Deep learning has shown great potential in improving and accelerating the entire medical imaging workflow, from image acquisition to interpretation. This talk will focus on the recent advances of deep learning in medical imaging, from the reconstruction of accelerated signals to automatic quantification of clinically useful information.
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.
