I'm Yiannis Papadopoulos
and I lead the group on dependable intelligent systems.
I'm talking about things like autonomous cars,
surgical robots, or robots that will take care of old people
or drones that in the future we will send
to deliver goods, or financial algorithms that in the future
will take the majority of the decisions in the stock market.
These are systems which must be reliable and trustworthy
because if they fail, they can have catastrophic consequences
for people and the environment, for the economy and the society.
For example,
the autonomous car can fail to detect a pedestrian
and they may kill the pedestrian or a financial algorithm
may take some erroneous decisions.
These errors can multiply and can bring the whole system down
and damage the global economy.
In the
university here, we take these risks very, very seriously.
So for 25 years, we have been working in developing methods and tools
to improve the trustworthiness of A.I.,
artificial intelligence and intelligence systems.
And one of the tools that we developed is called the hip-hops tool
and with the tool we do failure analysis
and improvement of this model and then propose design improvements
that will mask or remove those faults
and improve its trustworthiness.
It uses evolutionary techniques
similar to the ones that nature uses to evolve the species,
something that we pioneered here and we call penguin algorithms.
And these are algorithms that capture the social intelligence
or the collaboration of penguins in a colony.
By encoding these algorithms into the computer,
we are able to solve engineering problems by sending to them software penguins.
There's something else that
we have developed, which I think is very much in the state of the art.
We call it safety of machine learning or ‘safe and well’. Machine learning algorithms,
they learn from data to do specific things,
for example, in a car to detect pedestrians.
You use a camera and algorithms to detect pedestrians.
But one problem with machine learning is that
if in operation, the data that you get
is different from the training data, perhaps because it is raining,
perhaps because the images are blurred, then the machine learning algorithm
will become untrustworthy and erroneous. And 'safe and well',
what we develop does precisely this.
It can give an estimate
of confidence in the accuracy of machine learning,
which of course is really important for the safety of the car.
This technology has achieved
global technology transfer,
so there are many large organisations that use these tools
from Honeywell Aerospace in the US
to Huawei in China,
Toyota and Honda in Japan, Volvo in Sweden
to Fiat in Italy and to Empire in Brazil.
In the last Research Excellence
Framework, this work was ranked internationally excellent.
The challenges will continue to exist and new challenges will come.
And I feel that the most important thing
is working with young people
and setting common goals and working towards these goals
and helping them also to develop their careers and becoming
useful scientists that can provide useful things to society.
It's very rewarding to see the work of my group
having such impact; academic and industrial,
and it is precisely
that working with these people
that I find most rewarding about being in the University of Hull
and being in academia.