also how they work with other computers,
how they how you get these things to work together.
And that's really the area that I've been working in for the last 20, 25 years or so.
Fantastic,
fantastic.
So you two both kind of started out in the same way and went in slightly different directions.
Detlef, next question I'm going to ask to you,
and that's what's the most popular programming language used in AI.
That's an interesting question.
I would say at the moment it is probably python
there used to be a kind of,
struggle between prison and the language called R,
which is,
they're both offering you free open source libraries
that you can use for data analysis and machine learning.
It looks like prisons becoming more popular,
But there's also Java is still around,
which has a lot of libraries that offer the
ability to do machine learning and other AI techniques.
So,
if it comes to the manipulation of data and,
doing an analysis on it,
not just building AI systems that aew complex like complex like Alexa or
things like that.
And I would say it's a python and R,
And if you go into building applications that have to support
a lot of users and probably Java. Brilliant.
Thank you,
thank you.
And Michael,
I'm going to ask this next one to you,
and that is,
could we ever teach an AI system to be creative?
That's a really good question.
So it's a really live debate.
There are people who work in the area of AI and creativity,
and they come from different,
different disciplines.
So there are computer scientists.
There are AI researchers.
But there are also others coming from philosophy and in other areas.
There are fields of study where they
focus on exactly this area.
So many people will say by default.
Absolutely,
of course,
because we already have,
computer artists,
robot artists.
We have robot composers.
There's something called I think it's the Digital fool if you if you want to look it up,
which I think a robot artist
There are lots of examples of creative AI programmes already,
and so the answer to the question on the one hand is Yes.
On the other hand,
there's a really interesting question about what it means to be creative
and whether machines can ever have that.
I think it's a really interesting debate.
I've spoken with lots of people about this and the answers.
I mean scientists,
experts.
The answers are always very different. From my perspective,
and I believe absolutely this possible for machines to be creative,
no question.
But I've got I've got colleagues in my department at King's who say to me No way,
it's not possible,
so I think it's really,
really tough.
Tough question to answer.
And we have no consensus about this at the moment,
but really interesting to try to see if we can get creative machines.
Fantastic.
Thank you.
Now I'm seeing a lot of questions coming in around autonomous vehicles.
So I'm going to try and roll them up into one big question,
which are,
you know,
when do you think we might see autonomous vehicles on the roads in the UK?
if people wanted to learn a bit more about autonomous vehicles,
are there any projects or things they could read up on?
and also,
if we had this is what I particularly like.
If an autonomous vehicle was trained properly,
could we stop traffic jams?
Detlef,
I'm going to come to you first on that one.
Okay,
there's a lot in there.
so it's pretty difficult to say when we might see the first autonomous cars in the UK.
Because you have to change traffic laws for this.
And it's,
it's still kind of difficult for these kind of autonomous cars to drive in
really complex traffic and narrow roads.
So it's probably more difficult to,
drive around in inner London,
Then it would be to drive from the M25.
So if roads are relatively big and straight forward
to to negotiate we could probably put an autonomous car
onto a UK Road today.
But I don't think it is legal right now.
As I said the conditions have to be changed.
For this.
You might see it first in situations where,
the traffic is ready to definitely straightforward.
And the problem is the mixing of autonomous and human driven
cars.
And there are jokes about this.
You can easily outmanoeuvre an autonomous car and make it stop on the road
if you know how.
Because they have to be really,
really careful how they negotiate traffic.
So I think we'll mainly see,
issues around solving the legal problems and the organisational problems of mixing autonomous
traffic and non autonomous traffic.
If you want to know more about this,
then there are a lot of websites again.
Almost every car manufacturer has a programme around autonomous cars,
which you can look up.
famously,
Google and uber run
large research programmes about autonomous driving,
which you can look up and you'll find tonnes of material there to study.
Fantastic,
thanks Detlef.
Michael,
do you have any views on autonomous vehicles?
So a lot of the people involved in the car industry,
several years ago had predicted that we would have autonomous vehicles on the roads
already,
and as time has passed,
they've kind of had to change what they've been saying for exactly the reasons that Detlef is talking about so that as often happens in these areas,
the timeline gets further away.
The closer you get to what the prediction was,
I don't know when we'll get there.
I think there are challenges.
I think one of the difficulties that we hold autonomous vehicles to a much higher
standard than we do human drivers,
and I think that's as it should be.
It's important,
but we just need to understand why some of those some of those things might be in place.
But as Detlef says,
one of the big difficulties is,
is people the fact that you've got people driving on the road and makes it much more difficult
for for autonomous vehicles to do their thing so if I can switch to your
to the last part of your question,
which was,
will we be able to get rid of traffic on the roads?
Well,
if we had autonomous vehicles,
only then the possibilities are really interesting because you can have,
for example,
certainly on the motorways.
You can have cars driving in convoy.
You can find many,
much more efficient ways to get cars to go through a
particular channel,
particular motorway and so on.
And that's really interesting.
But what's what starts to get more interesting is what that will allow because once you get a car on the
road and if you think about it in the Converse,
then it's going to have to talk to some of the other cars on the road,
and they're going to have to negotiate and do some interesting things.
So autonomous vehicles on the road is one question.
Okay,
and hopefully that will happen soon in a way that is safe and reliable and gives
us the assurance that we need to be happy that they will be there,
but autonomous vehicles where they can do much more than just drive by
themselves and give us some real benefits of being on the road
like driving in convoy,
like negotiating and discussing things with each other.
That's going to be much further away,
and there are many more challenges.
But I think that is where the real benefits are going to come.
And that's really quite exciting.
Fantastic.
Thank you both.
So I'm now going to give you a little bit of a challenge.
So you've just got both of you.
You've just got into a lift with somebody who's a nontechnical person,
so you only have them for a very short period of time.
How would you describe AI to that nontechnical person?
Michael,
I'm going to go with you first.
So you've got so many hard questions,
Carol.
Uh so for me,
the definition of AI,
if you like,
is,
it's getting a computer to do something that
if a human did it,
we would think it required intelligence.
And the reason I say it like that is because what's happened over the years is we
thought there have been particular problems that have required AI of required intelligence,
and then we solved them.
And once we've solved them,
we've kind of looked and said,
Actually,
that didn't really require intelligence at all.
intelligence is required for a much more complicated problem,
and so it's a really hard thing to pin down.
And it's really important because,
the idea of what it is determines how we create regulations
or laws,
how we kind of organise things on the road and so on.
It determines lots and lots of things.
And so,
actually what's more interesting is not the definition of artificial intelligence.
But perhaps something that's more interesting is algorithmic decision making
where we get computers to take decisions for us,
and that's a much more precise term.
So AI is,
is something that's become very vague and very nebulous.
But for me,
it's something that if the machine does it sorry if a human did it,
we would have said it would have required intelligence.
Fantastic.
Thank you,
Michael. Detlef
Would you say anything different?
We have exactly the same definition because we had actually had to think about it in BT
because what we want to do is we want to list all the AI programmes that are in
use in the company,
so we can properly manage them.
And you find now that AI comes into the company in different channels and
attached to some software that previously didn't have AI capabilities.
So how do you explain to people that use these programmes and the
software what AI is so that they know that they should list the use of this
tool in some register?
So we used exactly the same definition and we said by
artificial intelligence is the ability of the machine to display
intelligent behaviour.
And then we say they're intelligent.
We mean it is an activity that we normally would expect a person human to
perform.
And by this we mean,
for example,
reasoning,
speech,
vision,
these kind of things.
And but the machine performs this by analysing and adapting to data about
its environment.
This is what we say,
what AI is.
And then we try to make it a little bit more technical,
so that people who read descriptions about what some software does can
identify it.
And so we consider a system or some software to be
AI if it uses any form of machine learning, some form of
logical or heuristic reasoning or some probably form of
probabilistic reasoning.
Fantastic.
Thank you.
Thank you both,
and and and just going to give a quick plug there for our activity pack,
which is on the website where Detlef and Michael's videos were today.
If you would like to have a go at making your own AI system,
you can have a go at making an algorithm to recognise cats,
which which might sound like a very simple thing.
But I think if you if you guys have a go at,
you'll find it not perhaps as straightforward as it first seems,
so another one of the many great questions that we've got coming and I don't think we're gonna manage to get all of them,
all of them today.
But this one is a really interesting one.
Do you think that AI could ever be taught to truly work without bias?
Michael,
I'm going to come to you on that one.
Gosh,
um,
uh,
Well,
look,
here's the difficulty.
I think there's always bias of some kind in data.
So,
it's not clear what an unbiased system would be.
It's not clear what an unbiased human would be.
I think what we need to do is to try to make sure that we are
transparent about what's happening.
So I don't think we can get rid of bias.
But I think we can be transparent and show what the data is that we are basing
a reasoning algorithm on,
how the decision has been made so that we explain it and trace through what's going on so that
we are clear and transparent about these things.
But to remove bias,
I think,
is almost impossible because biases in some sense perspective and it's sort of
in the eye of the beholder.
It's really important that we understand what things are in the
data that can lead to perverse outcomes and unrepresentative
outcomes,
which I think is a critical point.
But the way to solve that and the way to address that is through transparency,
through transparency,
through explainability to make sure that we are absolutely clear about what's happening in the reasoning.
Brilliant.
Thank you,
Michael.
and Detlef I'm going to ask you a slightly different question just to try and cover some more questions because you've talked a
lot about how AI is being used in BT.
Are there any jobs that you've found that perhaps AI is not the best thing to use
for,
um and if so,
what are they,
for jobs?
Well,
there's a lot of,
discussion around Will I lead to the loss of jobs,
and then you find some interesting,
relationships.
So,
for example,
jobs that involve person to personal relationships,
like serving coffee or talking to people.
What,
questions?
They have trying to solve problems together.
This is not something that AI can do.
Yes,
when I give presentations,
I sometimes ask the audience if you have a pilot on a plane and you have
the cabin crew on the plane,
who do you think will be replaced by AI first?
And it is obviously the pilot.
It's not the cabin crew,
right?
You know that planes can fly through autopilots,
and actually,
we could fly planes completely without pilots,
start and land them without pilots just that nobody would get on board because you wouldn't trust it.
But the cabin crew you couldn't replace you can't imagine a robot
rolling through the aisles
trying to serve your coffee.
I'd like to see what that would look like.
But this is areas where AI won't be seen
for for a long,
long while.
Brilliant.
Thank you.
So we're coming very much towards the end of our session today,
and I just want to ask you both.
One more question.
it's a quite a short one.
And that is what advice would you give your younger self?
Michael,
I'm going to come to you first.
What advice would I give my younger self in relation to AI,
uh Oh,
gosh.
Oh,
that's so hard.
I think I would say I believe that it can be solved.
When I was younger,
there were so many countless problems in the field of AI was much younger,
and it was it was very immature.
And now we have just changed the world.
The world is a very different place because of what AI has done.
So I would say,
believe that it can be solved and going to all that.
Fantastic.
Thank you,
Michael.
and Detlef
What advice would you give to a young Detlef?
Yeah,
I would have said.
Learn more statistics earlier that would have helped me and otherwise stick with it.
Brilliant.
So it sounds like perseverance is that is an important,
an important skill in them in the world of AI.
So thank you both very much and thank you to everyone who's who's been sending their questions into
us for today's chat.
we are back tomorrow at the same time.
Tomorrow is Optical fibre Day.
So if you'd like to come and ask all the questions you want to ask about optical fibre,
we'll have some of our experts answering those tomorrow.
and there'll be some content,
of course,
going live showing some some interesting videos from BT and also from a company called Corning and
again,
some ideas for some activities that you might like to try at home.
I just want to finish off by saying a huge thank you to Michael and to debt left for giving up their time today and to
answer all your questions.
Hopefully,
hopefully you don't feel too scarred,
guys,
because there were some pretty tough ones there,
Um and finally also huge thank you to everyone for joining us and sending in your
questions.
And finally,
a very big thank you to our friends at West Suffolk College who has been hosting the Webinar.
So thank you very much and enjoy the rest of your day.