The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment of recognition for the of AI role in biology. What comes next after protein folding?
In ML classification tasks, achieving high accuracy is only part of the goal; it's equally important for models to express how confident they are in their predictions.
This story is a collaboration of three Institutes that are working at the intersection of cell research, cancer research and care, and artificial intelligence.
Our approach provides a simple and practical perspective on what memorization can mean, providing a useful tool for functional and legal analysis of LLMs.
We propose the asymmetric certified robustness problem, which requires certified robustness for only one class and reflects real-world adversarial scenarios.
How can we reconcile the ease of specifying tasks through natural language-based approaches with the performance improvements of goal-conditioned learning?