We would be delighted for you to join us at the The Alan Turing Lecture titled 'Charting the Horizons of Artificial Intelligence Research' which will be given by Dr Raia Hadsell on Friday, 29th April 2022 at 5.00pm.

The lecture will be held in the Keynes Hall and, although it is free to attend, booking is required as seating is limited. Prior to the lecture, two Exhibitions will be held from 3.15pm - 4.45pm. Following the lecture, guests are invited to enjoy refreshments from 6.15pm - 7.15pm. Further information is available via the event webpage.

The lecture will also be streamed live (and open to all) via Zoom webinar. To register via Zoom please click here.

Lecture Abstract: The dream of an artificial general intelligence (AGI) that will share our values, solve our hardest problems, and benefit all of humanity is very slowly becoming a reality, owing to advances in data science and machine learning as well as the ever-increasing speed and efficiency of computers. In this talk I will describe the horizons of modern AGI research and identify some early successes as well as risks, particularly with respect to scientific breakthroughs. Weather forecasting, plasma stabilisation for nuclear fusion, and deciphering of ancient texts—these are some areas where breakthroughs have emerged from partnerships with AI scientists. As we get closer to AGI I believe that the technology will be increasingly general, adaptable, and interactive, bringing opportunities and benefits to people everywhere, in both their personal and work life.

Raia Hadsell is the Director of Robotics at DeepMind. She holds an appointment at UCL and is an ELLIS fellow. She is on the executive board of ICLR, CoRL, and WiML (Women in Machine Learning), a founding organizer of Neuroscience for AI Systems (NAISys), and is on the CIFAR Brains and Machines Advisory Board. She has served as a senior area chair or area chair of ICML, NeurIPS, ICLR, CoRL, and AAAI in recent years, and is the Program Chair for NeurIPS in 2020. Dr. Hadsell joined DeepMind in 2014 to pursue new solutions for artificial general intelligence. Her research focuses on the challenge of continual learning for AI agents and robots, and she has proposed neural approaches such as policy distillation, progressive nets, and elastic weight consolidation to solve the problem of catastrophic forgetting.