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Attention in the AI safety community has increasingly started to include strategic considerations of coordination between relevant actors in the field of AI and AI safety, in addition to the steadily growing work on the technical considerations of building safe AI systems. This shift has several reasons: Multiplier effects, pragmatism, and urgency. Given the benefits of coordination between those working towards safe superintelligence, this book surveys promising research in this emerging field regarding AI safety. On a meta-level, the hope is that this book can serve as a map to inform those working in the field of AI coordination about other promising efforts. While this book focuses on AI safety coordination, coordination is important to most other known existential risks (e.g., biotechnology risks), and future, human-made existential risks. Thus, while most coordination strategies in this book are specific to superintelligence, we hope that some insights yield “collateral benefits” for the reduction of other existential risks, by creating an overall civilizational framework that increases robustness, resiliency, and antifragility.
strategic oversight --- multi-agent systems --- autonomous distributed system --- artificial superintelligence --- safe for design --- adaptive learning systems --- explainable AI --- ethics --- scenario mapping --- typologies of AI policy --- artificial intelligence --- design for values --- distributed goals management --- scenario analysis --- Goodhart’s Law --- specification gaming --- AI Thinking --- VSD --- AI --- human-in-the-loop --- value sensitive design --- future-ready --- forecasting AI behavior --- AI arms race --- AI alignment --- blockchain --- artilects --- policy making on AI --- distributed ledger --- AI risk --- Bayesian networks --- artificial intelligence safety --- conflict --- AI welfare science --- moral and ethical behavior --- scenario network mapping --- policymaking process --- human-centric reasoning --- antispeciesism --- AI forecasting --- transformative AI --- ASILOMAR --- judgmental distillation mapping --- terraforming --- pedagogical motif --- AI welfare policies --- superintelligence --- artificial general intelligence --- supermorality --- AI value alignment --- AGI --- predictive optimization --- AI safety --- technological singularity --- machine learning --- holistic forecasting framework --- simulations --- existential risk --- technology forecasting --- AI governance --- sentiocentrism --- AI containment
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As explored in this open access book, higher education in STEM fields is influenced by many factors, including education research, government and school policies, financial considerations, technology limitations, and acceptance of innovations by faculty and students. In 2018, Drs. Ryoo and Winkelmann explored the opportunities, challenges, and future research initiatives of innovative learning environments (ILEs) in higher education STEM disciplines in their pioneering project: eXploring the Future of Innovative Learning Environments (X-FILEs). Workshop participants evaluated four main ILE categories: personalized and adaptive learning, multimodal learning formats, cross/extended reality (XR), and artificial intelligence (AI) and machine learning (ML). This open access book gathers the perspectives expressed during the X-FILEs workshop and its follow-up activities. It is designed to help inform education policy makers, researchers, developers, and practitioners about the adoption and implementation of ILEs in higher education.
Statistics . --- Machine learning. --- Learning. --- Instruction. --- Knowledge representation (Information theory) . --- Statistics for Social Sciences, Humanities, Law. --- Machine Learning. --- Statistics and Computing/Statistics Programs. --- Learning & Instruction. --- Knowledge based Systems. --- Representation of knowledge (Information theory) --- Artificial intelligence --- Information theory --- Learning process --- Comprehension --- Education --- Learning, Machine --- Machine theory --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Educació STEM --- Educació superior --- Educació en Ciència, Tecnologia, Enginyeria i Matemàtiques --- Educació STEAM --- SMET (Educació) --- STEM (Educació) --- Science, Technology, Engineering and Mathematics Education --- Educació --- Ensenyament científic --- Educació tecnològica --- Educació universitària --- Ensenyament superior --- Ensenyament universitari --- Estudis superiors --- Estudis universitaris --- Etapes educatives --- Abandó dels estudis (Educació superior) --- Competències transversals --- Educació clàssica --- Ensenyament de la biblioteconomia --- Estudis de postgrau --- Extensió universitària --- Lectura (Educació superior) --- Orientació en l'educació superior --- Primer cicle d'ensenyament universitari --- Seminaris --- Tercer cicle d'ensenyament universitari --- Campus virtuals --- Escrits acadèmics --- Pràcticums --- Universitats --- Educació superior transfronterera --- Statistics for Social Sciences, Humanities, Law --- Machine Learning --- Statistics and Computing/Statistics Programs --- Learning & Instruction --- Knowledge based Systems --- Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy --- Statistics and Computing --- Innovative Learning Environments --- ILEs --- Science, Technology, Engineering, and Math --- STEM --- virtual reality --- VR --- augmented reality --- mixed reality --- cross reality --- extended reality --- artificial intelligence --- AI --- adaptive learning --- personalized learning --- higher education --- multimodal learning --- mobile learning --- Open Access --- Social research & statistics --- Mathematical & statistical software --- Teaching skills & techniques --- Cognition & cognitive psychology --- Expert systems / knowledge-based systems
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