scholarly journals Special Issue Editorial: Crowd AI for Good

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Frank O. Ostermann ◽  
Laure Kloetzer ◽  
Marisa Ponti ◽  
Sven Schade

This special issue editorial of Human Computation on the topic "Crowd AI for Good" motivates explorations at the intersection of artificial intelligence and citizen science, and introduces a set of papers that exemplify related community activities and new directions in the field.

2021 ◽  
pp. 263497952110427
Author(s):  
Anna Harris

How to render sensory memory? In this article, I speculate on the possibilities of textural methods which attend closely to textile forms, specifically embroidery, as a way to explore this enduring question in multimodal research. To open up concerns about bodily relations between humans, as well as the more-than-human bodies we share worlds with, this article focuses on sensory memory fragments of encounters with the microbial conglomerations of sourdough bread starter. I offer three bubbling, sour-sweet texts: 1) an archived auto-ethnographic account of learning how to make a sourdough starter; 2) a social-media inspired piece on the sticky home archives of quarantine; and 3) a future speculative citizen science project. These fragments co-exist with microbes I have embroidered on ancient linens. From the tangy strings of sourdough histories, and the tangled threads in cloth I draw concrete methodological suggestions for new directions in textural research projects, such as material fieldnotes and crafted data. In doing so, I join other authors in this special issue in the call for multimodal forms of ethnographic storytelling about sensory memory, in this case one that attends not only to messy entanglements with bodies but also their textural, material, layered histories extending into the depth of their surfaces.


AI Magazine ◽  
2012 ◽  
Vol 34 (1) ◽  
pp. 10 ◽  
Author(s):  
Steve Kelling ◽  
Jeff Gerbracht ◽  
Daniel Fink ◽  
Carl Lagoze ◽  
Weng-Keen Wong ◽  
...  

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.


2021 ◽  
Vol 13 (15) ◽  
pp. 2883
Author(s):  
Gwanggil Jeon

Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications [...]


2016 ◽  
Vol 6 (1) ◽  
pp. 1-3
Author(s):  
Yukiko I. Nakano ◽  
Roman Bednarik ◽  
Hung-Hsuan Huang ◽  
Kristiina Jokinen

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