Academic Area
Artificial Intelligence
Understanding how intelligent systems are built, trained, evaluated, and governed.
Thinking About Thinking supports rigorous, interdisciplinary inquiry into artificial intelligence and machine learning, from foundational theory to real-world deployment.
This focus area brings together researchers, engineers, and practitioners working across artificial intelligence, machine learning, computer science, data science, robotics, and related fields. Our aim is to build shared understanding, surface open problems, and strengthen the intellectual infrastructure needed to study and shape intelligent systems responsibly.
We are particularly interested in questions that sit between disciplines: how learning systems generalize, how they interact with human institutions, how they should be evaluated, and how technical decisions shape social outcomes.
Core Questions We Explore
How do modern learning systems generalize, reason, and fail?
What are the limits of current machine learning paradigms?
How should AI systems be evaluated, interpreted, and compared?
How do technical systems interact with human cooperation, institutions, and governance?
Recordings
We publish recordings of talks, panels, and seminars to make serious thinking about artificial intelligence and machine learning accessible beyond the conference hall.
Artificial intelligence and machine learning are core themes across our flagship events.
At our AE Global Summits, we convene researchers, builders, and policymakers to examine Open Problems in AI research, infrastructure, applications, and governance.
At our Conference on the Mathematics of Neuroscience and AI we explore the deeper mathematical and computational foundations of learning and intelligence, often in dialogue with neuroscience and cognitive science.
Events
Ambassadors
Year 2025
Ambassadors
