Name
Abstract | ||
|---|---|---|
Machine learning is often used to automatically solve human tasks. In this paper, we look for tasks where machine learning algorithms are not as good as humans with the hope of gaining insight into their current limitations. We studied various Human Interactive Proofs (HIPs) on the market, because they are systems designed to tell computers and humans apart by posing challenges presumably too hard for computers. We found that most HIPs are pure recognition tasks which can easily be broken using machine learning. The harder HIPs use a combination of segmentation and recognition tasks. From this observation, we found that building segmentation tasks is the most effective way to confuse machine learning algorithms. This has enabled us to build effective HIPs (which we deployed in MSN Passport), as well as design challenging segmentation tasks for machine learning algorithms. |
Year | Venue | Keywords |
|---|---|---|
2004 | NIPS | system design,machine learning |
Field | DocType | Citations |
Multi-task learning,Active learning (machine learning),Segmentation,Computer science,Human interaction,Mathematical proof,Artificial intelligence,Computational learning theory,CAPTCHA,Machine learning | Conference | 96 |
PageRank | References | Authors |
5.33 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kumar Chellapilla | 1 | 951 | 62.13 |
| Patrice Y. Simard | 2 | 1112 | 155.00 |