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}.

Self-consistency-based approaches, which involve repeatedly sampling multiple outputs and selecting the most consistent one as the final response, prove to be remarkably effective in improving the factual accuracy of large language models. Nonetheless, existing methods usually have strict constraints on the task format, largely limiting their applicability. In this paper, we present Integrative Decoding (ID), to unlock the potential of self-consistency in open-ended generation tasks. ID operates by constructing a set of inputs, each prepended with a previously sampled response, and then processes them concurrently, with the next token being selected by aggregating of all their corresponding predictions at each decoding step. In essence, this simple approach implicitly incorporates self-consistency in the decoding objective. Extensive evaluation shows that ID consistently enhances factuality over a wide range of language models, with substantial improvements on the TruthfulQA (+11.2%), Biographies (+15.4%) and LongFact (+8.5%) benchmarks. The performance gains amplify progressively as the number of sampled responses increases, indicating the potential of ID to scale up with repeated sampling.

People say, "A picture is worth a thousand words". Then how can we get the rich information out of the image? We argue that by using visual clues to bridge large pretrained vision foundation models and language models, we can do so without any extra cross-modal training. Thanks to the strong zero-shot capability of foundation models, we start by constructing a rich semantic representation of the image (e.g., image tags, object attributes / locations, captions) as a structured textual prompt, called visual clues, using a vision foundation model. Based on visual clues, we use large language model to produce a series of comprehensive descriptions for the visual content, which is then verified by the vision model again to select the candidate that aligns best with the image. We evaluate the quality of generated descriptions by quantitative and qualitative measurement. The results demonstrate the effectiveness of such a structured semantic representation.

Recent self-supervised approaches have used large-scale image-text datasets to learn powerful representations that transfer to many tasks without finetuning. These methods often assume that there is one-to-one correspondence between its images and their (short) captions. However, many tasks require reasoning about multiple images and long text narratives, such as describing news articles with visual summaries. Thus, we explore a novel setting where the goal is to learn a self-supervised visual-language representation that is robust to varying text length and the number of images. In addition, unlike prior work which assumed captions have a literal relation to the image, we assume images only contain loose illustrative correspondence with the text. To explore this problem, we introduce a large-scale multimodal dataset containing over 31M articles, 22M images and 1M videos. We show that state-of-the-art image-text alignment methods are not robust to longer narratives with multiple images. Finally, we introduce an intuitive baseline that outperforms these methods on zero-shot image-set retrieval by 10% on the GoodNews dataset.

The Sr. Solutions Architect – ML/AI focuses on defining the strategic architecture by understanding the enterprise analytics and translating it into actionable strategies that deliver low latency, highly scalable, cloud based Machine Learning and Artificial Intelligence. The Sr. Architect leads the data driven transformation of Foot Locker in partnership with members of the data, CX and infrastructure teams. This role has end-to-end responsibilities for our ML/AI/Cognitive platform - from design, thru technical specification, to delivery. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required.

New drone video out of Florida captured an aerial view of the devastation wrought by Hurricane Irma in the Sunshine State. The video, taken by Travis Long and posted by the Miami Herald Wednesday, showed Irma's path of destruction in Manatee County, south of Tampa on the west coast. The video showed enormous trees ripped out of the ground by their roots, roofs torn clean off homes and overturned and sunken boats. At least one person could be seen in the video working to restore a home amid the wreckage. President Donald Trump headed down to Florida Thursday to determine the extent of the damage left by the record-breaking hurricane.

For over 60 years, KFC restaurants have been serving the same secret original recipe to patrons. But Colonel Sanders is going against tradition with a new concept store located in Shanghai, China that lets customers order fried chicken from a voice activated robot. Dubbed'Dumi', the robot is smart enough to handle order changes and substitutes, but its creators say it cannot distinguish other dialects or accents. Colonel Sanders is going against tradition with a new concept store located in Shanghai, China that lets customers order fried chicken from a voice activated robot. Dubbed'Dumi', the high-tech automation can handle order changes, as well as substitutes – but it cannot distinguish other dialects or accents Dumi is a voice activated robot employed at a concept KFC store called'Original '.