Crowd Computing

Crowd Computing is a research field at the intersection of computer science and data science. Crowd computing studies how large groups of people can solve complex tasks that are currently beyond the capabilities of artificial intelligence algorithms, and that cannot be solved by a single person alone. 
 
Crowd Computing involves the algorithmic engagement and coordination of people by means of Web-enabled platforms. These complex tasks are mainly focused on the creation, enrichment, and interpretation of data, making crowd computing a building block of data science. Examples of such tasks include the coordinated creation of data about real-world events when electronic sensors 
are not available; the annotation of existing data sets to create ground truth data for the training of machine learning algorithms; and the analysis and interpretation of Web data to identify or moderate inappropriate content (e.g., hate speech, or fake news). 
 
Crowd computing is an essential tool for any data-driven and AI company: from Meta to Microsoft, from Google to IBM, from Spotify to Pandora, all major companies employ crowd computing to fulfill their data needs, both by involving employees, and by reaching out to anonymous crowds through online marketplaces like Amazon Mechanical Turk, Appen, Prolific, and Toloka. 
 
The objective of the CS4145 Crowd Computing course is to introduce the scientific and  technical underpinnings of crowd computing, and to investigate how it can be used for computer science applications (e.g., information retrieval, machine learning, next-generation interfaces, and data mining) and for real-world applications (e.g., cultural heritage preservation, online knowledge creation, smart cities, etc.)