Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing.

"Digital transformation since the pandemic has been massive. Telehealth has gone from being a novelty to a necessity. Having said that, we need to be reliant on healthcare institutions to get cured and we need technology to make it better," said Mitali Dutta, head of data science and predictive analysis, group IT information and data management, Philips Innovation Campus, at her session at The Rising 2021 by Analytics India Magazine. She pointed at a rise in chronic diseases and healthcare costs, a scarcity of healthcare professionals in India. According to Dutta, the healthcare industry has to focus on the following four aspects, which she called Quadruple AIM of India's healthcare system: To achieve all the above, India needs artificial intelligence.

Having studied and worked in the field of machine learning and artificial intelligence for over 25 years, Professor Jeff Bilmes has a different view of the field than many people have heard. Recently, he has been excited by the science of information management as it relates to machine learning–in other words, how to make large data sets smaller and more efficient. This is important for AI and machine learning, as the field is, at its core, about how to teach computers to solve complex tasks. Large and inefficient data sets make it more difficult for this to occur and significantly add to the cost of teaching computers with indirect algorithms. That the field has come so far in such a short time is due to three factors–big data and big information, large amounts of commodity vectors for supercomputing, including GPUs, and expressive mathematical "deep" models–but there is still much work to be done.