Royal Society Milner Prize Lecture 2021 given by Professor Zoubin Ghahramani FRS.
Modern artificial intelligence (AI) is heavily based on systems that learn from data. Such machine learning systems have led to breakthroughs in the sciences and underlie many modern technologies such as automatic translation, autonomous vehicles, and recommender systems. Probability theory provides a foundation for understanding machine learning and how to build rational decision-making systems. Professor Ghahramani will review the foundations of probabilistic AI and how it relates to deep learning. He will then discuss some topics at the frontier of probabilistic machine learning and some of the societal challenges and opportunities for AI.
Attending the event
- This lecture will take place online as a Zoom webinar on 10 November at 6.30pm GMT. This event will be recorded (including the live Q&A) and the recording will be available on YouTube soon after the event.
- The event is free to join. Advance registration is essential.
- Live subtitles will be available.
The Royal Society Milner Award and Lecture, supported by Microsoft Research, is the premier European award for outstanding achievement in computer science. It is awarded to candidates at the peak of their career who have made a substantial contribution to computer science in Europe, with the strategic aim of supporting European researchers and institutes. The recipient is a European researcher or researcher who has been resident in Europe for 12 months or more, and is chosen by the Council of the Royal Society on the recommendation of the Milner Award Committee. The Committee is made up of Fellows of the Royal Society, Members of the Académie des sciences (France) and Members of Leopoldina (Germany). The award is named in honour of Professor Robin Milner FRS (1934-2010), a pioneer in computer science. The medal is of bronze, is awarded annually and is accompanied by a gift of £5,000.
Professor Zoubin Ghahramani FRS was awarded the Royal Society Milner Award and Lecture in 2021 for his fundamental contributions to probabilistic machine learning.
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