Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.

California's landmark frontier AI law to bring transparency Late last month, California became the first state in the United States to pass a law to regulate cutting-edge AI technologies. Now experts are divided over its impact. They agree that the law, the Transparency in Frontier Artificial Intelligence Act, is a modest step forward, but it is still far from actual regulation. It mandates reporting of incidents such as large-scale cyber-attacks, deaths of 50 or more people, large monetary losses and other safety-related events caused by AI models. It also puts in place whistleblower protections.

A VSR, respectively) has traditionally been conducted independently.

This paper proceeds as follows.

We study adversarially robust transfer learning, wherein, given labeled data on multiple (source) tasks, the goal is to train a model with small robust error on a previously unseen (target) task. In particular, we consider a multi-task representation learning (MTRL) setting, i.e., we assume that the source and target tasks admit a simple (linear) predictor on top of a shared representation (e.g., the final hidden layer of a deep neural network). In this general setting, we provide rates on the excess adversarial (transfer) risk for Lipschitz losses and smooth nonnegative losses. These rates show that learning a representation using adversarial training on diverse tasks helps protect against inference-time attacks in data-scarce environments. Additionally, we provide novel rates for the single-task setting.

When turned on, "GitHub Copilot checks code completion suggestions with their surrounding