Today, everybody's talking about artificial intelligence,
in the field of the car industry,
in the health industry you hear things about artificial intelligence.
And this is supposed to produce things, incredible things nobody
ever thought of before. But how is this possible?
Machines are programmed by humans after all.
Hello I'm Luc de Brabandère, I'm teaching the philosophy of science in various universities,
and today I'm with Mérouane Debbah.
Mérouane is director of the Huawei lab in Paris completely dedicated to algorithms and mathematics.
Mérouane has been studying applied mathematics
for many, many years, so it's interesting to know, in the end:
What do we mean by artificial intelligence?
To explain this, let me give you a very simple example.
Suppose you want to kick a ball at a given distance D, say 450 metres.
In this case, you have two options. The first option is that you have at your disposal
what we call the Newton equations. In this case, you solve those equations and
you're able to find the angle with which you can kick a ball at a given distance,
here 450 metres. It turns out that, in many cases, especially in
telecommunication networks, it's extremely difficult to get those Newton equations
which explain the behaviour of the network. The other option which is
considered today by the machine learning community is around the topic of big data.
What you do in this case is that you kick the ball many times, and you
start registering in a table the distance versus the angle with which you kicked the ball.
By having those huge data and numbers, next time you're asked to
kick the ball at a given distance, say basically 450 metres, then you will start
looking in that table if that distance is already there.
This has already been known about in our community for many years, it's called data mining.
There's no intelligence, but still you need to come up with some
very sophisticated algorithms which enable you to do a fast search.
In the specific case I'm talking about, you will need basically now to search
1 metre, 2 metres, 3 metres, but you will clusterise your data and know
immediately that 450 metres is in the class 400 - 500.
Now if the distance is not there, and this is where we talk about intelligence,
you will start doing some kind of linear combinations of the
distance which were already existing in your database to find what are the new
linear combinations you need to do in the angle domain to find the angle
with which you can kick the ball. This linear regression that you're doing
basically is at the heart of many machine learning algorithms.
It requires some sophisticated mathematical tools and basically enables you to find the
angle with which you can kick the ball. In the terminology of machine learning,
or AI, we call this a black box that you train with the data, where the input
basically is the distance that you need to find and the output is the angle
basically that is given to you. Now this technique seems quite outstanding in the
sense that you're able with AI techniques to recover Newton equations.
However, you have to have in mind that there's a couple of caveats with that method.
The first caveat of course is when you start training basically your
system, it might be sunny, but you're then asked to kick the ball when it's rainy.
We have here basically a discrepancy which requires retraining every time the
conditions change, so this has a cost. The second caveat of this method is also
what we call the input-output parameters which are not necessarily known.
In the example that I gave, there's not a mapping or a unique mapping between distance and angle.
There's also what we call the velocity which is important and
the initial velocity with which you kick. And this is not something that is
immediately known when you start tackling the problem.
The third caveat of this method is of course the generalisation problem.
Imagine you start learning or kicking the ball basically on Earth and you're asked after to kick the
ball on the Moon. It's extremely complicated to extrapolate from the
existing data to be able to know what the governing laws are that will be
required on the Moon. This option or this point of view of replacing models with
big data has had a huge impact on our discipline.
Typically, in voice recognition, for many years people have spent time trying to model the larynx
and trying to find the way voice was emitted.
It turns out that this wasn't successful. The first successes appeared when we
decided to stop modelling, but see voice recognition as what we call a
classification problem. We register many, many data voices, and next time there's a
new pattern of voice appearing, we do some kind of correlation to be able to
infer what the sound was which was emitted. The techniques of AI are not new,
and they date back to a big conference in 1956 where top mathematicians gave their
view, such as Shannon and Turing. It went into a couple of winters after 1956, and
since the year 2000 we've been seeing basically a big outbreak of AI
techniques in our community. And this is for three main reasons: the first reason
is basically the huge amount of data and storage capability that we have today
and that enables us basically to have the capability of crunching the data;
the second main reason is the computing capability that is available today and
which enables us of course to also crunch the data;
the third reason is basically the machinery of the algorithmic techniques that we have available today,
which enables us to do inference. Today, many mathematicians are working on new
mathematical techniques that can provide trustworthy and explainable AI algorithms
which are at the same time efficient and implementable.
Watch the full series on the YouTube channel, What Makes It Tick?