These methods usually count on discriminative softmax metric or energy method to screen OOD samples.

We give examples of computing the path-specific harm in Appendices B-D. Omission Problem: Alice decides not to give Bob a set of golf clubs. Therefore, according to the CCA, Alice's decision not to give Bob the'Bob given clubs', and outcome Whatever utility function describes Bob's preferences, the action Note there are other reasonable scenarios where Alice's actions would constitute harm. 'the clerk Alice harmed Bob by not giving him golf clubs'. For example, if Bob's utility is U ( y)= y (i.e. 1 for clubs, 0 for no clubs), then the harm caused by Alice is P ( Y A moment later, Eve would have robbed Bob of his clubs.

This paper proposes a fast and scalable method for uncertainty quantification of machine learning models' predictions.

About two weeks ago, OpenAI released GPT-5. The newest AI model in the GPT line, GPT-5 was put forth as the company's "smartest, fastest, most useful model yet" with "built-in thinking" and "expert-level intelligence." But the release backfired for one important reason. Part of the changes in GPT-5 involved addressing the sycophantic positivity found in previous models, where the AI chatbot would incessantly praise the user to an undo degree and emphatically agree to make the user feel better. Lots of users disliked this, so GPT-5 was made to be "less effusively agreeable" and "use fewer unnecessary emojis."

One approach toward automatic grading is to learn an agent that interacts with a student's program and explores states indicative of errors via reinforcement learning. However, existing work on this approach only provides binary feedback of whether a program is correct or not, while students require finer-grained feedback on the specific errors in their programs to understand their mistakes. In this work, we show that exploring to discover errors can be cast as a meta-exploration problem.

Self-supervised pre-training recently demonstrates success on large-scale multi-modal data, and state-of-the-art contrastive learning methods often enforce the feature consistency from cross-modality inputs, such as video/audio or video/text pairs.

While deep learning's performance has led it to become the dominant paradigm in machine learning,