Seems like natural language processing would be an interesting direction for captchas.
- A man is running. A dog is behind him barking and growling. What does the man think might happen?
- A man goes up the stairs to the roof. He walks to the very edge of the building. He takes one more step. What is the man trying to do?
The correct answer should be pretty easy to parse out. And I'd expect a better success rate for humans than some of the captchas today that increasingly are looking more like magic eye puzzles than character recognition. But of course the big question is generation. Can these sort of implication based stories be generated in a way such that the final text can not trivially be reversed to the answer (without even considering the 'meaning' of the question)? And for that matter can these even be realistically generated in mass?
You're in a desert walking along the sand when all of a sudden you look down and see a tortise. You reach down and flip the tortise on it's back. The tortise lays on it's back, it's belly baking in the hot sun but you're not helping. Why is that leon?
People always come at this from an angle of "what can I do that computers can't?". You need to take into account the incredible diversity of people who use the internet, and what they can and can't do. There's already a viral article written by an old lady who can't pass the current captchas. Add to this, people who don't speak english, or don't speak it well; people who battle to read and comprehend text in any language; people who battle with logical reasoning; etc, etc, etc. The lowest common denominator for a task that be easily solved by any human is pretty low.
When writing such captcha questions for a forum, I generally use google as a validation to see that google can't answer the question in the top listed links. This allow me to easily adjust questions to the point where natural language processing should not be able to answer the question but a human person would.
Yep, Question-Answering Semantic Role labeling is an interesting research project around crowdsourcing NLP datasets. https://dada.cs.washington.edu/qasrl/
This was my first thought, there are many different things a human could think of, and we will probably cycle through them all in a few ms. This would have to be multiple choice and then what? The "AI" would have a baseline 25% chance of getting it correct (assuming 4 options).
- A man is running. A dog is behind him barking and growling. What does the man think might happen?
- A man goes up the stairs to the roof. He walks to the very edge of the building. He takes one more step. What is the man trying to do?
The correct answer should be pretty easy to parse out. And I'd expect a better success rate for humans than some of the captchas today that increasingly are looking more like magic eye puzzles than character recognition. But of course the big question is generation. Can these sort of implication based stories be generated in a way such that the final text can not trivially be reversed to the answer (without even considering the 'meaning' of the question)? And for that matter can these even be realistically generated in mass?