The world is full of extraordinary young people brimming with ideas for how to crack tough problems. Every year, we recognize 35 such individuals from around the world--all of whom are under the age of 35. These scientists, inventors, and entrepreneurs are working to help mitigate climate change, accelerate scientific progress, and alleviate human suffering from disease. Some are launching companies while others are hard at work in academic labs. They were selected from hundreds of nominees by expert judges and our newsroom staff. Get to know them all--including our 2025 Innovator of the Year-- in these profiles .

On the southeastern coast of Puerto Rico lies the country's only coal-fired power station, flanked by a mountain of toxic ash. The plant, owned by the utility giant AES, has long plagued this part of Puerto Rico with air and water pollution. Before the coal plant opened Guayama had on average just over 103 cancer cases per year. In 2003, the year after the plant opened, the number of cancer cases in the municipality surged by 50%, to 167. In 2022, the most recent year with available data, cases hit a new high of 209.

In a real-world RAG system, the current query often involves spoken ellipses and ambiguous references from dialogue contexts, necessitating query rewriting to better describe user's information needs. However, traditional context-based rewriting has minimal enhancement on downstream generation tasks due to the lengthy process from query rewriting to response generation. Some researchers try to utilize reinforcement learning with generation feedback to assist the rewriter, but these sparse rewards provide little guidance in most cases, leading to unstable training and generation results. We find that user's needs are also reflected in the gold document, retrieved documents and ground truth. Therefore, by feeding back these multi-aspect dense rewards to query rewriting, more stable and satisfactory responses can be achieved. In this paper, we propose a novel query rewriting method MaFeRw, which improves RAG performance by integrating multi-aspect feedback from both the retrieval process and generated results. Specifically, we first use manual data to train a T5 model for the rewriter initialization. Next, we design three metrics as reinforcement learning feedback: the similarity between the rewritten query and the gold document, the ranking metrics, and ROUGE between the generation and the ground truth. Inspired by RLAIF, we train three kinds of reward models for the above metrics to achieve more efficient training. Finally, we combine the scores of these reward models as feedback, and use PPO algorithm to explore the optimal query rewriting strategy. Experimental results on two conversational RAG datasets demonstrate that MaFeRw achieves superior generation metrics and more stable training compared to baselines.

This paper presents a comprehensive analysis of explainable fact-checking through a series of experiments, focusing on the ability of large language models to verify public health claims and provide explanations or justifications for their veracity assessments. We examine the effectiveness of zero/few-shot prompting and parameter-efficient fine-tuning across various open and closed-source models, examining their performance in both isolated and joint tasks of veracity prediction and explanation generation. Importantly, we employ a dual evaluation approach comprising previously established automatic metrics and a novel set of criteria through human evaluation. Our automatic evaluation indicates that, within the zero-shot scenario, GPT-4 emerges as the standout performer, but in few-shot and parameter-efficient fine-tuning contexts, open-source models demonstrate their capacity to not only bridge the performance gap but, in some instances, surpass GPT-4. Human evaluation reveals yet more nuance as well as indicating potential problems with the gold explanations.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Wisconsin lawmakers were set to vote Thursday on proposals to regulate artificial intelligence, joining a growing number of states grappling with how to control the technology as November's elections loom. The Assembly was scheduled to vote on a bipartisan measure to require political candidates and groups to include disclaimers in ads that use AI technology. Violators would face a 1,000 fine.

We've already seen OpenAI and Salesforce incorporate their standalone chatbots into larger, more comprehensive machine learning platforms that span the breadth and depth of their businesses. On Tuesday, Google announced that its Bard AI is receiving the same treatment and has been empowered to pull real-time data from other Google applications including Docs, Maps, Lens, Flights, Hotels and YouTube, as well as the users' own silo of stored personal data, to provide more relevant and actionable chatbot responses. "I've had the great fortune of being a part of the team from the inception," Jack Krawczyk,bproduct lead for Bard, told Engadget. "This Thursday marks six months since Bard entered into the world." But despite of the technology's rapid spread, Krawczyk concedes that many users remain wary of it, either because they don't see an immediate use-case for it in their personal lives or "some others are saying, 'I've also heard that it makes things up a lot.'"

This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources for rapid response. We use the United States Geological Survey (USGS) Landslide Inventory data annotated by experts after Hurricane Mar\'ia in Puerto Rico on Sept 20, 2017, and their subsequent susceptibility study which uses extensive additional information such as precipitation, soil moisture, geological terrain features, closeness to waterways and roads, etc. Since such data might not be available during other events or regions, we aimed to produce a landslide density map using only elevation and SAR data to be useful to decision-makers in rapid response scenarios. The USGS Landslide Inventory contains the coordinates of 71,431 landslide heads (not their full extent) and was obtained by manual inspection of aerial and satellite imagery. It is estimated that around 45\% of the landslides are smaller than a Sentinel-1 typical pixel which is 10m $\times$ 10m, although many are long and thin, probably leaving traces across several pixels. Our method obtains 0.814 AUC in predicting the correct density estimation class at the chip level (128$\times$128 pixels, at Sentinel-1 resolution) using only elevation data and up to three SAR acquisitions pre- and post-hurricane, thus enabling rapid assessment after a disaster. The USGS Susceptibility Study reports a 0.87 AUC, but it is measured at the landslide level and uses additional information sources (such as proximity to fluvial channels, roads, precipitation, etc.) which might not regularly be available in an rapid response emergency scenario.

Evolv Technology, the leader in weapons detection security screening, announced its newest partnership, with Distrito T-Mobile. The entertainment complex is Evolv's first customer in Puerto Rico, and reflects the company's growing presence outside of the continental United States. AI and ML News: Why SMBs Shouldn't Be Afraid of Artificial Intelligence (AI) "This partnership is exciting because not only does Evolv help visitors to Distrito T-Mobile stay safe, but the work we are doing together marks our expansion into Puerto Rico" Distrito T-Mobile, which opened in August 2021 and is located in the Miramar section of San Juan, drew more than 1.7 million visitors in its first six months of operation. Evolv's state-of-the-art Evolv Express weapons screening solution allows visitors to seamlessly walk into the 476,000 square-foot complex, without burdensome delays at the entrance. "Distrito T-Mobile is just getting started," said Francisco Mariani, Distrito T-Mobile's General Manager.

The size of monkey brains are influenced by social interactions, a new study revealed, finding more friends in a group leads to larger social regions in the brain. A team of researchers from the University of Pennsylvania in Philadelphia, studied the brains, and social interactions of a group of rhesus macaques living on Cayo Santiago, an island off the coast of Puerto Rico. They found that the number of social connections predicted the size of key nodes in parts of the brain responsible for social decision-making and empathy. Though all these findings relate specifically to free-ranging rhesus macaques, they have possible implications for human behavior, in particular to understanding neuro-developmental disorders like autism, according to the team. Researchers determined that, for macaques with more grooming partners, the mid–superior temporal sulcus (STS) and ventral-dysgranular insula grew larger.

They had spent many months honing an algorithm designed to steer an unmanned hot air balloon all the way from Puerto Rico to Peru. The balloon, controlled by its machine mind, kept veering off course. Salvatore Candido of Google's now-defunct Project Loon venture, which aimed to bring internet access to remote areas via the balloons, couldn't explain the craft's trajectory. His colleagues manually took control of the system and put it back on track. It was only later that they realised what was happening.