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This is the same kind of question as: "are computers better than humans at Math." They are obviously better at some things related to scale: they can compute th

by catwell 10y ago

This is the same kind of question as: "are computers better than humans at Math." They are obviously better at some things related to scale: they can compute things much faster and derive solutions to equations much more easily. But the issue is: they don't really "understand" what they do. And that is why computers are still not better than humans at discovering things independently, even though a lot of proofs are now machine-assisted. Similarly, machines are becoming better than us at recognizing some specific instances within categories of objects because they can know more of them, i.e. they have larger "databases". But they are still bad at learning new concepts on their own, even though there has been much progress on that front in recent years. In general, I don't think it is a good idea to consider limitations in Computer Vision as "vision" issues; instead we should consider them as wider AI issues. Basically, ask ourselves: "could a blind Human solve this problem with the information that our current vision algorithms have"? I wrote a more detailed response along those lines over three years ago at Quora, when I was still working in CV and the field was achieving its switch to neural nets and deep learning. I still think it is mostly relevant today. https://www.quora.com/What-are-the-major-open-problems-in-computer-vision/answer/Pierre-Chapuis https://www.quora.com/What-are-the-major-open-problems-in-co...

TheOtherHobbes 10y ago
It's not obvious that humans really "understand" math. Or at least, only a very tiny minority of humans understand math well enough to improvise with it. Most humans are only able to learn a small handful of "cookbook" math practices. This is a standard trope in AI - AIs are compared with the sum total skill of human culture as a whole, not with the relatively weak skills of individual humans. (We have individuals with stand-out skills in specific domains, but there are no - at least virtually no - individuals with stand-out skills in many domains.) Perhaps future approaches to AI will be collective. Instead of a single smart all-powerful monoAI we'll build evolving problem-solving polyAI cultures, and skim off the skills and insights they develop. So "solving vision" isn't a useful measure. AI vision is getting close to classifying photos with human-like levels of consistency. 3D vision is still a problem, but will probably come with time. But then what? Non-blind humans can all recognise familiar people, pick out strangers as strangers, identify a standard selection of objects, make educated guesses about non-familiar objects, and so on. But humans can also appreciate art, identify memes and find them amusing, respond to font choices and colours, describe and label spatial relationships and views, and point to the location of objects/places that are not currently in view. Trained artists and architects can identity and name specific proportions and identify cultural references. Etc. How many of these are necessary to "solve vision"?

by catwell 10y ago

TheOtherHobbes 10y ago

It's not obvious that humans really "understand" math. Or at least, only a very tiny minority of humans understand math well enough to improvise with it. Most humans are only able to learn a small handful of "cookbook" math practices. This is a standard trope in AI - AIs are compared with the sum total skill of human culture as a whole, not with the relatively weak skills of individual humans. (We have individuals with stand-out skills in specific domains, but there are no - at least virtually no - individuals with stand-out skills in many domains.) Perhaps future approaches to AI will be collective. Instead of a single smart all-powerful monoAI we'll build evolving problem-solving polyAI cultures, and skim off the skills and insights they develop. So "solving vision" isn't a useful measure. AI vision is getting close to classifying photos with human-like levels of consistency. 3D vision is still a problem, but will probably come with time. But then what? Non-blind humans can all recognise familiar people, pick out strangers as strangers, identify a standard selection of objects, make educated guesses about non-familiar objects, and so on. But humans can also appreciate art, identify memes and find them amusing, respond to font choices and colours, describe and label spatial relationships and views, and point to the location of objects/places that are not currently in view. Trained artists and architects can identity and name specific proportions and identify cultural references. Etc. How many of these are necessary to "solve vision"?