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}.

Vision Language Models (VLMs) are rapidly advancing in their capability to answer information-seeking questions. As these models are widely deployed in consumer applications, they could lead to new privacy risks due to emergent abilities to identify people in photos, geolocate images, etc. As we demonstrate, somewhat surprisingly, current open-source and proprietary VLMs are very capable image geolocators, making widespread geolocation with VLMs an immediate privacy risk, rather than merely a theoretical future concern. As a first step to address this challenge, we develop a new benchmark, GPTGeoChat, to test the ability of VLMs to moderate geolocation dialogues with users. We collect a set of 1,000 image geolocation conversations between in-house annotators and GPT-4v, which are annotated with the granularity of location information revealed at each turn. Using this new dataset, we evaluate the ability of various VLMs to moderate GPT-4v geolocation conversations by determining when too much location information has been revealed. We find that custom fine-tuned models perform on par with prompted API-based models when identifying leaked location information at the country or city level; however, fine-tuning on supervised data appears to be needed to accurately moderate finer granularities, such as the name of a restaurant or building.

Transformer networks have revolutionized NLP representation learning since they were introduced. Though a great effort has been made to explain the representation in transformers, it is widely recognized that our understanding is not sufficient. One important reason is that there lack enough visualization tools for detailed analysis. In this paper, we propose to use dictionary learning to open up these "black boxes" as linear superpositions of transformer factors. Through visualization, we demonstrate the hierarchical semantic structures captured by the transformer factors, e.g., word-level polysemy disambiguation, sentence-level pattern formation, and long-range dependency. While some of these patterns confirm the conventional prior linguistic knowledge, the rest are relatively unexpected, which may provide new insights. We hope this visualization tool can bring further knowledge and a better understanding of how transformer networks work. The code is available at https://github.com/zeyuyun1/TransformerVis

In this paper, we argue that AI ethics must move beyond the concepts of race-based representation and bias, and towards those that probe the deeper relations that impact how these systems are designed, developed, and deployed. Many recent discussions on ethical considerations of bias in AI systems have centered on racial bias. We contend that antiblackness in AI requires more of an examination of the ontological space that provides a foundation for the design, development, and deployment of AI systems. We examine what this contention means from the perspective of the sociocultural context in which AI systems are designed, developed, and deployed and focus on intersections with anti-Black racism (antiblackness). To bring these multiple perspectives together and show an example of antiblackness in the face of attempts at de-biasing, we discuss results from auditing an existing open-source semantic network (ConceptNet). We use this discussion to further contextualize antiblackness in design, development, and deployment of AI systems and suggest questions one may ask when attempting to combat antiblackness in AI systems.

It makes me hear phones ringing at your local search marketing agency, with aspiring chefs and restaurateurs on the other end of the line, ready to bring experts aboard in the "sometimes difficult" quest for online visibility. Is your team ready for these clients? How comfortable do you feel talking restaurant Local SEO when such calls come in? When was the last time you took a broad survey of what's really ranking in this specialized industry? Allow me to be your prep cook today, and I'll dice up "best restaurant" local packs for major cities in all 50 US states. We'll julienne Google Posts usage, rough chop DA, make chiffonade of reviews, owner responses, categories, and a host of other ingredients to determine which characteristics are shared by establishments winning this most superlative of local search phrases.

Robots have been around, in some form or other, since the ancient world. Early legends of automata existed in Greek and Roman legends and basic mechanical'robots' were designed and built in China and Greece. Our modern concept of robots wouldn't appear until the Industrial Revolution with the notion of the android (humanoid robot) coming into existence in 20th Century film and sci-fi literature. Since then many engineers have worked tirelessly to improve and, in some cases, redefine robotics. These 15 are just some of them. The following is far from exhaustive and is in no particular order. Their contribution to robotics: Joseph Endgelberger is widely credited for the birth of the robotics industry.

Rufus Thompson, left, is accused of kidnapping and murdering Danny Diaz-Delgado near Trenton, N.J. last month. A New Jersey man has been arrested and accused of kidnapping and murdering a man trying to buy a PlayStation video game console that was advertised online, according to multiple reports. Rufus Thompson, 29, was arrested Saturday morning in Trenton. He is charged with murder, felony murder, robbery, kidnapping and weapons offenses in the death of 20-year-old Danny Diaz-Delgado. The Trentonian reported that Diaz-Delgado's body was found March 24 near the banks of Assunpink Creek in Hamilton Township.

In 1961, the world's first industrial robot clocked in at a General Motors plant in Trenton, New Jersey. The 4,000-pound mechanical arm, named Unimate, was designed to weld cars and lift big pieces of metal. The robot was a huge success--even landing a spot on the Tonight Show with Johnny Carson. Get the latest from Kellogg Insight delivered to your inbox. Almost sixty years later, the emergence of artificial intelligence (AI) has seen machines leap from physical to mental labor.

In 1961, the world's first industrial robot clocked in at a General Motors plant in Trenton, New Jersey. The 4,000-pound mechanical arm, named Unimate, was designed to weld cars and lift big pieces of metal. The robot was a huge success--even landing a spot on the Tonight Show with Johnny Carson. Almost sixty years later, the emergence of artificial intelligence (AI) has seen machines leap from physical to mental labor. As computers step into roles that involve reasoning, a new wave of industries ranging from medicine to finance stands to benefit from--or get left behind by--AI.

It's long been clear that urbanization and automated technologies are shaping society, but it hasn't been obvious how the two forces affect each other. A new study from MIT's Media Lab posits that the smaller the city, the greater the impact it faces from automation. The finding, they say, could encourage legislators to pay special attention to workers in smaller cities and offer them support services. Other researchers have attempted to measure the effect of technology on employment in cities, but the Media Lab authors, who have identified which jobs and skills tend to be more prevalent in smaller cities and larger ones, claim to be the first to explain why different U.S. cities are more susceptible (or resilient) to technological unemployment. They say that bigger cities have a disproportionately large number of jobs for people who do cognitive and analytical tasks, such as software developers and financial analysts--occupations that are less likely to be disrupted by automation.