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}.

Automatic factuality verification of large language model (LLM) generations is becoming more and more widely used to combat hallucinations. A major point of tension in the literature is the granularity of this fact-checking: larger chunks of text are hard to fact-check, but more atomic facts like propositions may lack context to interpret correctly. In this work, we assess the role of context in these atomic facts. We argue that fully atomic facts are not the right representation, and define two criteria for molecular facts: decontextuality, or how well they can stand alone, and minimality, or how little extra information is added to achieve decontexuality. We quantify the impact of decontextualization on minimality, then present a baseline methodology for generating molecular facts automatically, aiming to add the right amount of information. We compare against various methods of decontextualization and find that molecular facts balance minimality with fact verification accuracy in ambiguous settings.

Among the key factors driving the economic divide in America is the rise of technology that has eliminated many jobs through automating manufacturing tasks. A new report from the Brookings Institution warns that, thanks to the rise of artificial intelligence, economic disparity between coastal cities and heartland regions is about to get even worse. The 2016 Presidential election served as a wake-up call to the economic effect that the automation of many routine jobs is "massively rearranging the nation's economic geography," says the report, written by Brookings Senior Fellow Mark Muro. "The 2016 election may go down as the first time society began to grasp the full implications of automation's potential to transform the physical world," Muro wrote. "As big, techy cities like New York, Washington, and the Bay Area seemed to increasingly inhabit a different world from the rest of America, the people and places that were'left behind' revolted."

Tesla Motors Inc. TSLA -1.46 % is changing the way its Autopilot system works following the fatal crash in May of a car that was driving under semi-autonomous control. The revision, which Tesla Chief Executive Elon Musk outlined Sunday, will depend more on radar signals to help guide Tesla vehicles along roadways, and adds safeguards to keep drivers engaged at high speed. The software updates will be rolled out within the next two weeks and delivered to vehicles over the air, he said. They will affect Tesla vehicles built since October 2014, before which the hardware used by Autopilot wasn't included. Autopilot, which uses cameras, radar and sensors to steer vehicles and adjust their speed, has come under scrutiny since the Florida crash that killed Tesla driver Joshua Brown in May, the first known death of a driver using the system.