Hello, I'm Michael Luck from Kings College London.
I'm a Professor of Computer Science,
and I've been working in the area of artificial intelligence for about 30 years.
Now, What's interesting about artificial intelligence
is that although it's much older than me,
it's really only in the last 10 years or so that
it's taken off and reached out into the wider
public sphere,
and it's now affecting all aspects of our daily lives.
For example,
we have Amazon's Alexa and Apple's Siri,
which are just some examples of digital assistants
that many of us are now using
As a matter of course,
we have AI systems that can play games like Go with Deepmind's
AlphaGo and AlphaGo Zero programmes.
We have programmes that solve crosswords and write news articles and also,
perhaps more importantly,
we now have programmes like Deep Mind's AlphaFold,
which can predict how proteins fold into 3D shapes.
This is a really complex process that's fundamental
to understanding the biological
machinery of life.
These AI problems are still very specific and narrow and
typically involve one programme working by itself,
trying to figure out a solution.
In other words,
they're really not concerned with General AI or what's sometimes called
artificial general intelligence or AGI.
And one of the things that's needed for AGI or more generally,
for AI in the real world, is to be able to interact with others.
Then one of the big applications of AI is autonomous vehicles,
getting cars to drive themselves without human control.
And while
that requires some of the kinds of AI I've just mentioned, in
order to get to human like driving capability.
It also requires the car to pay attention to how it interacts with
others, both human and machine.
While there have been some accidents with trials
of autonomous cars where they haven't
recognised pedestrians or cyclists, in general they're pretty effective already
and there are lots of trials taking place around the world.
But even more than just providing autonomous control of a vehicle,
self-driving cars offer the possibility in the future of being able to do
some things that humans can't do so well or as easily, like driving more
efficiently in convoys,
communicating about and managing situations with obstacles and accidents,
or even just organising multiple vehicles better on the roads.
So while this means that we need to ensure a car can operate
autonomously,
it also means that we need to get them to co-operate and to collaborate in
situations where there is a distribution of control and no single
vehicle is in charge.
The same issues are also important when we consider any kind of problem where
machines work together,
whether it's physical interactions like cars in convoy,
swarms of drones working together or robot football,
or whether it's virtual interactions simply in getting multiple different AI
systems working together on complex problems just like we as humans do.
And this also applies to machines working
together with humans in partnerships that help
to improve what people can do by themselves.
Here,
AI plays the role of assistant to people, interacting with us and helping us to
improve what we already know,
reducing mistakes and improving performance in
things like customer service and patient care.
By working with machines in what some call augmented intelligence,
we can better manage the risks of automation, but improve efficiency and keep
the humans' common sense in the loop.
For machines to work with each other and with us,
we need to have confidence in them and to trust them.
AI technologies are increasingly being found all around us,
but there are still many technical challenges,
and there are some serious concerns about the safety in, and trust
in current AI technologies.
For example,
we don't have enough assurance over AI system behaviour, in that because some
of the learning is incredibly complex,
we don't always know what a system will do.
Some AI decisions are not explained to users,
and at times the AI decisions can't even be understood by the
engineers that built the system.
And there have been many examples of machine-learning systems that are distorted by bias
in the data on which they're trained and so give bad results in determining how to
classify an image or who to lend money to or who to recommend for a job.
So the practical development and exploitation of new AI technologies and
applications, requires safe and trusted AI,
where an AI system is considered to be safe when it's possible to provide
some kind of guarantee about its behaviour,
and it's considered to be trusted when it's possible for us humans to have
confidence in the decisions that it makes, and its reasons for doing so.
This means that not only do we need to ensure that the system gets the right
answer or does the right thing,
but it does so in the right way.
So we need to make sure that AI systems give us confidence by being
transparent,
explaining what they do and how their decisions are made, being
clear and explicit about the data sources they have learned from and the
algorithms they used to come to their decisions and also to ensure that they
represent a diversity of perspectives in all of these areas.
Only then will we be fully able to take advantage of the progress that's being
made and see a future not just of AI decisions,
but also of AI interaction and collaboration.