The current U.S. administration has launched a wara on academia. Indirect costs, or, more accurately, facility and administration expenses, support research but cannot be directly attributed to a specific project, such as lab infrastructure, utilities, and administrative support. These are real costs; the limit, which has since been suspended by courts, is a severe blow to biomedical research in the U.S. Beyond expanding this limit to other agencies, such as the National Science Foundation (NSF), the administration is also reportedly considering slashing NSF's annual budget from approximately US 9 billion down to about US 3– 4 billion. This would deal a devastating blow to academic U.S. research, especially computing research. As statedc by the Computing Research Association (CRA), "NSF budget cuts would put the future of U.S. innovation and security at risk."

HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and systems, published over the last two decades. From this review, we identified seven themes associated with the evolution of a project: from establishing community partnerships to sustaining results. Our findings suggest that several tensions characterize these projects, many of which relate to the power and position of researchers, and the computing research environment, relative to community partners. We discuss the implications of our findings and offer methodological proposals to guide HCI, and computing research more broadly, towards practices that center communities.

Along with the development of modern computing technology and social sciences, both theoretical research and practical applications of social computing have been continuously extended. In particular with the boom of artificial intelligence (AI), social computing is significantly influenced by AI. However, the conventional technologies of AI have drawbacks in dealing with more complicated and dynamic problems. Such deficiency can be rectified by hybrid human-artificial intelligence (H-AI) which integrates both human intelligence and AI into one unity, forming a new enhanced intelligence. H-AI in dealing with social problems shows the advantages that AI can not surpass. This paper firstly introduces the concept of H-AI. AI is the intelligence in the transition stage of H-AI, so the latest research progresses of AI in social computing are reviewed. Secondly, it summarizes typical challenges faced by AI in social computing, and makes it possible to introduce H-AI to solve these challenges. Finally, the paper proposes a holistic framework of social computing combining with H-AI, which consists of four layers: object layer, base layer, analysis layer, and application layer. It represents H-AI has significant advantages over AI in solving social problems.

Checks are needed to guide the development of guard-rails for ethical and responsible community-engaged computing research. The era of "move fast and break things" can produce false starts, injured communities, and widespread techlash. The tech sector can be more socially conscious and focus on community engagement using research from universities, computing researchers, and professionals. For example, smart cities might increase efficiency and improve quality of life, but for whom?10 Research shows how smart city initiatives can harm certain groups through, for example, facial recognition technologies that misidentify, produce ethnic bias and discrimination, or create opportunities for abuse.5 Technology benefits do not always accrue evenly across community members. Ethics rarely keeps pace with technological innovation.

Further, TCS eats its own dog food--it has deployed a home-grown enterprise social media platform7 and more recently a deep-learning-based conversational system across all its 400K employees.c There have been many lessons learned along this journey; we mention some critical ones here: First, research initiatives have always preceded their applicability, and so continuing to invest in research areas seemingly unrelated to the current business pays off in initially unforeseen ways. For example, research in genome-based early prediction of rare diseases4 later enabled TCS' business to build genome analysis pipelines for pharma customers. Deep expertise in computational chemistry3 is now allowing TCS to design new chemical formulations and molecules for customers, a very different kind of service that could potentially expand the very scope of its core business in the future.

This report, written for the general computing and scientific audience and for students and others interested in artificial intelligence, summarizes the major directions in artificial intelligence research, sets them in context relative to other areas of computing research, and gives a glimpse of the vision, depth, research partnerships, successes, and excitement of the field. This article is reprinted with permission from ACM Computing Surveys 28(4), December 1996. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.

Artificial intelligence (AI) has been one of the most controversial domains of inquiry in computer science since it was first proposed in the 1950s. Defined as the part of computer science concerned with designing systems that exhibit the characteristics associated with human intelligence--understanding language, learning, reasoning, solving problems, and so on (Barr and Feigenbaum, 1981)--the field has attracted researchers because of its ambitious goals and enormous underlying intellectual challenges. The field has been controversial because of its social, ethical, and philosophical implications. Such controversy has affected the funding environment for AI and the objectives of many research programs. AI research is conducted by a range of scientists and technologists with varying perspectives, interests, and motivations.

This article was reprinted with permission from "ACM Computing Surveys: 28(4): December 1996, 653-670. original is available from ACM.