Zooniverse: Web-Scale Citizen Science

Web-scale citizen science such as Zooniverse (www.zooniverse.org) has provided a temporary solution to the flood of data that confronts researchers of 21st century, however the solution is a short-term one. In this presentation I will outline a potential strategy for combining a large web community and significant compute resources to create a scalable, intelligent classification engine.

Transcript

1. Zooniverse: Web-Scale Citizen Science Arfon Smith @arfon
3. 1,000,000,000,000 1 trillion hours
4. Spectrum of cognitive surplus
6. http://www.novacelestia.com
12. 0 20 40 60 80 100 Professor
13. 0 2,500 5,000 7,500 10,000 Professor Paper
14. 0 12,500 25,000 37,500 50,000 Professor Paper PhD
15. 0 250,000 500,000 750,000 1,000,000 Professor Paper PhD SDSS
17. 680,000 volunteers
18. 250,000,000 analyses
24. (it works)
39. 0 25,000,000 50,000,000 75,000,000 100,000,000 Professor Paper PhD SDSS ?
41. Galaxy Zoo Supernovae
42. Supernova (3) Reference New Difference
43. Possible Supernova (1) Reference New Difference
44. Likely Junk (-1) Reference New Difference
45. Smith et al. - arXiv:1011.2199
46. Optimisations (potential)
47. 1. Accuracy/experience of the classifier 2. Difficulty of the object being classified 3. Uncertainty of the object’s classification 4. Difficulty of the task 5. Understand classifier motivation 6. Understand current ‘state’ of the classifier
48. Dynamic Bayesian Combination of Multiple Imperfect Classifiers Simpson et al. - arXiv:1206.1831
51. Classification strategies
54. Combining Human and Machine Intelligence in Large-scale Crowdsourcing Kamar, Hacker & Horvitz - http://is.gd/horvitz
56. Motivations
62. Reference New Difference
63. What are humans good at?
65. SDSS HST Starforming pea Narrow-line Seyfert pea
69. Social Computing Systems
70. Expensive Noisy Classifier agents
72. Thanks arfon@zooniverse.org @arfon