They have released no code and no paper, and failed at the demo that ScienceNOW asked for.
Here's their explanation for the failure, quoted in the article:
> So why didn’t our demo work on the checkerboard pattern? Before the image is presented to our algorithm, it has to pass through a retina+LGN kind of processing (it is a basic, common-to-all-CAPTCHAS pre-processing). In the demo, we had a specialized retina which was faster and gave a few percentage points more accuracy on reCAPTCHA; the downside being it not working well on the checkerboard pattern. (Or any pattern where some portions of the letter are black and some portions of the letter are white). Its just like putting on sunglasses being specialization we add to our eyes for going out in the sun, which causes us to see some other patterns not as well. It is not a fundamental flaw and we have tested that our system works with the checkerboard pattern as well
That's a lot of fancy words to say that they overfit to their training data.
That makes this sound like a very typical result in supervised machine learning (if it's a result at all). They have used an algorithm to learn a brittle heuristic that works in the cases it was trained to work on. Whether they have a Recursive Cortical Network™ that Emulates The Human Brain™ is irrelevant.
Here's their explanation for the failure, quoted in the article:
> So why didn’t our demo work on the checkerboard pattern? Before the image is presented to our algorithm, it has to pass through a retina+LGN kind of processing (it is a basic, common-to-all-CAPTCHAS pre-processing). In the demo, we had a specialized retina which was faster and gave a few percentage points more accuracy on reCAPTCHA; the downside being it not working well on the checkerboard pattern. (Or any pattern where some portions of the letter are black and some portions of the letter are white). Its just like putting on sunglasses being specialization we add to our eyes for going out in the sun, which causes us to see some other patterns not as well. It is not a fundamental flaw and we have tested that our system works with the checkerboard pattern as well
That's a lot of fancy words to say that they overfit to their training data.
That makes this sound like a very typical result in supervised machine learning (if it's a result at all). They have used an algorithm to learn a brittle heuristic that works in the cases it was trained to work on. Whether they have a Recursive Cortical Network™ that Emulates The Human Brain™ is irrelevant.