However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols.

Effective communication requires the ability to refer to specific parts of an observation in relation to others.

T o this end, we propose a simple yet powerful paradigm for seamlessly unifying different human pose and shape-related tasks and datasets. Our formulation is centered on the ability - both at training and test time - to query any arbitrary point of the human volume, and obtain its estimated location in 3D. We achieve this by learning a continuous neural field of body point localizer functions, each of which is a differently parameterized 3D heatmap-based convolutional point localizer (detector).

A critical approach for efficiently deploying computationally demanding large language models (LLMs) is Key-V alue (KV) caching.

Preferences serve as an alternative but recent work rarely considers preference learning given multiple tasks.

Scout AI is using technology borrowed from the AI industry to power lethal weapons--and recently demonstrated its explosive potential. Like many Silicon Valley companies today, Scout AI is training large AI models and agents to automate chores. The big difference is that instead of writing code, answering emails, or buying stuff online, Scout AI's agents are designed to seek and destroy things in the physical world with exploding drones. In a recent demonstration, held at an undisclosed military base in central California, Scout AI's technology was put in charge of a self-driving off-road vehicle and a pair of lethal drones. The agents used these systems to find a truck hiding in the area, and then blew it to bits using an explosive charge.