In the next LTS 2022 Seminar Dr. Emmanuel Kahembwe will discuss how MLAs are trained using multimodal data sets. This will be an accessible session for non-experts.

Dr. Emmanuel Kahembwe is a member of the ART/1 Committee on Artificial Intelligence at the British Standards Institution (BSI). He is also a member of the European Committee for Electrotechnical Standardization (CEN-CENELEC) WG on Digital Sovereignty. He is an alumni of The University of Edinburgh (MSc & PhD), Heriot-Watt University (PhD), Napier University (BSc), The Edinburgh Centre for Robotics and The Heidelberg Laureate Forum. His expertise spans the fields of AI, Robotics, Autonomous Systems, High Performance Systems and Games Development. He is the CEO of VDE UK, part of the VDE group, which is Europes oldest and largest technology organisation.

Andrew Murray is a Professor of Law at LSE Law School, with particular reference to New Media and Technology Law. He is Director of the LSE Law, Society and Technology Group, and a Fellow of the Royal Society for the Encouragement of Arts, Manufactures and Commerce (FRSA). He is also Associate Dean of LSE Law School.

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The LSE Law, Technology and Society group conducts world-leading research into the regulation of technology and its normative implications, including the legal, regulatory, policy and social implications of emerging technologies such as AI and ICT, biomedical and biotechnologies, distributed systems (including blockchain), FinTech, RegTech and LawTech.

LSE Law School (@LSELaw) was ranked first for research outputs in the most recent Research Excellence Framework (REF) and in the top 10 law departments overall by The Complete University Guide in 2020. In the QS World University rankings for 2020, the Department was ranked sixth (out of 200 departments worldwide). Our staff play a major role in helping to shape policy debates, and in the education of current and future lawyers and legal scholars from around the world.

Twitter Hashtag for this event: #LSELTS

Training Advanced Machine Learning using Multimodal Data Sets