Unlike traditional backdoor attacks that focus on specific class labels, our approach aims to induce poisoned models to predict future data as a predefined target pattern.

Neural Information Processing Systems http://nips.cc/

Neolithic period to the Industrial Revolution and includes information reviewed and assembled by history experts and graduate research assistants.

Neither (i) nor (ii) are known for general RMABs. Therefore, to capture the scheduling problems addressed inthiswork,weintroduce anewsubclass ofRMABs,Collapsing Bandits, distinguished by the following feature: when an arm is played, the agent fully observes its state, "collapsing" any uncertainty, but when an arm is passive, no observation is made and uncertainty evolves.

"collapsing" any uncertainty, but when an arm is passive, no observation is made,

Plus: how wastewater tracking could help curb measles' rise in the US. Can ChatGPT Health do better? For the past two decades, there's been a clear first step for anyone who starts experiencing new medical symptoms: Look them up online. The practice was so common that it gained the pejorative moniker "Dr. But times are changing, and many medical-information seekers are now using LLMs. According to OpenAI, 230 million people ask ChatGPT health-related queries each week.

Apple's Siri AI will be powered by Gemini Reclaim stolen African artifacts with your crew on February 10. The intriguing Africanfuturist heist game,, is out on February 10. Developed by independent South African studio Nyamakop, the game focuses on a ragtag crew from Johannesburg that liberates real-life African artifacts from a series of fictionalized Western museums. You have to carefully plan each heist with your fellow teammates, knowing where to place each crew member and how you're going to get in and out in one piece. Once you've grabbed the artifact you're looking for in each mission, an alarm will sound and you have a limited amount of time to escape, so good preparation is vital.

Large Language Models (LLMs) have the potential to transform humanities and social science research, yet their history knowledge and comprehension at a graduate level remains untested. Benchmarking LLMs in history is particularly challenging, given that human knowledge of history is inherently unbalanced, with more information available on Western history and recent periods. We introduce the History Seshat Test for LLMs (HiST-LLM), based on a subset of the Seshat Global History Databank, which provides a structured representation of human historical knowledge, containing 36,000 data points across 600 historical societies and over 2,700 scholarly references. This dataset covers every major world region from the Neolithic period to the Industrial Revolution and includes information reviewed and assembled by history experts and graduate research assistants.