Human-based computation

Human-based computation (HBC), human-assisted computation, ubiquitous human computing or distributed thinking (by analogy to distributed computing) is a computer science technique in which a machine performs its function by outsourcing certain steps to humans, usually as microwork. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human–computer interaction. For computationally difficult tasks such as image recognition, human-based computation plays a central role in training Deep Learning-based Artificial Intelligence systems. In this case, human-based computation has been referred to as human-aided artificial intelligence. In traditional computation, a human employs a computer to solve a problem; a human provides a formalized problem description and an algorithm to a computer, and receives a solution to interpret. Human-based computation frequently reverses the roles; the computer asks a person or a large group of people to solve a problem, then collects, interprets, and integrates their solutions. This turns hybrid networks of humans and computers into "large scale distributed computing networks". where code is partially executed in human brains and on silicon based processors. Human-based computation (apart from the historical meaning of "computer") research has its origins in the early work on interactive evolutionary computation (EC). The idea behind interactive evolutionary algorithms is due to Richard Dawkins. In the Biomorphs software accompanying his book The Blind Watchmaker (Dawkins, 1986) the preference of a human experimenter is used to guide the evolution of two-dimensional sets of line segments. In essence, this program asks a human to be the fitness function of an evolutionary algorithm, so that the algorithm can use human visual perception and aesthetic judgment to do something that a normal evolutionary algorithm cannot do. However, it is difficult to get enough evaluations from a single human if we want to evolve more complex shapes.

Learning rich optical embeddings for privacy-preserving lensless image classification

Martin Vetterli, Eric Bezzam, Matthieu Martin Jean-André Simeoni

By replacing the lens with a thin optical element, lensless imaging enables new applications and solutions beyond those supported by traditional camera design and post-processing, e.g. compact and lightweight form factors and visual privacy. The latter ari ...

2022

Understanding generalization and robustness in modern deep learning

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Learning rich optical embeddings for privacy-preserving lensless image classification

Understanding generalization and robustness in modern deep learning

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Learning rich optical embeddings for privacy-preserving lensless image classification