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Large Language Models (LLMs) are increasingly used to support scientific research, but their knowledge of scientific advancements can quickly become outdated. We introduce ScienceMeter, a new framework for evaluating scientific knowledge update methods over scientific knowledge spanning the past, present, and future. ScienceMeter defines three metrics: knowledge preservation, the extent to which models' understanding of previously learned papers are preserved; knowledge acquisition, how well scientific claims from newly introduced papers are acquired; and knowledge projection, the ability of the updated model to anticipate or generalize to related scientific claims that may emerge in the future. Using ScienceMeter, we examine the scientific knowledge of LLMs on claim judgment and generation tasks across a curated dataset of 15,444 scientific papers and 30,888 scientific claims from ten domains including medicine, biology, materials science, and computer science. We evaluate five representative knowledge update approaches including training- and inference-time methods. With extensive experiments, we find that the best-performing knowledge update methods can preserve only 85.9% of existing knowledge, acquire 71.7% of new knowledge, and project 37.7% of future knowledge. Inference-based methods work for larger models, whereas smaller models require training to achieve comparable performance. Cross-domain analysis reveals that performance on these objectives is correlated. Even when applying on specialized scientific LLMs, existing knowledge update methods fail to achieve these objectives collectively, underscoring that developing robust scientific knowledge update mechanisms is both crucial and challenging.

--Wearable devices like exoskeletons are designed to reduce excessive loads on specific joints of the body. Specifically, single-or two-degrees-of-freedom (DOF) upper-body industrial exoskeletons typically focus on compensating for the strain on the elbow and shoulder joints. However, during daily activities, there is no assurance that external loads are correctly aligned with the supported joints. Optimizing work processes to ensure that external loads are primarily (to the extent that they can be compensated by the exoskeleton) directed onto the supported joints can significantly enhance the overall usability of these devices and the ergonomics of their users. Collaborative robots (cobots) can play a role in this optimization, complementing the collaborative aspects of human work. In this study, we propose an adaptive and coordinated control system for the human-cobot-exoskeleton interaction. This system adjusts the task coordinates to maximize the utilization of the supported joints. When the torque limits of the exoskeleton are exceeded, the framework continuously adapts the task frame, redistributing excessive loads to non-supported body joints to prevent overloading the supported ones. We validated our approach in an equivalent industrial painting task involving a single-DOF elbow exoskeleton, a cobot, and four subjects, each tested in four different initial arm configurations with five distinct optimisation weight matrices and two different payloads. Personal use of this material is permitted. ANUAL operations such as packaging [1], assembly [2] and painting [3] are essential in many industries, though they can place a significant strain on the physical health of human workers.

Just days after a gunman shot and killed 17 people at Marjory Stoneman Douglas High School in Parkland Florida in February, Shreya Nallapati, a 17-year old high school senior from Highlands Ranch, Colorado, declared "a technological revolution against mass shootings, specifically in schools." Fed up with the lack of action being taken by government officials and leaders to end mass shootings, Nallapati decided to take what she knew best-- technology -- and apply it in a way that would make a lasting impact. "I was tired of people posting condolences on Facebook and then forgetting about the incident," Nallapati explains. "I want to use my knowledge of artificial intelligence to bring people together to solve a problem that is prevailing in society." Nallapati put a call out to her network of young women technologists, the Aspirations in Computing community, to join the effort.

Artificial intelligence is the technology of the future, and public schools are indoctrinating students to prepare. STEM School Academy in Highland Ranch, Colorado allowed students to use a 3D printer and artificial intelligence to create historical figures for their history class. "I wanted to bring history alive. I wanted the students to experience the process of talking to an artificial intelligence, talking to a person long deceased," history teacher Owen Cegielski told 9News. So who did the students decide to reanimate?