New-generation AI Foundations, Features and Futures
- X-AI and X-intelligences: such as behavior AI and intelligence, data AI and intelligence, domain intelligence, symbolic AI and intelligence, situated AI and intelligence, connectionist AI and intelligence, nature-inspired AI and intelligence, social AI and intelligence, organizational AI and intelligence, network intelligence, human intelligence and human-like AI, and their metasynthesis – metasynthetic AI; and human X-intelligences for goal, intention, emotion, sentiment, perception, vision, conversation, communication, behavior, action, learning, inference, decision, and reflection.
- X-intelligence metasynthesis: developing methods and mechanisms to synthesize diverse X-intelligences and X-AI, synthesizing human X-intelligences, domain intelligence, data intelligence, behavior intelligence, machine intelligence, network intelligence, social intelligence, and organizational intelligence; building consensus between X-AI and X-intelligences; and managing divergence and convergence between intelligences, etc., forming metasynthetic intelligent systems.
Trans-AI/Trans-DS for Science, Society, Engineering, and Beyond
- DSAI for nature and science: data science and AI (DSAI) for nature and science research and innovation, such as DSAI for natural science, astronomy, chemical science, material science, biological science, drug and medicine, and mathematics and statistics.
- DSAI for humanity, arts, health and society: DSAI enabling better humanity, health, arts, businesses, law, accounting, finance, management, and public services, etc.
- DSAI for engineering and technology: DSAI enabling better environment, ecology, electrical engineering, electronic engineering, mechanical engineering, design, manufacturing, transportation, communication, etc.
Longbing Cao. AI and Data Science for Smart Emergency, Crisis and Disaster Resilience. Int. J. Data Sci. Anal., 15(3): 231–246, 2023. BibTeX | |
Longbing Cao. AI in Combating the COVID-19 Pandemic. IEEE Intell. Syst. 37(2): pp. 3-13, (2022). BibTeX | |
Longbing Cao. AI in Finance: Challenges, Techniques and Opportunities, ACM Computing Surveys, 55(3):64, 1–38, 2022. BibTeX | |
Longbing Cao, Qiang Yang, Philip S. Yu: Data science and AI in FinTech: An overview. Int. J. Data Sci. Anal. 12(2): 81-99, 2021. BibTeX | |
Longbing Cao. AI in Finance: A Review, SSRN, 1-35, 2020. BibTeX |