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upstart writes:

Citizen science, supercomputers and AI:

Citizen scientists have helped researchers discover new types of galaxies, design drugs to fight COVID-19, and map the bird world. The term describes a range of ways that the public can meaningfully contribute to scientific and engineering research, as well as environmental monitoring.

As members of the Computing Community Consortium (CCC) recently argued in a Quadrennial Paper, "Imagine All the People: Citizen Science, Artificial Intelligence, and Computational Research," non-scientists can help advance science by "providing or analyzing data at spatial and temporal resolutions or scales and speeds that otherwise would be impossible given limited staff and resources."

Recently, citizen scientists' efforts have found a new purpose: helping researchers develop machine learning models, using labeled data and algorithms, to train a computer to solve a specific task.

This approach was pioneered by the crowdsourced astronomy project Galaxy Zoo, which started leveraging citizen scientists in 2007. In 2019, researchers used labeled data to train a neural network model to classify hundreds of millions of unlabeled galaxies.

"Using the millions of classifications carried out by the public in the Galaxy Zoo project to train a neural network is an inspiring use of the citizens science program," said Elise Jennings, a computer scientist at Argonne Leadership Computing Facility (ALCF) who contributed to the effort.

TACC is supporting a number of projects—from identifying fake news to pinpointing structures in danger during natural hazards—that use citizen science to train AI models and enable new scientific successes.

[...] Citizen science is as old as science itself, and yet it has more tricks to teach us, if we can learn to harness it properly. By employing cutting edge computational tools, citizenscience is poised to add even more value to the traditional scientific enterprise.

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