[MUSIC]
>> Okay. So, I'll give you
a little bit of an overview what is AI.
You probably all have preconceptions and ideas.
What is AI? You have seen a lot of movies about AI.
You have maybe thoughts like Terminator in your head,
robots, all sorts of things.
I just want to set some of these things right and say,
what is AI and what does it mean for us?
Because it's an up and coming technology.
Actually, it's almost everywhere already,
and it's going to be quite important for all of us.
So, it's useful to know a little bit more about it.
This gentleman is Joseph Weitzenbaum,
and I start with telling you a little story about him.
He was a professor at MIT,
that's the Massachusetts Institute of
Technology in the United States,
one of the most prestigious universities there is,
and he was working in computer science.
In 1966, he programmed the first chatbot.
So, a chatbot is a program that you can converse with.
So, in that day and age,
obviously, there was no speech understanding or detection.
So, you had to type your text.
You could type a line of text,
and the computer would come back with a line of text.
The computer that he uses in the picture here,
that was an IBM 1794.
This whole room was the computer.
It cost about three and a half million dollars back then.
In today's money, that would be something like
$27 million or 22 million pounds.
If you have a smartphone in your pocket,
that will be about 10,000 times more
powerful than all of this equipment that they used back
then.
What he did is he programmed this chatbot in a computer
language.
He wrote a script,
and it was a very simple chatbot,
and he made it to emulate psychotherapist.
So, the trick that he used is that he looked
for keywords in the input text.
So, if you would say something about your mother, for
example,
the computer would come back and say,
"Tell me more about your mother,"
just to keep the conversation going.
If you said something like, "Oh,
I'm feeling really sad," then it would
come back and phrase it as a question,
and tell me why you think you're
feeling sad or something like this.
So, you could keep a conversation going with
these very simple tricks for a long time.
If you step outside of this sort of context,
then it would break apart.
But when he was done,
when he had finished computing it,
then he asked the secretary to come in and have a go.
So, he sat her down at the keyboard,
and she was typing something,
the computer was responding,
and after a few exchanges,
she turned around and said,
"Would you mind leaving the room, please?"
She thought she would having
a private conversation with the computer,
and getting genuine advice from the computer,
and she didn't want him to see that.
He found that whoever he sat in front of the computer,
many people had the same sort of impression that
they were having a genuine conversation with the computer,
and the computer understood how they feel.
Obviously, there was no such thing.
It was a very simple script.
You could look at the code that he had written to program
this machine.
You may have seen in the news just last week,
there was a similar story.
There's an engineer from Google who went
public and said that the chatbot that Google is programming,
called Lambda, that this program is sentient,
that it has feelings,
and that it should have rights,
and that Google should treat it better.
And...
So, it's a similar situation.
Now, we have 60 years in the future.
We have another chatbot and somebody
thinks this program is alive.
It knows what it's talking about.
It actually doesn't.
But it shows that we are very
quick to anthropomorphize things.
So, if something talks like a person,
then we think it's a person.
So, we're very quick to make these decisions.
It's actually quite difficult to find out if something
is sentient or has understanding,
even about our own consciousness.
It's very difficult to figure that out.
So, the question is, how do we do this?
In 1950, Alan Turing was suggesting a test.
So, you would have a conversation with
a computer and a conversation with
a person at the same time just through a typewriter.
So, you couldn't see anybody, couldn't hear anybody.
You then, after the conversation,
had to say who is the person and who is the computer.
If you couldn't make this distinction, then he said,
"Okay, then that is evidence that the machine
can think like a human."
But these examples show
that it's probably not enough to have a test like this
because we're very quick in jumping to
conclusions about if a computer is actually sentient or not.
So, now, let's have a look at how these things work.
So, how can we make a computer
say something that sounds like a person has said it?
Also, how can we make a computer see?
AI is actually everywhere,
and you have it in your smartphone if you have one.
Even if you haven't found that out yet,
but what most modern smartphones can do now,
you can go in your picture collection, for example,
and look for pictures by typing words.
So, this is an example on my phone here.
I just looked for pictures that have ice cream in them.
I've never told my phone
that this is a picture that has ice cream.
These pictures haven't been labeled.
So, by itself, it could see the picture,
figure out, "Okay, that looks like ice cream."
You can talk to computers or your smartphone.
You have something like a personal assistant on it,
or a smart assistant.
If you have an iPhone, it's Alexa.
If you have a Google or an Android phone,
then it's probably Google Assistant or something else,
and you can have conversations with these things.
You can ask it questions.
And here's a funny one with Siri from the iPhone.
It probably doesn't work anymore.
If you try it now, they probably have improved that.
But you can see that sometimes these things don't work.
And also with image recognition, it not always works.
Sometimes they make mistakes.
But why does it work?
So, we have big data.
That's a term for having lots of data.
Typically, so much data that processing it
becomes a problem in itself,
but the internet is full of it.
And we have all helpfully uploaded our pictures
onto the internet, and we have labeled the pictures.
We have put up a picture with a caption.
And this data can be used by something
that's called machine learning,
which takes examples of input and output
that should appear together.
And this can be used to train an AI system to say,
"Okay, if you see this input, please produce this output."
And you just need millions and millions and millions
of these examples to make it work.
And the same thing happens with language.
And we can make this work now
because we also have cheap compute.
Computers have become much cheaper.
You don't even need to buy a massive computer.
If you want to solve a massive computational problem,
you can rent one in the cloud, right?
So, you can go onto the internet
and rent a computer for a day or an hour
and make it do some stuff.
And something like what's called the deep neural network
invented. That is a particular system for machine learning
that
can make use of examples and figure out what output to
produce when it sees a particular input. There's an
illustration at the bottom here. Deep neural network is
actually
inspired by the cells that we have in our brains called
neurons. These cells can either be active or inactive so
they
can be on or off and they influence each other. So if a
cell is on, then the status can be propagated to other
cells,
switch them on or switch them off. And this is simulated in
these computer models. And so here's an illustration. You
just
need to know that the deep neural network has layers of
computational units that do stuff and they're connected to
each other. And if you have an input signal, you can
propagate
it all the way through to the output. And on the output,
you
get something like a label maybe or classification or a
number
that is then supposed to mean something. At BT, we use AI
for
over 20 years. We have always done research in AI. And for
example, we use AI to help our field engineers. We do the
work
schedules through an AI system. We give them augmented
reality
to identify the parts they're working on. We use AI in
cybersecurity. That is we look at the data in our networks
to
figure out if somebody tries to attack the network, place
malware in them. And we use AI for planning networks. If we
want to build a new network somewhere, on a greenfield site,
maybe in a new housing estate, the aim is to use a minimum
amount of cabling and equipment and figuring out what's the
optimal plan for a network is actually quite difficult. If
you
have to do it on pen and paper, it can take months. A
computer
with an AI system can do it in hours. We also have AI
services
for customers. So for example, we have something that's
called
BT Call Protect, and that looks at call patterns and tries
to
identify callers who are actually spam or nuisance
callers. And then somebody checks this when it has been
discovered. And these callers are then redirected to a junk
mailbox so they don't ring at your house. BT is actually
the
UK company that has the largest portfolio of AI patents. So
that
shows you that we have done a lot of work on AI over the
years. It's not magic, it's just math. And it's relatively
simple math. And even if you don't like math, it's not so
difficult to understand what's happening here. All you need
to
do is you need to be able to multiply and to sum and maybe
apply a function to turn a number into something else.
That's all that happens in these deep neural networks. What
you
see here in the middle is just an illustration. So each of
these circles is one of these computational units that can
either be on or off. And the connections between these
units,
they propagate the signals from one unit to the other. And
you
can see they're densely connected. And what one of these
units does, it just looks at all the incoming connections
and
sums up the input. And when the signal travels over a
connection, it is multiplied with a number. And if you want
to break it down what happens there mathematically, it's a
multiplication of a vector through a matrix. I don't know
if you have had this in school yet, you might get it at
some
point. But that's all there is to it. And so how do we make
it
do something interesting? So it can have different types of
inputs. So it could have images, it could have text, it
could
have maybe a position on a gaming board. And this needs to
be transformed into a vector of zeros and ones. You can
imagine
With an image, it's not so difficult.
Images are made of pixels, right?
Each pixel has a color.
So essentially, you can say this picture of this cat there
is a matrix of dots and each dot has a color,
and each color I can express as a number,
and then I can express this number
as a binary code of zeros and ones.
So it's relatively easy to turn an image
into a long string of zeros and ones.
I can do the same thing with words,
if you're interested, you can Google word embedding
and find out how that works.
And you can imagine I can do the same thing with
a game position like a chessboard or a Go board,
by just numbering the fields and the figures,
and then I can turn this into
a representation of zeros and ones.
This is then passed through the network,
and at the end of the network,
it produces an output,
and this output is understood as a label.
So if I put the picture of the cat,
it might come at one output which represents,
this is an image of a cat,
or if I put a part of a sentence in it,
it might complete the sentence by telling me,
okay, the cat sits on the mat.
And when I present it with a position on a gaming board,
it might come up with a move that
I should make in the next step.
And I create these systems through example.
So if we take images,
I show them millions and millions of
images and the labels of these images.
And if it gets the output wrong,
then I can tell it, no, that's wrong.
You should do something else.
And I can generate
an arrow signal that I feed back into
this network the other way and it can then
adjust all the numbers that
are sitting on these connections.
These numbers on the connections are called parameters,
and I come back to this in a minute.
Here's an interesting video.
If you look up Cassie Kozirkoff on YouTube,
she does great talks about AI and
how it works and gives great examples.
And she has an example here where she
trained a neural network to recognize her two cats.
She has two cats, Huxley and Tesla.
And she wanted to build a neural network,
so if she shows a picture of Huxley,
it's recognized as Huxley.
And if she puts a picture of Tesla,
it's recognized as Tesla.
But she made a mistake.
And you can see it circled here in red.
All the pictures where Tesla
appears have a heater in the background.
There's a radiator you can see.
And one unfortunate feature of
these kind of AI systems
that are built from these deep networks is,
they like to cheat.
So they look for things that are very
obvious and they might not be obvious to you.
So if you look at these pictures,
the radiator wouldn't be obvious to you
because you know you're looking for cats.
But of course, the network has no clue
what is a cat, what is the radiator.
It doesn't even know it gets
a picture that it's supposed to classify.
But if there's something reoccurring
in a picture that is very obvious,
then it latches onto that
and uses this to recognize what the input is.
So what Cassie has built here
is actually a radiator detector.
And you have to be really
careful with the data that you use to
build these systems that the data
represents what you want the system to learn.
And that can go badly wrong if you're not careful.
Look up Joy Bullenwien.
She did great work by figuring out that
AI systems that are supposed to
recognizing faces are biased.
That means they are biased against
women and biased against darker skin.
So that means they perform less well when they see
faces from women or from people with.
darker skin and this is because when the systems have been
built,
people haven't taken care to put representative data into
these systems.
They mainly picked faces of white men to train these
systems.
We call this bias and this can be quite harmful.
So, if you have a system that is supposed to identify
people,
but it can't identify women or people with darker skin,
then you have a big problem on your hands.
So, I encourage you to look this up.
She does excellent work.
She's calling herself poet of code.
So, she combines poetry and computer science
and very interesting person to see what she's doing.
Let's have a look at what other stuff we can do with AI
these days.
So, we talked about images and we talked about language or
text.
Here's an example of what you can do when you will create a
simulation in the computer.
Nowadays, this is often called a digital twin.
So, you take something that is real, a physical system or
an environment
and you create a simulation of it in the computer
and you can create these simulations also through machine
learning.
Here we have an example of an artificial world that just
consists of stairs.
I have little robots which are also simulated
and I let them loose in this world of stairs
because I want them to learn to navigate stairs.
So, each of these computers has a machine learning program
running
and as it navigates these stairs, it gets signals.
So, if it does it well, it gets a positive signal.
If it does it badly, gets a negative signal.
And from these simple signals and by trial and error,
it can figure out how not to fall down stairs
and how to navigate stairs properly.
And you can do this on millions of these computers in
parallel in this simulated world.
And when you're done, you can combine all the things that
these robots have learned into one model
and you can load it into a real robot.
It's on the other side of the picture here.
And then this robot can navigate stairs in the real world.
So, this is another trick that AI uses to learn things from
examples,
in this case, a trial and error simulation.
And that's something that we use a lot when we learn stuff
ourselves through trial and error.
But if you remember how you learned riding a bicycle, this
was essentially trial and error.
Nobody told you how to exactly hold the handlebar and how
to exactly pedal.
You figured it out by not falling over.
And that's called trial and error learning or the technical
term is reinforcement learning.
So, you reinforce an action that is good and you discourage
an action that is not good.
This can be pushed so far that you can have simulations of
the real world.
So, here we have a simulation of a highway system
and we have artificial cars driving in this system.
And the simulation is so accurate that you can have
pedestrians, motorcycles, bicycles,
you have trees and lighting and buildings,
everything that you would find in the real world.
And you can use this simulation to train autonomous cars,
which is safer than letting them drive around on real roads.
And this kind of capability based on compute has gotten so
far
that the company NVIDIA aims to have half a million kilometer
of highways simulated in the near future
in which that it can use to train autonomous vehicles.
Another interesting application is climate change.
You all know that we need to do something.
And one way to...
figure out where all the emissions come from is to look
from space and look at
power plants. Not all power plants in all countries report
accurately what the
emissions are. But if you have satellite pictures and you
can look at the
emissions and you do this for power plants where you know
what the actual
emissions are, you can learn how these images relate to
emissions and then you
can track the whole planet for carbon emissions from power
plants. And you can
have other applications like this where you use computer
vision based on AI
systems like discovering where trash is in the ocean or in
rivers. You can even
distinguish different types of trash of plastic or other
stuff. All done by
training up AI models based on images. You can also create.
So you can turn
these things around. So instead of seeing an image and
telling you what it is, you
can tell the system I want an image about teddy bears
mixing sparkling
chemicals as mad scientist in a steampunk style. So this is
an AI system
that has been trained to create images based on text that
you feed in. And it
has come up with this picture based on this text. And that's
quite interesting
and it can also be quite frightening because now we're at a
point that AI
systems can create photorealistic pictures of stuff that
does not exist.
And it can also, you know this maybe as deep fakes, so AI
systems can create
photorealistic pictures of faces that either don't exist or
belong to somebody.
So this is something we need to think about. What can we do
with this
stuff? This is quite funny but you can also do serious
stuff and maybe even
harmful stuff with it. Text. Let's go back to chatbots. The
modern chatbots, you
sometimes hear this word transformer as a particular type
of deep network. And
GPT-3, it stands for generalized pre-trained transformer,
is a particular
type of deep network that has learned to generate text. So
the way that works is
you put some text in and it just continues to create text
in the same
style. So it can spit out endless text and this text looks
like it has been
produced by a person. And this is because, yeah that's what
it has been trained to
do, right? So it takes text from the internet. For example
you can, all of
Wikipedia or all of the news sites are dumped into this
machine learning model
and it then learns the patterns in this text. It learns
what word follows another
word. And you know this from your phone and predictive
texting, right? If you
start typing a text, it tries to figure out what could be
the next word. And it
has learned this from your past texts. And so this is the
same kind of
mechanism, just blown up and made much bigger. This type of
model has 175
billion parameters. So if you remember the little picture I
showed with the
connections between units, each connection has a number. So
this network
has 175 billion of those and these numbers need to be
adjusted and fine
tuned to make this AI model what you wanted to do. And so
you can imagine
there's a lot of compute that has to go into this to make
this happen. So you
need a lot of text examples to fine-tune all of these 175
billion numbers. And if
you had to do it now on a computer, it would cost you
around five million
dollars to do it. And the amount of compute is something
like one year of
GPU time.
And so you need to go parallel with this
to make it in a reasonable time.
And this is how these models are built these days.
And they appear to produce text
that has been written by a person
because they have seen text written by people.
And that's what they reproduce.
There was an article in the Guardian a couple of years ago
where they tried to over-hype and make people afraid of it
because they had an article written by this chatbot.
But what they actually did is they generated eight texts
and edited the best bits into one.
So it wasn't really produced by the machine.
But in the media, you have to be careful what you read.
There's a lot of hype around it.
So they either try to make the AI the savior of mankind
or the destroyer of mankind.
Whose phone is it?
(laughs)
And sometimes it doesn't work.
So here's an example.
So the purple text is produced by the chatbot
and the black text in italics is the input.
So if I put in, you're having a small dinner party,
you want to serve dinner in the living room,
the dining room table is wider than the doorway.
So to get it into the living room, you will have to,
now let's see what the bot says.
The bot says, remove the door.
You have a table saw, so you cut the door in half
and remove the top half.
So that should tell you this thing is not intelligent.
It's just parroting words.
It has learned from text as input.
It can't reason about the physical reality.
It doesn't understand what the door is and the table
and that cutting the top half of the door off
is not gonna help.
And you can find tons of these examples.
All of this will be available to you at some point
on our website.
You can follow these links and read about
how these things work.
Here's another example of what can be done these days.
This is a video conferencing service.
And what this does, it modifies your eyes.
So it makes your eyes look at the camera all the time.
Even if you look down because you're reading something
or typing on your keyboard or you're looking
to the side a little bit.
It corrects the image.
So what you see as the recipient,
you don't see the real person anymore.
You see a created image of the person
that appears as if it's looking at the camera.
The text or the speech that is produced
can be translated in real time into another language.
So you can speak into it in English.
And you can say, I want what comes out of it
to be in Spanish.
And there will be a translation of English to Spanish
in real time.
And it will also modify your lip movements
so that it looks like you're speaking Spanish
and the Spanish comes out in your own voice.
So I call it your fake you.
So what you see is not a transmission anymore
but your camera image is reinterpreted
and recreated as an image on the other side.
And your language is interpreted and recreated
on the other side.
And that might have lots of opportunities
and can become very valuable for communication.
But you have to think about, can I trust it?
How do I know that what comes out at the other end
is still what I'm saying?
There's no transmission anymore.
There's an interpretation happening in the middle.
So we're talking about issues of trust in AI
and we need to understand, can we trust a service like this?
(clears throat)
Let's look in a few other kind of mistakes
that AIs like to make.
I told you that AIs like to cheat.
So when you train them, they look.
for the easy way out, right? They look for something that's
maybe in an image or
in the language that is easy to find and then they latch
onto it. So here is
an AI system that was trained to recognize a disease from x-ray
images
and this map of red squares here shows where the main input
comes from for
this AI system. So where does it get its strongest signals
from? So the more red
you see the more information comes to the AI system and you
can see for some
weird reason it looks into the corners of the image. That's
not what you would
do, right? If you're a radiologist you look in the center,
this is where the lung is
and this is where you want to see something is wrong. So
this system then it
turns out has actually picked up these little markers on
the x-ray images and
it has learned to associate the markers with the prevalence
of a disease
because certain hospitals have more or less likelihood to
see sick people and
the way they mark the x-ray images or even the resolution
of the x-ray images
contains information about where it was taken and this can
be
associated with the likelihood of the disease. So this is
what the AI has gone
for. So it was easier for the AI to guess if there's a
disease or not based on
some artifacts in the picture instead of looking at the
actual x-ray image and
finding signs of the disease. So again that's something
that needs to be checked.
So if somebody has built an AI system in the lab you need
to go out in the real
world and test does it really work. And if we try to figure
out how do these
things see, what sort of information do they take from
images then we can
see they don't see like us. So here's an example. So we
have images and we have a
question about the image and the AI system was trained to
respond to these
questions. So the first one is what is covering the windows.
These are the
blinds obviously and a person would look at the windows and
you can track the
gaze of a person with special glasses and cameras so you
can figure out where
would people look at when you ask them, show them the
picture and ask them what
are the windows covered with. The AI looks elsewhere. The
AI system looks at
the bed and the lamp for some reason and it figures out
from that that blinds are
covering the windows. And the other picture here is a what
is the man doing
playing frisbee and the AI system hasn't even found the frisbee.
That's another
example that shows that AI systems see the world
differently and that means we
have to carefully check what it is that they're actually
doing before we use
them. Actually AI systems are garbage in garbage out. This
is a term that says
when you have a computer program that expects a certain
input but you give it
some garbage then it will produce garbage. And AI systems
are not different
so you can put anything in a system that is supposed to
recognize images it will
come up with an answer it won't say oh I've never seen it I
don't know what that
is. So what we want to do is we want explanations from AI
systems. A lot of
research concentrates on this because we want to be able to
trust them that
there's good science behind it we want to be able to
challenge outcomes and
decisions. We want to know that the system is ethically
unfair. We might be
legally obliged to do that and we also want to learn from
mistakes. So if the AI
system goes wrong we want to understand why that is so we
can avoid it happening
again. And I just want to finish with a few examples of
what I call AI snake oil.
There's so much hype around AI that you get sold products
that are supposedly
doing something clever but they actually don't. And one
example is hiring tools so
that something you will all go through in some
point in the future you will go to job interviews.
Hopefully it will be in person.
But especially during the pandemic, there was a lot of
video interviewing.
And so you sit in front of your webcam and you talk into
the computer.
The video gets recorded and then there are tools that try
to interpret the video.
They try to figure out are you being honest?
Are you being genuine?
Are you being engaged?
What is your kind of emotional status?
And this actually causes a lot of problems.
And these systems are also biased.
And they can be easily tricked and they fail.
So here's an example where we had the same interview.
Once with a white background and once with an artificial
bookcase as the background.
And you can see the scores at the bottom.
The bookshelf gets much higher scores.
So that again tells you that the AI system isn't really
looking at the person.
And can really figure something out about the person.
It's getting signals from the surrounding image.
There's a lot of places where when you work with AI where
things go wrong.
I don't have time to go into the details.
But at every stage of building an AI system you have to
check.
Am I biased?
Do I have the right type of data that I put in there?
Do I really get what I want?
And at BT we have created ourselves a framework.
So we say our technology has to be for good.
So we want to deliver tech that empowers people and
improves their lives.
We know that we are accountable for the technology that we
release.
We have to protect against tech misuse.
We want to be fair so everybody should be treated fairly
and with respect.
And we want to be open about what we do.
So for example open means when you interact with an AI
system.
You should know that you interact with an AI system.
And it shouldn't tell you oh I'm a person.
If you want to try it out there's something called Teachable
Machine.
So if you Google this you find this website.
You can try and train an AI system with your own pictures.
And I want to leave you with a message that AI needs you.
AI is an important development that we see everywhere in
our lives.
And we need everybody to help with it.
And I once had a presentation by Professor Dame Wendy Hall
from University of Southampton.
And she said AI is too important to leave it to men.
And that means that addresses the fact that we do not have
enough women in the field.
So we need women to join the field.
And if you think it's all about math and physics or
technology it is not.
So if you're interested in other areas like if you're
working in humanities and the arts.
You will have ideas that the technologists will never have.
If you're interested in social science and how people
behave.
You will have insights that the technologists never have.
So we need people from all backgrounds from all types of
study and interests to join this field.
Because we need diverse teams. We can't have just techies
develop AI systems.
Because they will not see what can go wrong with these
systems.
They will not really have the insight about the risks and
the kind of situations that these things can get into.
So we need more people that have different perspectives.
And that can help us to build these things in a way that
they really do what we want them to do.
Alright, I hope you found it interesting and I hope you
have some time for questions.
[Music]
you