My name is Emma Hart, I'm a professor at
Edinburgh Napier University where I lead
a large center called the Center for
Algorithms, Visualization and Evolving
Systems, and within that a particular
group that's focused on nature-inspired
computing.
So for me, AI is a really broad topic
that covers anything, basically, where a
system is solving a kind of task that a
human might find complex.
So that can
cover anything from optimization, where
you want to find the best route for a
delivery vehicle to travel in order to
minimize carbon emissions, that, say, to
work in robotics, to work in natural
language, to work in and play in computer
games...so I think it's a really broad
topic.
So mainly my work is obviously focused by
by looking at nature and trying to
really understand what are the
high-level concepts that we observe in
natural systems that we'd like to
replicate in artificial systems.
So
my work has been really guided by two
things: so, work in natural evolution,
but also work in artificial immune
systems, which is taking inspiration from
the natural immune system because both
of these systems exhibit the kind of
high level properties we'd like
computational systems to exhibit, such as
responding to change, responding to
unknown situations, being able to adapt
very quickly to changing environments.
So for me, lifelong learning is probably
the most important aspect of
artificial intelligence that we need to
be focusing on today.
So in the past
we've worked very much with systems that
we train in some manner to do a task
very well - so maybe it's a robot to do a
task, or maybe as a system to play,
let's say, alphago, and then we just
filled that system, we just get it to do
the job we wanted it to do, and we don't
do any further training.
But it's not how
people work; we're always learning, we're
always making use of past knowledge to
do things better in the future and be
able to adapt that knowledge to new
situations.
So for me I think we should
start to build systems that never stop
learning; we just don't put them out
there to do a job, they have to continue
to learn, and I think there's really two
really important aspects for that.
So
I call these adaptation and innovation:
so the first is systems should adapt to
small changes in their environment, so as
they get more experience or as things
start to change around them, they should
make small changes to themselves in
order to do whatever it is they're that
we as humans want them to do better.
But
sometimes the environment changes so
much that the kind of current method of
doing something isn't sufficient, and
then the systems need to be able to
innovate, so they need to be able to kind
of invent - or come up with by themselves -
completely new ways of doing the tasks
that they're supposed to do.
So I think we're starting to see methods
to do this with automated design of
algorithms and design of software, but I
think both of these aspects - the
adaptation and the innovation - are really
key to getting a system to learn over
very long periods of time.
So I think evolution is a perfect
example of a mechanism and nature that
provides adaptation, and again it
provides adaptation on essentially two
timescales: so it can provide very fast
adaptation to deal again with
small changes, but as we know, like, over
generations and long periods of time it
also provides adaptation to, to bigger
changes in the environment, and I guess
we can also see it as solving if we
think about it from an optimization
perspective.
Solving a search problem,
evolution has to find the combination of
genes that gives rise to some organism
that is fit for an environment, and so
in the same way we can model that in
computational systems to try and sort
through vast combinations of
possibilities, to make a solution to give
us the best one possible.
So for me it's bringing it back to this
idea of lifelong learning and building
systems that never stop learning, not,
just, so we've come a long way especially
in the last few years I guess everybody
knows about, they had answers and deep
neural networks and things like that.
They're allowing us to use vast amounts
of data to do things like play games,
natural language type things, but for me,
that, that's only a small part of the
problem, I guess.
I think what we need
from evolution is systems that don't
need vast amounts of data to learn, but,
you know, if we're gonna, for example, take
a swarm of robots and put them on an
unknown planet and want them to explore
it, we don't know what the environment's
going to be like, we can't train that
system with vast amounts of data because
we don't have it.
So I think we need
evolutionary computing to be looking at
these applications where the system has
to learn very quickly by itself with
very little data and very few
examples, and maybe it's got to learn
very fast in order to to survive and
complete its tasks.