议题

Public forecasts by frontier lab leaders about when systems matching or exceeding human cognitive capability will arrive, and the mechanisms those forecasts depend on.

The practice of taking model-based systems from working prototype to production reliability — evaluation, fine-tuning, serving cost and the trade-offs between them.

How legal systems assign responsibility, rights and liability for systems that act without a human deciding each action.

The effect of AI systems on employment, task composition and the structure of entry-level work.

The application of AI systems to scientific discovery — protein structure, weather, materials and drug design — as distinct from general-purpose language capability.

Systems in which language models plan, call tools and carry out multi-step tasks rather than only generating text in response to a prompt.

Network coverage, spectrum and the economics of reaching places terrestrial infrastructure does not serve.

The electricity, grid capacity and siting constraints that determine where AI infrastructure can physically be built.

How organisations measure the output and experience of software teams, and how AI tooling changes what those measures mean.

How large organisations move AI systems from pilot into governed production, including data foundations, access control and operating model changes.

Measuring and improving how consistently model-driven systems produce correct results across repeated runs.

AI systems that perceive and act in the physical world — robotics, autonomous vehicles and the simulation stacks used to train them.