Sarah Masud is a fifth-year PhD scholar at the Laboratory for Computational Social Systems (LCS2) at the Indraprastha Institute of Information Technology, Delhi (IIIT-D). She holds the prestigious Google PhD Fellowship (2023-present) was previously awarded the Prime Minister's Doctoral Fellowship (2020-2023). As part of her PhD, she has authored publications in top-tier venues, addressing the analysis of hateful content in online forums. AI Membership Committee and is a Journal of Open Source Software reviewer. Before her academic pursuits, Sarah worked as a data scientist in developer tooling at Red Hat, Bangalore, for 2.5 years.

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.

In Part 1 of this technical series, we covered how SingularityNET will make it possible for any non-professional user to create, educate, train, and launch their own personal AI agent. In Part 2, we covered some proposed integrations with the OpenCog platform. Now, let's get to the social computing aspect of SingularityNET, with reputation management being a critical component. The reliability of a decentralized financial system such as blockchain relies on distributed consensus. But designing a functional reputation system remains one of the key challenges for any social computing platform. To address both needs, the SingularityNET team has designed a Proof-of-Reputation consensus.

The program included four workshops, Computational Personality Recognition (Shared Task) (WS-13-01), Social Computing for Workforce 2.0 (WS-13-02), Social Media Visualization 2 (WS-13-03), and When the City Meets the Citizen (WS-13-04). The Workshop on Computational Personality Recognition allowed participants to compare the results of their systems on a common benchmark. Unlike competitive shared tasks, the workshop did not focus just on performance, but rather on discovering which feature sets, resources, and learning techniques are useful in the extraction of personality from text. Organizers provided two gold-standard labeled data sets (released 1 February 2013): essays.zip, Participants were required to use at least one of the data sets provided by the organizers for their experiments; provide the files used for the experiments; and submit a short paper reporting all the information about features, resources, and techniques used in the experiments, and discussing results.

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.

There is a growing trend towards the convergence of cyber-physical systems (CPS) and social computing, which will lead to the emergence of smart communities composed of various objects (including both human individuals and physical things) that interact and cooperate with each other. These smart communities promise to enable a number of innovative applications and services that will improve the quality of life. This position paper addresses some opportunities and challenges of building smart communities characterized by cyber-physical and social intelligence.

Recently, the use of social and human computing has witnessed increasing interest in the AI community. However, in order to harness the true potential of social computing, human subjects must play an active role in achieving computation in social networks and related media. Our work proposes an initial desiderata for effective social computing, drawing inspiration from artificial intelligence. Extensive experimentation reveals that several open issues and research questions have to be answered before the true potential of social and human computing is achieved. We, however, take a somewhat novel approach, by implementing a social networks environment where human subjects cooperate towards computational problem solving. In our social environment, human and artificial agents cooperate in their computation tasks,which may lead to a single problem-solving social network that potentially allows seamless cooperation among human and machine agents.