Adm. Daryl Caudle joins'America's Newsroom' to discuss rising tensions with China's navy, the use of AI in US defense, and a powerful Memorial Day re-enlistment ceremony at Ground Zero. The U.S. Navy has called off a search for a sailor assigned to the USS George Washington amid reports that he possibly went overboard while the ship was sailing north of Australia. The sailor was reported overboard on the aircraft carrier on Monday as the ship was transiting the Timor Sea, the Navy said. US DEFENSE OFFICIAL REACTS TO IRAN'S CLAIMS ABOUT ENCOUNTER WITH WARSHIP This photo shows a general view of U.S. aircraft carrier USS George Washington shortly after berthing at Manila Bay in Manila on July 3. (TED ALJIBE/AFP via Getty Images) The search effort involving the George Washington, its carrier strike group, as well as the Australian Defence (sic) Force and Australian Border Force, concluded at 12:40 p.m. Wednesday. "USS George Washington expresses sincere condolences to those impacted by this loss and is actively engaged with the crew to make services available to tend to their needs during this challenging time," Lt. Cmdr.

In support of open and reproducible research, there has been a rapidly increasing number of datasets made available for research. As the availability of datasets increases, it becomes more important to have quality metadata for discovering and reusing them. Yet, it is a common issue that datasets often lack quality metadata due to limited resources for data curation. Meanwhile, technologies such as artificial intelligence and large language models (LLMs) are progressing rapidly. Recently, systems based on these technologies, such as ChatGPT, have demonstrated promising capabilities for certain data curation tasks. This paper proposes to leverage LLMs for cost-effective annotation of subject metadata through the LLM-based in-context learning. Our method employs GPT-3.5 with prompts designed for annotating subject metadata, demonstrating promising performance in automatic metadata annotation. However, models based on in-context learning cannot acquire discipline-specific rules, resulting in lower performance in several categories. This limitation arises from the limited contextual information available for subject inference. To the best of our knowledge, we are introducing, for the first time, an in-context learning method that harnesses large language models for automated subject metadata annotation.

Large language models (LLMs) have shown remarkable capabilities across a broad range of tasks involving question answering and the generation of coherent text and code. Comprehensively understanding the strengths and weaknesses of LLMs is beneficial for safety, downstream applications and improving performance. In this work, we investigate the degree to which GPT-4 has acquired factual geographic knowledge and is capable of using this knowledge for interpretative reasoning, which is especially important for applications that involve geographic data, such as geospatial analysis, supply chain management, and disaster response. To this end, we design and conduct a series of diverse experiments, starting from factual tasks such as location, distance and elevation estimation to more complex questions such as generating country outlines and travel networks, route finding under constraints and supply chain analysis. We provide a broad characterisation of what GPT-4 (without plugins or Internet access) knows about the world, highlighting both potentially surprising capabilities but also limitations.