Technology Engineering Need to melt ice? A new molecular trick could transform deicing. Breakthroughs, discoveries, and DIY tips sent every weekday. As winter approaches, large swaths of the United States are eagerly awaiting their first big snowfalls of the season. As the snowflakes fall, many will dig out old, rusted sleds, toil over shaping the perfect snowball, and relish an evening brought back to life by a warm cup of hot cocoa .

But her passion is for paper--with no scissors. Today, she's a tessellation expert who teaches, invents new designs, and writes papers on the underlying math. Madonna Yoder '17 photographed in her studio Ross Mantle When Madonna Yoder '17 was eight years old, she learned how to fold a square piece of paper over and over and over again. After about 16 folds, she held a bird in her hands. The first time she pulled the tail of a flapping crane, she says, she realized: . That first piece was an origami classic, folded by kids at summer camp for generations and many people's first foray into the art form.

Precision Livestock Farming (PLF) has emerged as a critical field for monitoring and improving animal health and behavior[1]. Accurate and continuous tracking of livestock behavior is essential for identifying early signs of health issues an d enabling timely intervention. Traditional methods for monitoring pig behavior, such as manual observation, are labor - intensive, limited in scalability, and prone to inaccuracies [2]. Recent advancements in PLF have introduced automated systems that lev erage biosensors to track behavior in real time. These sensors, often attached to animals, collect data that is both costeffective and reliable, making them indispensable for modern livestock management [3,4].

Accurate determination of the performance of parallel GPU code typically requires execution-time profiling on target hardware -- an increasingly prohibitive step due to limited access to high-end GPUs. This paper explores whether Large Language Models (LLMs) can offer an alternative approach for GPU performance prediction without relying on hardware. We frame the problem as a roofline classification task: given the source code of a GPU kernel and the hardware specifications of a target GPU, can an LLM predict whether the GPU kernel is compute-bound or bandwidth-bound? For this study, we build a balanced dataset of 340 GPU kernels, obtained from HeCBench benchmark and written in CUDA and OpenMP, along with their ground-truth labels obtained via empirical GPU profiling. We evaluate LLMs across four scenarios: (1) with access to profiling data of the kernel source, (2) zero-shot with source code only, (3) few-shot with code and label pairs, and (4) fine-tuned on a small custom dataset. Our results show that state-of-the-art LLMs have a strong understanding of the Roofline model, achieving 100% classification accuracy when provided with explicit profiling data. We also find that reasoning-capable LLMs significantly outperform standard LLMs in zero- and few-shot settings, achieving up to 64% accuracy on GPU source codes, without profiling information. Lastly, we find that LLM fine-tuning will require much more data than what we currently have available. This work is among the first to use LLMs for source-level roofline performance prediction via classification, and illustrates their potential to guide optimization efforts when runtime profiling is infeasible. Our findings suggest that with better datasets and prompt strategies, LLMs could become practical tools for HPC performance analysis and performance portability.

Online hate speech has become increasingly prevalent on social media platforms, causing harm to individuals and society. While efforts have been made to combat this issue through content moderation, the potential of user-driven counterspeech as an alternative solution remains underexplored. Existing counterspeech methods often face challenges such as fear of retaliation and skill-related barriers. To address these challenges, we introduce CounterQuill, an AI-mediated system that assists users in composing effective and empathetic counterspeech. CounterQuill provides a three-step process: (1) a learning session to help users understand hate speech and counterspeech; (2) a brainstorming session that guides users in identifying key elements of hate speech and exploring counterspeech strategies; and (3) a co-writing session that enables users to draft and refine their counterspeech with CounterQuill. We conducted a within-subjects user study with 20 participants to evaluate CounterQuill in comparison to ChatGPT. Results show that CounterQuill's guidance and collaborative writing process provided users a stronger sense of ownership over their co-authored counterspeech. Users perceived CounterQuill as a writing partner and thus were more willing to post the co-written counterspeech online compared to the one written with ChatGPT.

Deep fake videos of President Joe Biden and Republican frontrunner Donald Trump highlight how the 2024 presidential race could be the first serious test of American democracy's resilience to artificial intelligence. Videos of Biden dressed as trans star Dylan Mulvaney promoting Bud Light and Trump teaching tax evasion inside a quiet Albuquerque nail salon show that not even the nation's most powerful figures are safe from AI identity theft. Experts say that while today it is relatively easy to spot these fakes, it will be impossible in the coming years because technology is advancing at such a fast pace. There have already been glimpses of the real-world harms of AI. Just earlier this week, an AI-crafted image of black smoke billowing out of the Pentagon sent shockwaves through the stock market before media factcheckers could finally correct the record.

Artificial intelligence's rapid growth has led to advancements like autonomous vehicles, virtual reality, and ChatGPT. But AI technologies and training AI models require a lot of energy, increasing concerns about the environmental impact of AI and its sustainability. To put AI's energy usage into perspective, it took nine days to train one of OpenAI's early model chatbots, MegatronLM. According to TechTarget, during those nine days, 27,648 kilowatt hours of energy was used. That's about the same amount of energy used by three U.S. homes over the course of an entire year.

The rise of hate speech on online platforms has led to an urgent need for effective content moderation. However, the subjective and multi-faceted nature of hateful online content, including implicit hate speech, poses significant challenges to human moderators and content moderation systems. To address this issue, we developed ToxVis, a visually interactive and explainable tool for classifying hate speech into three categories: implicit, explicit, and non-hateful. We fine-tuned two transformer-based models using RoBERTa, XLNET, and GPT-3 and used deep learning interpretation techniques to provide explanations for the classification results. ToxVis enables users to input potentially hateful text and receive a classification result along with a visual explanation of which words contributed most to the decision. By making the classification process explainable, ToxVis provides a valuable tool for understanding the nuances of hateful content and supporting more effective content moderation. Our research contributes to the growing body of work aimed at mitigating the harms caused by online hate speech and demonstrates the potential for combining state-of-the-art natural language processing models with interpretable deep learning techniques to address this critical issue. Finally, ToxVis can serve as a resource for content moderators, social media platforms, and researchers working to combat the spread of hate speech online.

Now is the time to talk, to put in place standards and regulations to mitigate the risk of a society ... [ ] based on surveillance and other nightmarish scenarios. Artificial Intelligence has become commonplace in the lives of billions of people globally. Research shows that 56% of companies have adopted AI in at least one function, especially in emerging nations. AI is used in everything from optimizing service operations through to recruiting talent. It can capture biometric data and it already helps in medical applications, judicial systems, and finance, thus making key decisions in people's lives. But one huge challenge remains to regulate its use.

Have you ever lost your grip on something that you've dropped into the swimming pool, or worse, toilet? Scientists may have developed a solution to holding onto underwater objects, but it is not primarily intended to help you rescue your iPhone from a watery fate. Researchers at Virginia Tech have developed a glove that will allow divers to get a firm grasp while, for example, rescuing someone or salvaging a shipwreck. The'octa-glove' is inspired by octopus tentacles, and is covered in robotic suckers equipped with sensors that can tell how far away an object is. When the sensors detect a nearby surface, it sends a signal to the controller which will activate the sucker's adhesion.