Artificial Intelligence
How learning systems generalise and how they fail.
Artificial intelligence is the most consequential of the sciences of intelligence and the most fragmented. Empirical progress has been substantial; the methodological standards by which large learning systems are evaluated and the institutions that govern their deployment, have not, on the whole, kept pace. Our work brings together researchers, engineers and practitioners across artificial intelligence, machine learning and the surrounding formal sciences. The questions of greatest interest sit between fields: how learning systems generalise; what evaluations are adequate to the systems we now build; where the present limits lie (mathematical, computational, empirical); and how technical decisions of considerable consequence shape and are shaped by, the institutions in which they are deployed.
- How do modern learning systems generalise, reason and fail and crucially why?
- What methods of evaluation, interpretation and direct comparison are adequate to the systems we now build?
- Where do the limits of current machine-learning paradigms lie?
- How do technical systems and human institutions reshape one another?
Conferences
Artificial intelligence is the through-line across our flagship events. At the AE Global Summit, we convene researchers, builders and policymakers around open problems in research, infrastructure, application and governance. At the Conference on the Mathematics of Neuroscience and AI, the same questions are taken up at the foundations, in dialogue with the cognitive and brain sciences.
AIs that set their own goals - learning general purpose world models for efficient planning & acting
Keynote speaker Professor Juergen Schmidhuber from King Abdullah University of Science and Technology (KAUST) and IDSIA presents his talk "A…
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Panel Discussion - "Open Problems in Computational Neuroscience"
Panel discussion on the topic of “Open Problems in Computational Neuroscience” Panel guests: Dr James Whittington (Chair) Professor Wolfgang…
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Interviews - Dr James Whittington (NM2025)
Interview with Dr James Whittington and Dr Ivana Kejic! We'd like to thank our sponsors for this conference: XTX Markets, Google DeepMind, C…
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Interviews - Professor Eve Marder (NM2025)
Interview with Professor Eve Marder and Ashley Kim! We'd like to thank our sponsors for this conference: XTX Markets, Google DeepMind, Conte…
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Fireside chat: Where next? Perspectives on AI research after grad school
Panel discussion on the topic of “Where next? Perspectives on AI research after grad school” Panel guests: Dr Ruairidh McLennan Battleday (H…
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Mood as a vehicle of reinforcement learning (Professor Eran Eldar)
Professor Eran Eldar presents "Mood as a vehicle of reinforcement learning" Abstract - The science of learning and decision-making has large…
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The Thinking About Thinking Ambassador Programme is an open, application-based pathway for students and early-career researchers who wish to take part in serious, interdisciplinary conversations about intelligence. Ambassadors support our conferences, workshops, and community initiatives; in turn, they help to extend thoughtful dialogue across universities, disciplines and countries. The programme is designed for those who care, in earnest, about ideas, collaboration, and the construction of intellectual community.
Outstanding ambassadors who, over time, demonstrate sustained contribution, leadership and intellectual engagement may, in due course, be invited into the Thinking About Thinking Fellowship, a private, invitation-only programme reserved for long-term contributors to the work.