- Integer Programming and AI: Fred Glover's research discusses the future of integer programming and its links to artificial intelligence. The paper
explores four key areas: controlled randomization, learning strategies, induced decomposition, and tabu search. These areas have characteristics that appear usefully relevant to developments on the horizon.
- Reinforcement Learning from Human Feedback: RLHF
provides a framework to align Large Language Models with humans. By incorporating human feedback, RLHF helps guide LLMs from generating harmful or incorrect outputs. It is anticipated that RLHF will prove to be an effective way to optimize smaller LLMs for specific applications, leading to more efficient and tailored language generation capabilities.
- AI in Radiology: A study by Ahmed Hosny et al. discusses the application of artificial intelligence to image-based tasks in radiology. AI methods excel at automatically recognizing complex patterns in imaging data
and providing quantitative, rather than qualitative, assessments of radiographic characteristics.
- AI in Healthcare: Fei Jiang and colleagues review the current status of AI applications in healthcare and discuss
its future. AI can be applied to various types of healthcare data (structured and unstructured). Major disease areas that use AI tools include cancer, neurology, and cardiology.
- Explanation in AI: Tim Miller's research focuses on explainable artificial intelligence. The paper argues that the field of explainable
artificial intelligence can build on existing research in philosophy, psychology, and cognitive science.
- Explainable AI (XAI): Amina Adadi and Mohammed Berrada's survey
provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI.
