We propose novel "clustering and conquer" procedures for the parallel large-scale ranking and selection (R&S) problem, which leverage correlation information for clustering to break the bottleneck of sample efficiency. In parallel computing environments, correlation-based clustering can achieve an $\mathcal{O}(p)$ sample complexity reduction rate, which is the optimal reduction rate theoretically attainable. Our proposed framework is versatile, allowing for seamless integration of various prevalent R&S methods under both fixed-budget and fixed-precision paradigms. It can achieve improvements without the necessity of highly accurate correlation estimation and precise clustering. In large-scale AI applications such as neural architecture search, a screening-free version of our procedure surprisingly surpasses fully-sequential benchmarks in terms of sample efficiency. This suggests that leveraging valuable structural information, such as correlation, is a viable path to bypassing the traditional need for screening via pairwise comparison--a step previously deemed essential for high sample efficiency but problematic for parallelization. Additionally, we propose a parallel few-shot clustering algorithm tailored for large-scale problems.

Researchers at Rochester Institute of Technology are using augmented and virtual reality as part of a modern training platform to help address the skilled labor shortage in manufacturing. Traditional positions in the manufacturing industry are evolving and require new skill sets to manage processes and equipment. Today's Industry 4.0 and advanced manufacturing operations require knowledge about smart technologies and automation. Using artificial intelligence (AI) and augmented or virtual reality (AR/VR) applications as a training strategy can support novice trainees in learning critical manufacturing skills, said Yunbo Zhang, an assistant professor in RIT's Kate Gleason College of Engineering and part of the research team developing the technology platform. Developing smart technology solutions can also be a means to retain the knowledge of master machinists and manufacturing engineers.