Its machine-learning algorithm was able to solve complex to distorted CAPTCHA text at a 99.8% accuracy while humans fell short at a miserly 33%. By solving all those CAPTCHAs, the AI eventually becomes so good at solving it, even better than humans.Ī classic case of student being better than the teacher is Google’s reCAPTCHA experiment back in 2014. However, CAPTCHA in itself is a tool for training AI. Ultimately, CAPTCHA is a filter or selectively permeable barrier meant to keep bots out and let humans in. In an intriguing variation, researchers in 2010 proposed using CAPTCHAs to index ancient petroglyphs, computers not being very good at deciphering gestural sketches of reindeer scrawled on cave walls.Early versions of CAPTCHA (2000s) Still the early version of CAPTCHA: in order to make it more difficult for bots the text became more obscured Adoption of images from Google’s Street View Use of natural images became a better option Use of adversarial images to throw off bots was adopted and is still very much in use. Researchers have looked into asking users to identify objects in Magic Eye-like blotches.
People have tried stymying image recognition by asking users to identify, say, pigs, but making the pigs cartoons and giving them sunglasses. Such cultural CAPTCHAs are aimed not just at bots, but at the humans working in overseas CAPTCHA farms solving puzzles for fractions of a cent. (You can imagine how well that went.) There have been proposals for trivia CAPTCHAs, and CAPTCHAs based on nursery rhymes common in the area where a user purportedly grew up.
Researchers have tried asking users to classify images of people by facial expression, gender, and ethnicity. The literature on CAPTCHA is littered with false starts and strange attempts at finding something other than text or image recognition that humans are universally good at and machines struggle with.
0 kommentar(er)
