AI Science and Engineering: A New Field
Longbing Cao. IEEE Intell. Syst. 37(1): 3-13, 2022.
Access the paper at the IEEE Intell. Syst. website.

Discusses the emergence of artificial intelligence as a new field in engineering. AI first emerged in the 1950s and over the next few decades, experienced both advances and obstacles.1 However, there is now a new era of AI. The history of AI has been an acceleration from object intelligence (e.g., on symbol, behavior, and agent) to system intelligence (e.g., human, nature, and society), and from individual intelligence (e.g., learning intelligence) to metasynthetic intelligence (hybridizing and synthesizing intelligences). Building on the tumultuous AI evolutions, this new-generation AI is accelerating its pace of innovating, differentiating, transforming, and reshaping the world. The new-generation AI not only enables a smarter and more resilient humanity, well-being, and economy, but also everything else. What lessons can we learn from reviewing these AI advances and challenges? What makes AI science and engineering (AISE, or intelligent science and technology) a solid and comprehensive scientific field in addition to transforming other scientific and engineering disciplines and translating businesses and economy into their smart editions? What are the fundamental questions to be addressed in AISE? What forms the body of knowledge of AISE? What type of profile should AI professionals have to meet the requirements of AISE? What should AI education look like to produce qualified AI professionals? These questions deserve enduring, comprehensive, deep, creative, and critical thinking, ideas, and actions first and foremost to establish the AI field. Here, I share my limited view on AISE as a new discipline and the imperative developments, including the AI profession and AI education, to drive and enable the intelligent digital era and Industry 4.0. I hope my humble opinions will spur

A New Age of AI: Features and Futures
Longbing Cao. IEEE Intell. Syst. 37(1): 25-37, 2022.
Access the paper at the IEEE Intell. Syst. website.

By reviewing the 70 years of AI, this article summarizes and discusses the paradigm transformations from the age of AI before the year 2000 to the new age of AI from the year 2000 onward. It reviews the AI thinking and features of various AI generations and paradigms during these two ages of AI and their transformations. The paper further summarizes several AI Formulas from the AI vision, system, goal, task, and process perspectives. Several important areas are highlighted in developing AI Futures: shrinking the gaps between human, natural and social AI, and developing human-like/level AI, meta AI, reflective AI, metasynthetic AI, data-driven AI, beyond ‘IID AI,’ actionable AI, and sustainable AI. In the new age of AI, we encourage your deep thinking of AI futures.