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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are increasingly used to support scientific research, but their knowledge of scientific advancements can quickly become outdated.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.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. For the two weeks that the Hackneys' baby girl lay in a Pittsburgh hospital bed weak from dehydration, her parents rarely left her side, sometimes sleeping on the fold-out sofa in the room. They stayed with their daughter around the clock when she was moved to a rehab center to regain her strength. Finally, the 8-month-old stopped batting away her bottles and started putting on weight again. "She was doing well and we started to ask when can she go home," Lauren Hackney said.

To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which have great potential for a variety of applications due to the abundance of information in light fields compared to singular images. Using the Unity and C# frameworks, we develop a novel approach for generating large, scalable, and reproducible light field datasets based on customizable hardware configurations to accelerate light field deep learning research.

NEW YORK CITY--Uber is looking to get into pharmacy medication delivery. Prescription eyeglass company Warby Parker is moving into virtual eye exams, and audio equipment maker Bose wants to help consumers get better sleep through hearing technology. Consumer-focused companies are rapidly moving further into healthcare, and industry incumbents need to be ready for accelerating change: That was one of the big takeaways from CB Insights' Future of Health conference in Manhattan this week. It's not just startups attacking entrenched interests in healthcare; it's large companies as well, said CB Insights CEO Anand Sanwal during the conference. "The field of play is changing pretty dramatically, and the competitive lines are constantly being redrawn," he said, noting Amazon's "unbundling" of the pharmacy, Apple's unbundling of the clinical trials process and Google's unbundling of the hospital.

The pressures facing healthcare delivery organizations are well documented: increased regulatory oversight, financial challenges, heightened competition, a shrinking talent pool, and many more. Also well known is the army of consultants, systems integrators and technology companies promising their solutions will address one or more of these market trends. All too often, implementation fails to deliver the promised results, leaving the healthcare organization with a big bill and little to show for it. What if there were new solutions, built on the powerful capabilities of artificial intelligence and machine learning (AI/ML) that provided proven, quantifiable cost savings of 30% or more, and consistent improvements in uptime for some of the most expensive equipment healthcare facilities operate? Harbor Research, a leading strategy and technology research firm, notes utilizing AI/ML to leverage complex machine data from healthcare imaging equipment alone will provide $11.1 billion in revenue value (decreased costs/increased revenue generation) by 2022.

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.

It's a myth that Artificial Intelligence is just for large corporations. Robotics and automation are transforming the workplace for smaller businesses as the technology becomes ever cheaper and more accessible. There are many reasons to be optimistic about AI. An Accenture report suggests that it could bring an additional £814 billion to the UK economy by 2035, whist research firm Gartner reports that in 2020, AI will create 2.3 million jobs. General AI-based productivity tools are becoming more readily available and increasingly affordable – sometimes even free.

In the world of state and local government, emerging technologies are always a hot topic. Steve Towns and Joe Morris are two of our content and research experts and they have decided to cover these technologies and more in a recurring video series. The topics of discussion this week are blockchain and artificial intelligence. Think we're hearing a lot more talk about those. We just did some research around that.