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From Digital Twins to Digital Selves and Beyond : Engineering and Social Models for a Trans-humanist World
This book aims at deepening the understanding of the relation between cyber-physical systems (CPSs) as socio-technical systems and their digital representations with intertwined artificial intelligence (AI). The authors describe why it is crucial for digital selves to be able to develop emotional behavior and why a humanity-inspired AI is necessary so that humans and humanoids can coexist.
Fostering transformative change for sustainability in the context of socio-ecological production landscapes and seascapes (SEPLS)
This book is a compilation of case studies that provide useful knowledge and lessons that derive from on-the-ground activities and contribute to policy recommendations, focusing on the relevance of social-ecological production landscapes and seascapes (SEPLS) to “transformative change.” The concept of “transformative change” has been gaining more attention to deal with today’s environmental and development problems, whereas both policy and scientific communities have been increasingly calling for transformative change toward sustainable society. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has planned to start the so-called “assessment on transformative change” if approved by the IPBES plenary to be held in 2021
Data science, AI, and machine learning in drug development
The confluence of big data, AI, and machine learning has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R&D, emerging applications of big data, AI and machine learning in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations
