Seminars

Explainability plays a pivotal role in building trust and fostering the adoption of artificial intelligence (AI) in healthcare, particularly in high-stakes domains like neuroscience where decisions directly affect patient outcomes.

AI has the potential to transform learning, creativity, and productivity. It represents a profound platform shift in technology – like the internet, or the shift to mobile. As with any transformational shift, AI will be (and already is) used for good and for malicious purposes.

Abstract: Wearable devices such as Apple Watch and Fitbit wristband allow users to track their health statistics around the clock. They have become increasingly popular over the past few years.

Multimodal Graph Learning (MGL) is an emerging area in machine learning that focuses on graphs whose nodes carry information from different modalities, such as text and image. A central challenge in MGL is integrating these heterogeneous data types, which are not directly comparable.

Idris is a functional programming language with first-class types, which allow properties to be expressed in the type system, and with an interactive type-driven editor which allows programs to be developed as a formal conversation with the machine.

*Abstract*

Mike Gordon’s Higher-Order Logic (HOL) is one of the most important logical foundations for interactive theorem proving. The standard semantics of HOL, due to Andrew Pitts, employs a downward closed universe of sets, and interprets HOL’s Hilbert choice operator via a
global choice function on the universe.

Large language models (LLMs) are helping millions of users to learn and write about a diversity of issues. In doing so, LLMs may expose users to new ideas and perspectives, or reinforce existing knowledge and user opinions.

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Abstract not available