There are several recent developments in the field of Artificial Intelligence (AI) which showcase its rapid advancement and the need for robust evaluation methods. OpenAI, a leading AI research company, is reportedly developing new strategies to address the slowdown in AI model improvements. Researchers from Bloomberg and UNC Chapel Hill have introduced M3DocRAG, a novel multi-modal RAG framework for Document Visual Question Answering (DocVQA). This framework aims to improve AI’s ability to understand complex documents with text, images, and tables. The increasing accuracy of AI models has prompted several companies to create their own internal benchmarks, as public tests are becoming inadequate to gauge the capabilities of advanced models. The need for more rigorous and comprehensive evaluations reflects the evolving nature of AI research and the increasing complexity of AI systems.
The SPAR program is a remote-first part-time program connecting mentors and mentees for three-month AI safety and governance research projects. This initiative aims to promote research in areas such as technical AI safety, AI policy and governance, AI strategy, and AI security. The program offers funding for compute costs and provides valuable experience for both mentors and mentees. The focus on AI safety and governance is increasingly relevant in the rapidly evolving landscape of artificial intelligence. The SPAR program plays a crucial role in fostering research and development within the AI community.
The STORM AI research system, developed by Stanford University, leverages Large Language Models (LLMs) to conduct research using perspective-guided question asking in simulated conversations. Initially designed for web-based research, STORM now supports searching local document vector stores, allowing users to analyze internal data sets like the United States Federal Emergency Management Agency (FEMA) disaster preparedness and assistance documentation. This powerful research system mimics human research methods by iteratively refining document retrieval and synthesis, ultimately generating rich articles that can be used by humans in their pre-writing research.