Machine learning seminar series - AI, a change in science/technology ... or culture?

Europe/London
11:30 BST

11:30 BST

Description

Host

Peter Dueben (ECMWF)

Speaker

Alberto Arribas is a Met Office and Alan Turing Institute Research Fellow; the Head of Met Office Informatics Lab; and a Professor at the University of Exeter Institute of Data Science and AI.
The Informatics Lab is the major innovation department at the UK Met Office. It combines scientists, technologists and designers to make environmental science and data useful across multiple sectors. The team works with the likes of NASA, Amazon, Microsoft and UK Government Departments to build prototypes and create new approaches and tools to solve problems.
In the past Alberto has led the development of world-leading weather and climate forecasting systems, published over 60 academic papers and been an editor for leading scientific journals, whilst lecturing and being a committee member for organisations such as the World Meteorological Organisation and the USA National Academy of Science.

Abstract

National Meteorological Services are facing the highest level of uncertainty and change in many decades due to a combination of technological discontinuities and contextual changes. 

This creates new organisational challenges, altering existing power and social structures within NMS. Analysis from other industries that have faced similar transformations in the past show that the period of change we are entering could be as long as 30 years and that there is a substantial risk that the foundations of the weather industry could be altered significantly.

Therefore, NMS need to use their resources not only to make best use of the diminishing improvements available within the current technology trajectory but to simultaneously innovate in new technologies to ensure they can generate value in the future.
This talk will analyse the strategic options available to National Meteorological Services using examples of ongoing work at the Met Office Informatics Lab.

Registration
Machine learning seminar series
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