I cannot understand why it is that so many obviously very intelligent people decide that we need another computer vision-based startup. Because the unfortunate truth is that computer vision (right now) doesn't work.
Let me qualify that. From the academic / research point of view, there have been a collection of real successes in computer vision in, say, the last ten years. But my sense is that what counts as a research success is a long way from what counts as a practical business success.
For example, the best generic object detector at the moment is probably Felzenszwalb's using deformable parts-based models[1]. And it's just not that good. On the latest PASCAL object detection challenge, you'll see that its mean precision is only ~30%.
Scott Brown, the interviewee, sets Vicarious apart by highlighting the fact that their system will be neurobiologically inspired. But the idea of learning hierarchical systems that mimic the brain's visual processing system is hardly new, and the jury is still out on whether these systems can do better than the "hand-coded" systems like Felzenszwalb's. As a random example, see [2].
Like.com showed you can build a business that uses computer vision in some way. But as Brown snarks, they "use a big bag of different heuristics to figure out the image." For the time being, that seems to be the only way to get computer vision to work in practice.
And yet facial-recognition is now freely available to consumers (Picasa, Facebook etc), our phones have blink detection, 3D motion detection and tracking is available to consumers for ~$100 (Kinect).
I'm not familiar with the PASCAL object detection challenge, but I just had a quick look. It's hard - if I understand it correctly, classifiers had to categorize photos into containing 5 types of objects form the 1000 leaf nodes of http://www.image-net.org/challenges/LSVRC/2010/browse-synset.... (Based on the description from http://www.image-net.org/challenges/LSVRC/2010/pascal_ilsvrc...). I'm having trouble understanding the scoring scheme (how is flat cost calculated?), but based on this I'm quite impressed.
> I cannot understand why it is that so many obviously very intelligent people decide that we need another computer vision-based startup. Because the unfortunate truth is that computer vision (right now) doesn't work.
This seems like a really good reason to create another computer vision-based startup.
As an AI grad student, this kind of sensationalism is somewhere between a minor irritation and a serious threat. AI always has had a severe problem with over-promising and under-delivering, and I'm of the humble opinion that until you're actually shipping the most awesome thing in the world you should keep your mouth shut. If the first thing people associate "AI research" with is "disappointment", that hurts everybody (particularly, NSF funding).
"Brain-based" AI should stay in the dark ages. Optimization-based AI is the present and the future.
(That said, if you want to talk about your sweet computer vision system that's "coming soon", go right ahead. Just don't call it AI.)
"Brain-based" AI should stay in the dark ages. Optimization-based AI is the present and the future.
Humans can see. Computer vision systems suck. There's a perfectly good one in our brains. Why not try to understand what already works?
Contrary to what most would believe, brain-based computer vision has made a lot of progress in the past 20 years. Some might think there is a fundamental flaw in the "brain-based" approach given past failures, but that ignores that fact that those failures very likely happened due to a poor understanding of the brain at the time.
The work in brain-based computer vision however has been mostly academic. Brain-based computer vision startups are even more recent, and I think it's exciting to see the startup approach to solving what has been mostly an academic problem. In a startup, the engineering mindset, quick iteration, as well as a lack of concern for publishing and other forces at play in academia could produce very different results.
I do agree that the 5 year promise is extreme, but I think we need time to see how this relatively new mode of work (both in terms of the technical approach, and the process of implementation in a startup) will play out before we call it a failure.
Full Disclosure: I was an intern at Numenta last summer.
Speaking from a researcher (both academic/industry) in vision for almost 10 years, I am afraid that they founders have quite underestimated the difficulty of the problem. Even a dog's visual system is very advanced, if you consider it from the big picture in evolution of visual sensory system in animals. So in the interview "if you can make a vision system that’s just as good as a dog" is in some sense analogous to saying "if you can simulate what's produced from 90% of visual evolution over these million years", which is clearly, a bit over-optimistic as a starting goal.
That being said, wish them luck. It's a worthy try afterall.
More accurately: Vicarious says it is trying to make its vision system the real deal. Smaller problem, and (at least according to the article) they haven't solved it yet.
Of course, smaller and small are different things - this is still a very hard thing to do. Hope they succeed.
Depends what you mean. "Good old fashioned AI" (explicit symbolic representations and rule-based inference) hasn't made a comeback yet. Even computational linguistics has largely shifted towards statistical methods based on crunching large amounts of data. So, AI is a lot livelier now than it was in the bad periods, but it's a different kind of AI than went cold in the late 1980s.
Dogs' visual systems are pretty sophisticated. Trying to mimic one of those first allows one to somewhat simplify things while getting a lot of insight into the human visual system which operates on basically the same principles.
It does no such thing, it is very likely that AI will be made up of a bunch of specialized interacting subsystems. As for No Free Lunch Theorem. See: Coevolutionary Free Lunch. Which by the way, is actually more akin to biological evolution than coevolution. http://cs.calstatela.edu/wiki/images/1/15/Wolpert-Coevolutio...
[+] [-] cbcase|15 years ago|reply
Let me qualify that. From the academic / research point of view, there have been a collection of real successes in computer vision in, say, the last ten years. But my sense is that what counts as a research success is a long way from what counts as a practical business success.
For example, the best generic object detector at the moment is probably Felzenszwalb's using deformable parts-based models[1]. And it's just not that good. On the latest PASCAL object detection challenge, you'll see that its mean precision is only ~30%.
Scott Brown, the interviewee, sets Vicarious apart by highlighting the fact that their system will be neurobiologically inspired. But the idea of learning hierarchical systems that mimic the brain's visual processing system is hardly new, and the jury is still out on whether these systems can do better than the "hand-coded" systems like Felzenszwalb's. As a random example, see [2].
Like.com showed you can build a business that uses computer vision in some way. But as Brown snarks, they "use a big bag of different heuristics to figure out the image." For the time being, that seems to be the only way to get computer vision to work in practice.
That all said, I wish them luck.
[1] http://people.cs.uchicago.edu/~pff/latent/
[2] http://www.cs.stanford.edu/people/ang//papers/nips07-sparsed...
[+] [-] JshWright|15 years ago|reply
[+] [-] nl|15 years ago|reply
I'm not familiar with the PASCAL object detection challenge, but I just had a quick look. It's hard - if I understand it correctly, classifiers had to categorize photos into containing 5 types of objects form the 1000 leaf nodes of http://www.image-net.org/challenges/LSVRC/2010/browse-synset.... (Based on the description from http://www.image-net.org/challenges/LSVRC/2010/pascal_ilsvrc...). I'm having trouble understanding the scoring scheme (how is flat cost calculated?), but based on this I'm quite impressed.
I'm human (yes, I swear it's true), and I couldn't classify things like different breeds of poodle: http://www.image-net.org/synset?wnid=n02113712
[+] [-] LiveTheDream|15 years ago|reply
This seems like a really good reason to create another computer vision-based startup.
[+] [-] unknown|15 years ago|reply
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[+] [-] aothman|15 years ago|reply
"Brain-based" AI should stay in the dark ages. Optimization-based AI is the present and the future.
(That said, if you want to talk about your sweet computer vision system that's "coming soon", go right ahead. Just don't call it AI.)
[+] [-] snikolov|15 years ago|reply
Humans can see. Computer vision systems suck. There's a perfectly good one in our brains. Why not try to understand what already works?
Contrary to what most would believe, brain-based computer vision has made a lot of progress in the past 20 years. Some might think there is a fundamental flaw in the "brain-based" approach given past failures, but that ignores that fact that those failures very likely happened due to a poor understanding of the brain at the time.
The work in brain-based computer vision however has been mostly academic. Brain-based computer vision startups are even more recent, and I think it's exciting to see the startup approach to solving what has been mostly an academic problem. In a startup, the engineering mindset, quick iteration, as well as a lack of concern for publishing and other forces at play in academia could produce very different results.
I do agree that the 5 year promise is extreme, but I think we need time to see how this relatively new mode of work (both in terms of the technical approach, and the process of implementation in a startup) will play out before we call it a failure.
Full Disclosure: I was an intern at Numenta last summer.
[+] [-] jamesaguilar|15 years ago|reply
A serious threat to what?
[+] [-] LiveTheDream|15 years ago|reply
Is this because the AI researchers truly over-promise, or because media/laypeople take a concept or statement and run with it?
[+] [-] euroclydon|15 years ago|reply
[+] [-] asknemo|15 years ago|reply
That being said, wish them luck. It's a worthy try afterall.
[+] [-] endtime|15 years ago|reply
Of course, smaller and small are different things - this is still a very hard thing to do. Hope they succeed.
[+] [-] fleitz|15 years ago|reply
[+] [-] rst|15 years ago|reply
[+] [-] abhikshah|15 years ago|reply
[+] [-] giardini|15 years ago|reply
"if you can make a vision system that’s just as good as a dog..."
Not quite my idea of "The Real Deal". And that's within a 5-year plan.
[+] [-] snikolov|15 years ago|reply
[+] [-] yters|15 years ago|reply
[+] [-] Dn_Ab|15 years ago|reply
[+] [-] ebiester|15 years ago|reply
[+] [-] uejdiws|15 years ago|reply
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