KAUST researchers are using AI to analyze satellite imagery for the automated detection of ancient stone structures in northwest Saudi Arabia, including mustatils (rectangular structures dating to the late 6th millennium BCE) and ruins in circular and triangular shapes. They developed a deep learning algorithm trained on manually identified datasets to isolate similar features over a wide area. The tool converts detected pixels into geodetic coordinates using GPS, assembling them into an online map and database. Why it matters: This project exemplifies computational archaeology, speeding up archaeological discoveries, promoting cultural heritage, and providing a methodology useful to other sectors of the economy.
MBZUAI researchers release LLM-DetectAIve, a tool for fine-grained detection of machine-generated text across four categories: human-written, machine-generated, machine-written then humanized, and human-written then machine-polished. The tool aims to address concerns about misuse of LLMs, especially in education and academia, by identifying attempts to obfuscate or polish content. LLM-DetectAIve is publicly accessible with code and a demonstration video provided.
Researchers introduce TimeTravel, a benchmark dataset for evaluating large multimodal models (LMMs) on historical and cultural artifacts. The benchmark comprises 10,250 expert-verified samples across 266 cultures and 10 historical regions, designed to assess AI in tasks like classification and interpretation of manuscripts, artworks, inscriptions, and archaeological discoveries. The goal is to establish AI as a reliable partner in preserving cultural heritage and assisting researchers.