AI in Science Fellowship Q+A Session
16/06/2026
16:00 - 17:00This informal Q+A is for anyone interested in finding out more about the AI in Science Postdoctoral Fellowships before applying to the scheme.
This informal Q+A is for anyone interested in finding out more about the AI in Science Postdoctoral Fellowships before applying to the scheme.
I will introduce the fundamental ideas and challenges of “Predictable AI”, a nascent research area that explores the ways in which we can anticipate key indicators of present and future AI ecosystems. I will argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems, and thus should be prioritised over performance.
In this presentation, Yutong will introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data. To do this, she will define a common format, “visual sentences”, in which she can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions without needing any meta-knowledge beyond the pixels.
This is a two-part talk series with an hours break in between on Large Language Models (LLMs) intended for non-specialists. I intend to explain the current paradigm, its main unsolved problems, and also provide links to potential interdisciplinary research areas.
We are excited to invite you to the I-X Breaking Topics in AI conference supported by Schmidt Futures
In this presentation, Leandro will share several accomplishments of the BigCode project, an open-scientific collaboration working on the responsible development and use of LLMs for code generation.
Language agents are emerging AI systems that use large language models (LLMs) to interact with the world. While various methods and demos have been developed, it is often hard to systematically understand or evaluate them.
