Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

Artificial Intelligence (AI) plays a critical role in the advancement of autonomous driving. It is likely the main facilitator of high levels of automation, as there are certain technical issues that only seem to be resolvable through advanced AI systems, particularly those based on machine learning. However, the introduction of AI systems in the realm of driver assistance systems and automated driving systems creates new uncertainties due to specific characteristics of AI that make it a distinct technology from traditional systems developed in the field of motor vehicles. Some of these characteristics include unpredictability, opacity, self and continuous learning and lack of causality [1], among other horizontal features such as autonomy, complexity, overfitting and bias. As an example of the specificity that the introduction of AI systems in vehicles entails, the UNECE's Working Party on Automated/Autonomous and Connected Vehicles (GRVA) has been specifically discussing the impact of AI on vehicle regulations since 2020 [2].

We consider the problem of active learning in the context of spatial sampling for level set estimation (LSE), where the goal is to localize all regions where a function of interest lies above/below a given threshold as quickly as possible. We present a finite-horizon search procedure to perform LSE in one dimension while optimally balancing both the final estimation error and the distance traveled for a fixed number of samples. A tuning parameter is used to trade off between the estimation accuracy and distance traveled. We show that the resulting optimization problem can be solved in closed form and that the resulting policy generalizes existing approaches to this problem. We then show how this approach can be used to perform level set estimation in higher dimensions under the popular Gaussian process model. Empirical results on synthetic data indicate that as the cost of travel increases, our method's ability to treat distance nonmyopically allows it to significantly improve on the state of the art. On real air quality data, our approach achieves roughly one fifth the estimation error at less than half the cost of competing algorithms.

Pre-trained large language models (LLMs) have recently achieved better generalization and sample efficiency in autonomous web automation. However, the performance on real-world websites has still suffered from (1) open domainness, (2) limited context length, and (3) lack of inductive bias on HTML. We introduce WebAgent, an LLM-driven agent that learns from self-experience to complete tasks on real websites following natural language instructions. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites via Python programs generated from those. We design WebAgent with Flan-U-PaLM, for grounded code generation, and HTML-T5, new pre-trained LLMs for long HTML documents using local and global attention mechanisms and a mixture of long-span denoising objectives, for planning and summarization. We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks; achieving 18.7% higher success rate than the prior method on MiniWoB web automation benchmark, and SoTA performance on Mind2Web, an offline task planning evaluation.

Law makers ask President Obama for more water pumping from Sacramento Delta.

Wildfires are increasingly impacting the environment, human health and safety. Among the top 20 California wildfires, those in 2020-2021 burned more acres than the last century combined. California's 2018 wildfire season caused damages of $148.5 billion. Among millions of impacted people, those living with disabilities (around 15% of the world population) are disproportionately impacted due to inadequate means of alerts. In this project, a multi-modal wildfire prediction and personalized early warning system has been developed based on an advanced machine learning architecture. Sensor data from the Environmental Protection Agency and historical wildfire data from 2012 to 2018 have been compiled to establish a comprehensive wildfire database, the largest of its kind. Next, a novel U-Convolutional-LSTM (Long Short-Term Memory) neural network was designed with a special architecture for extracting key spatial and temporal features from contiguous environmental parameters indicative of impending wildfires. Environmental and meteorological factors were incorporated into the database and classified as leading indicators and trailing indicators, correlated to risks of wildfire conception and propagation respectively. Additionally, geological data was used to provide better wildfire risk assessment. This novel spatio-temporal neural network achieved >97% accuracy vs. around 76% using traditional convolutional neural networks, successfully predicting 2018's five most devastating wildfires 5-14 days in advance. Finally, a personalized early warning system, tailored to individuals with sensory disabilities or respiratory exacerbation conditions, was proposed. This technique would enable fire departments to anticipate and prevent wildfires before they strike and provide early warnings for at-risk individuals for better preparation, thereby saving lives and reducing economic damages.

People around the globe have suffered the nerve-wracking anxiety of waiting weeks or months to find out if their homes have been damaged by wildfires that scorch with increased intensity. Now, once the smoke has cleared for aerial photography, researchers have found a way to identify building damage within minutes. Through a system they call DamageMap, a team at Stanford University and the California Polytechnic State University (Cal Poly) has brought an artificial intelligence approach to building assessment: Instead of comparing before-and-after photos, they've trained a program using machine learning to rely solely on post-fire images. The findings appear in the International Journal of Disaster Risk Reduction. "We wanted to automate the process and make it much faster for first responders or even for citizens that might want to know what happened to their house after a wildfire," said lead study author Marios Galanis, a graduate student in the Civil and Environmental Engineering Department at Stanford's School of Engineering.

An illustration of a drone that sprays crops, the kind of gadget that Google saw as potentially useful for fighting fires. Google asked the US Federal Aviation Administration for permission to test a drone for monitoring and fighting fires. However, its drone plans, which were published Thursday in the federal register, have since been extinguished. The request came from Alphabet's Google Research Climate and Energy Group -- not the company's Wing subsidiary, whose drone delivery service was certified by the FAA in 2019. Wing drones are being used to deliver food and medicine during the coronavirus pandemic.

Lightning was a factor in many of these fires. But past blazes, including the 2018 Camp Fire that destroyed the town of Paradise, Calif., were started by faulty transmission equipment. In that case, a worn piece of metal that holds power lines, known as a C-hook, broke and dropped a high-voltage electric line that ignited that fire. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. In June, PG&E Corp., parent company of Pacific Gas and Electric Co., pleaded guilty to 84 counts of involuntary manslaughter for its role in sparking that fire.

Based on the last two wildfire seasons, including 2018 when an entire California town was destroyed, utilities blamed for recent wildfires need all the help they can get maintaining aging grids. AI technologies may provide new monitoring tools. Paradise, Calif., population of about 27,000, was destroyed by the Camp Fire. The 2018 inferno claimed at least 84 victims. In June, Pacific Gas & Electric (PG&E) was ordered to pay a $3.5 million fine for causing the Camp Fire.