Artificial Intelligence

Biography

Biography: Bogdan Gabrys

Abstract

We are currently experiencing an incredible, explosive growth in digital content and information. According to IDC, the digital
universe in 2020 will be 50 times as big as in 2010 and that it will double every two years. Research in traditionally qualitative
disciplines is fundamentally changing due to the availability of such vast amounts of data. In fact, data-intensive computing has
been named as the fourth paradigm of scientifi c discovery and is expected to be a key in unifying the theoretical, experimental
and simulation based approaches to science. Th e commercial world has also been transformed by a focus on big data with
companies competing on analytics. Data has become a commodity and in recent years has been referred to as the new oil. Th ere
has been a lot of work done on the subject of intelligent data analysis, data mining and predictive modeling over the last 50 years
with notable improvements which have been possible with both the advancements of the computing equipment as well as with
the improvement of the algorithms. However, even in the case of the static, non-changing over time data there are still many
hard challenges to be solved which are related to the massive amounts, high dimensionality, sparseness or inhomogeneous
nature of the data to name just a few. What is also very challenging in today’s applications is the non-stationarity of the data
which oft en change very quickly posing a set of new problems related to the need for robust adaptation and learning over time.
In scenarios like these, many of the existing, oft en very powerful, methods are completely inadequate as they are simply not
adaptive and require a lot of maintenance attention from highly skilled experts, in turn reducing their areas of applicability.
In order to address these challenging issues and following various inspirations coming from biology coupled with current
engineering practices, we proposed a major departure from the standard ways of building adaptive, intelligent predictive systems by utilizing the biological metaphors of redundant but complementary pathways, interconnected cyclic processes,
models that can be created as well as destroyed in easy way, batteries of sensors in form of pools of complementary approaches,
hierarchical organization of constantly optimized and adaptable components. In order to achieve such high level of adaptability
we have proposed novel fl exible architectures which encapsulate many of the principles and strategies observed in adaptable
biological systems. Th e proposed approaches have been extensively and very successfully tested by winning a number of
predictive modeling competitions and applying to a number of challenging real world problems including pollution/toxicity
prediction studies, building adaptable soft sensors in process industry in collaboration with Evonik Industries or forecasting
demand for airline tickets covering the results of one of our collaborative research projects with Luft hansa Systems. Following
the drive towards automation of predictive systems building, deployment and maintenance, recent work at Prof. Gabrys' group
resulted in an approach and an open-source soft ware which allows to automatically compose, optimize and adapt multicomponent
predictive systems (MCPS) potentially consisting of multiple data preprocessing, data transformation, feature and
predictive model selection and post-processing steps.