The learned weights of a neural network have often been considered devoid of scrutable
internal structure. In this paper, however, we look for structure in the form of clusterability: how …

Objective To review the intellectual history of concussion from the mid‐19th century to the
opening decade of the 21st century. Background Head injuries (HI) and their acute and long‐…

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state-…

This essay uses gender as a category of historical and sociological analysis to situate two
populations—boxers and victims of domestic violence—in context and explain the temporal …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However, …

The literature on adversarial attacks in computer vision typically focuses on pixel-level
perturbations. These tend to be very difficult to interpret. Recent work that manipulates the latent …

… Stephen Casper received support for this work from … [5] Isabelle Augenstein, Christina
Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob …

Interpretable AI tools are often motivated by the goal of understanding model behavior in out-of-distribution
(OOD) contexts. Despite the attention this area of study receives, there are …

Despite efforts to align large language models to produce harmless responses, they are still
vulnerable to jailbreak prompts that elicit unrestricted behaviour. In this work, we investigate …

A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their
accuracy does not degrade when the network width is increased. Recent evidence suggests …