JSMRAI, 2026
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Artificial intelligence is no longer a niche subfield of computer science. It’s the engine that powers breakthroughs in medicine, climate science, the arts, law, and virtually every discipline you can imagine. Yet the sheer volume and diversity of AI‑driven research make it hard to see the forest for the trees.
The catalogue for Artificial Intelligence and Multidisciplinary Research is a living, searchable map that links AI methods, datasets, tools, and real‑world applications across domains. In this section, we showcase different books and proceedings that show what the catalogue is, why it’s a game‑changer for scientists, policymakers, and industry, and how you can help build the next generation of cross‑disciplinary knowledge hubs from the materials provided, the prices are reasonable and well affordable.
Data-Centric Artificial Intelligence for Multidisciplinary Applications
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This book explores the need for a data‑centric AI approach and its application in the multidisciplinary domain, compared to a model‑centric approach. It examines the methodologies for data‑centric approaches, the use of data‑centric approaches in different domains, the need for edge AI and how it differs from cloud‑based AI. It discusses the new category of AI technology, "data‑centric AI" (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data‑centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods.
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