Taken from high resolution satellite imagery. Can you tell us a bit about your background and your current area of research? I use computational tools to study animal behaviour. After my PhD, I joined the U.S. Army Research Office, where I used satellite imagery to follow animals across whole landscapes, which is a powerful technique for seeing broad patterns, but far too coarse to capture what individuals are actually doing. That gap is what drives my current work at Princeton: I combine drone video with AI methods to resolve movement at much finer scales, as I have done in studies of Olive Baboons and lions.

AI-assisted modernization can reduce the time and complexity of transforming legacy systems while creating a foundation for faster innovation, says Asifa Sherazi, CIO of health insurance at Bupa and Sanjeev Tripathi, senior VP, region head of BFSI, healthcare, and public sector at Infosys. For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue. But with the rise in customer expectations and the changes AI brought to the economics of software development, that calculation is changing. Bupa's modernization of its My Bupa mobile application offers a case study in what becomes possible when a legacy migration is treated as a business transformation rather than a technology rewrite. Bupa CIO of health insurance Asifa Sherazi describes the risks of waiting for legacy systems to become an emergency: "The end-of-life technology is a risk that compounds quietly, and then arrives all at once." For Bupa, moving its application from Xamarin to native Swift and Kotlin improved the app rating from 3.7 to 4.7, while the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. "What they'll notice is that when they need us, often at a stressful moment, it just simply works," Sherazi says. Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, contends that AI is helping change the equation. "The emergence of AI is fundamentally shifting the economics of modernization," he says, reducing the effort, risk, and time traditionally associated with these programs. At Bupa, combining AI-assisted reverse engineering with forward engineering helped deliver the transformation in approximately 60% less time than would have been possible in the pre-AI era.

The central U.S. should be a particular hot spot for birds heading south. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The migratory birds were seen currently gathering for their journey to the southern wintering grounds in Germany on October 10, 2020. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

Plus: Space data centers don't exist yet, but people already oppose them. Supercooled kidneys have been transplanted into pigs in a "landmark achievement" When it comes to organ donation, time is everything. As soon as an organ has been removed from a donor's body, it starts to deteriorate. Surgeons have only a matter of hours to get it into a recipient. In most cases, organs will be kept on ice during that time, at around 4 C (39 F). They cannot be frozen--in previous attempts, ice has formed, causing all kinds of damage.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?

We present the design and implementation of a new lifetime-aware tensor offloading framework for GPU memory expansion using low-cost PCIe-based solid-state drives (SSDs). Our framework, TERAIO, is developed explicitly for large language model (LLM) training with multiple GPUs and multiple SSDs. Its design is driven by our observation that the active tensors take only a small fraction (1.7% on average) of allocated GPU memory in each LLM training iteration, the inactive tensors are usually large and will not be used for a long period of time, creating ample opportunities for offloading/prefetching tensors to/from slow SSDs without stalling the GPU training process. TERAIO accurately estimates the lifetime (active period of time in GPU memory) of each tensor with the profiling of the first few iterations in the training process. With the tensor lifetime analysis, TERAIO will generate an optimized tensor offloading/prefetching plan and integrate it into the compiled LLM program via PyTorch. TERAIO has a runtime tensor migration engine to execute the offloading/prefetching plan via GPUDirect storage, which allows direct tensor migration between GPUs and SSDs for alleviating the CPU bottleneck and maximizing the SSD bandwidth utilization. In comparison with state-of-the-art studies such as ZeRO-Offload and ZeRO-Infinity, we show that TERAIO improves the training performance of various LLMs by 1.47 on average, and achieves 80.7% of the ideal performance assuming unlimited GPU memory.

Out-of-tree kernel patches are essential for adapting the Linux kernel to new hardware or enabling specific functionalities. Maintaining and updating these patches across different kernel versions demands significant effort from experienced engineers. Large language models (LLMs) have shown remarkable progress across various domains, suggesting their potential for automating out-of-tree kernel patch migration. However, our findings reveal that LLMs, while promising, struggle with incomplete code context understanding and inaccurate migration point identification. In this work, we propose MIGGPT, a framework that employs a novel code fingerprint structure to retain code snippet information and incorporates three meticulously designed modules to improve the migration accuracy and efficiency of out-of-tree kernel patches. Furthermore, we establish a robust benchmark using real-world out-of-tree kernel patch projects to evaluate LLM capabilities. Evaluations show that MIGGPT significantly outperforms the direct application of vanilla LLMs, achieving an average completion rate of 74.07%

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?