6 ms·

Vicarious Systems Says Its Artificial Intelligence Is The Real Deal

48 points by churp 16y ago

endtime 16y ago
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.
fleitz 16y ago
Does this mean the AI winter is over?
- rst 16y ago
  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.
  - merijnv 16y ago
    I'm not an AI student myself (I do CS, but with an interest in AI and probabilistic algorithms) and since I'm from a younger generation most of the "new" AI stuff like neural networks, evolutionary computing, etc. wasn't that new any more when it got taught. But "classical AI" (as in symbolic AI and rule-based systems) always struck me as rather dumb and inefficient after being shown the power of probabilistic systems like EC and neural networks. So in that sense I don't think classical AI will never make a comeback, and I'm convinced this is probably a good thing. I mean, the only example we have of solving extremely complex problems is nature, and nature just doesn't work with symbolic system and rule-based inference. It uses probabilistic systems which interact with each other in feedback loops. I, for one, welcome our new non-deterministic overlords.
cbcase 16y ago
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. That all said, I wish them luck. [1] http://people.cs.uchicago.edu/~pff/latent/ http://people.cs.uchicago.edu/~pff/latent/ [2] http://www.cs.stanford.edu/people/ang//papers/nips07-sparsedeepbeliefnetworkv2.pdf http://www.cs.stanford.edu/people/ang//papers/nips07-sparsed...
- deleted 16y ago
  [deleted]
- JshWright 16y ago
  Your argument against computer vision startups is that there isn't a viable computer vision solution at this point?
  - neutronicus 16y ago
    Well, his argument is that well-funded, very intelligent people are trying like hell at computer vision, and not succeeding. That's not a good sign - you'd prefer that your space has been hitherto overlooked by smart people with lots of money.
aothman 16y ago
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.)
- euroclydon 16y ago
  Does Numenta fall under "Brain-based?"
  - snikolov 16y ago
    Numenta does fall under brain based. I'm not sure what Vicarious are working on, but recently Numenta transitioned to radically more biological algorithms. It would be interesting to compare the two if Vicarious comes out with more detailed information about their algorithms.
  - abhikshah 16y ago
    I would say "Brain-inspired". As I see it, Numenta's model (as of about a year ago) is based on (1) the hierarchal organization of neurons, (2) the presence of feedback loops in neural architectures and (3) the importance of temporal processing even for static scenes. This doesn't include any intracellular details nor any of the larger and/or specialized brain structures.
- LiveTheDream 16y ago
  > AI always has had a severe problem with over-promising and under-delivering Is this because the AI researchers truly over-promise, or because media/laypeople take a concept or statement and run with it?
  - 16y ago
yters 16y ago
NFLT implies AI is logically impossible. Good way to short the stock market.
- ebiester 16y ago
  Considering NFLT isn't on the first page of google, would you mind expanding that acronym?
  - haliax 16y ago
    If the parent means the No Free Lunch Theorem, then the point is mistaken. That theorem says that you can't improve the performance of a classifier for some objective function, without making it worse on another -- in other words, all algorithms have an identical mean performance when averaged over all possible objective functions. The reason this doesn't mean that human-level AI is impossible is that we too are designed (well, evolved by natural selection) to perform well for a particular objective function: one in which say, the standard laws of physics/optics apply. Optical illusions illustrate that our performance on this objective function is not perfect. Moreover, you can see a human being's performance on a different objective function by, for example, trying to recognize objects in pictures which have been scrambled according to some predefined method (e.g. shuffle the pixels but use the same random seed each time). Each scene will still convey the same amount of information about the objects in it, but it'll be pretty tricky to recognize the objects.
    - yters 16y ago
      "The reason this doesn't mean that human-level AI is impossible is that we too are designed (well, evolved by natural selection) to perform well for a particular objective function: one in which say, the standard laws of physics/optics apply." That's an assumption no one has ever given the slightest shred of evidence for. I remain highly skeptical.
- Dn_Ab 16y ago
  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-Coevolution.pdf http://cs.calstatela.edu/wiki/images/1/15/Wolpert-Coevolutio...
giardini 16y ago
From the article "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 16y ago
  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.
asknemo 16y ago
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.
abhikshah 16y ago
Interestingly, one of the cofounders is Dileep George, previously the CTO and cofounder of Numenta.
uejdiws 16y ago
I don't know much about AI, but I should say that a good amount of money for an intelligent person is a good way for developing AI. Unfortunately freedom implies that results are not guaranteed.

48 points by churp 16y ago

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.

fleitz 16y ago

Does this mean the AI winter is over?

rst 16y ago
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.
- merijnv 16y ago
  I'm not an AI student myself (I do CS, but with an interest in AI and probabilistic algorithms) and since I'm from a younger generation most of the "new" AI stuff like neural networks, evolutionary computing, etc. wasn't that new any more when it got taught. But "classical AI" (as in symbolic AI and rule-based systems) always struck me as rather dumb and inefficient after being shown the power of probabilistic systems like EC and neural networks. So in that sense I don't think classical AI will never make a comeback, and I'm convinced this is probably a good thing. I mean, the only example we have of solving extremely complex problems is nature, and nature just doesn't work with symbolic system and rule-based inference. It uses probabilistic systems which interact with each other in feedback loops. I, for one, welcome our new non-deterministic overlords.

cbcase 16y ago

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. That all said, I wish them luck. [1] http://people.cs.uchicago.edu/~pff/latent/ http://people.cs.uchicago.edu/~pff/latent/ [2] http://www.cs.stanford.edu/people/ang//papers/nips07-sparsedeepbeliefnetworkv2.pdf http://www.cs.stanford.edu/people/ang//papers/nips07-sparsed...

deleted 16y ago
[deleted]
JshWright 16y ago
Your argument against computer vision startups is that there isn't a viable computer vision solution at this point?
- neutronicus 16y ago
  Well, his argument is that well-funded, very intelligent people are trying like hell at computer vision, and not succeeding. That's not a good sign - you'd prefer that your space has been hitherto overlooked by smart people with lots of money.

aothman 16y ago

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.)

euroclydon 16y ago
Does Numenta fall under "Brain-based?"
- snikolov 16y ago
  Numenta does fall under brain based. I'm not sure what Vicarious are working on, but recently Numenta transitioned to radically more biological algorithms. It would be interesting to compare the two if Vicarious comes out with more detailed information about their algorithms.
- abhikshah 16y ago
  I would say "Brain-inspired". As I see it, Numenta's model (as of about a year ago) is based on (1) the hierarchal organization of neurons, (2) the presence of feedback loops in neural architectures and (3) the importance of temporal processing even for static scenes. This doesn't include any intracellular details nor any of the larger and/or specialized brain structures.
LiveTheDream 16y ago
> AI always has had a severe problem with over-promising and under-delivering Is this because the AI researchers truly over-promise, or because media/laypeople take a concept or statement and run with it?
- 16y ago

yters 16y ago

NFLT implies AI is logically impossible. Good way to short the stock market.

ebiester 16y ago
Considering NFLT isn't on the first page of google, would you mind expanding that acronym?
- haliax 16y ago
  If the parent means the No Free Lunch Theorem, then the point is mistaken. That theorem says that you can't improve the performance of a classifier for some objective function, without making it worse on another -- in other words, all algorithms have an identical mean performance when averaged over all possible objective functions. The reason this doesn't mean that human-level AI is impossible is that we too are designed (well, evolved by natural selection) to perform well for a particular objective function: one in which say, the standard laws of physics/optics apply. Optical illusions illustrate that our performance on this objective function is not perfect. Moreover, you can see a human being's performance on a different objective function by, for example, trying to recognize objects in pictures which have been scrambled according to some predefined method (e.g. shuffle the pixels but use the same random seed each time). Each scene will still convey the same amount of information about the objects in it, but it'll be pretty tricky to recognize the objects.
  - yters 16y ago
    "The reason this doesn't mean that human-level AI is impossible is that we too are designed (well, evolved by natural selection) to perform well for a particular objective function: one in which say, the standard laws of physics/optics apply." That's an assumption no one has ever given the slightest shred of evidence for. I remain highly skeptical.
Dn_Ab 16y ago
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-Coevolution.pdf http://cs.calstatela.edu/wiki/images/1/15/Wolpert-Coevolutio...

giardini 16y ago

From the article "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 16y ago
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.

asknemo 16y ago

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.

abhikshah 16y ago

Interestingly, one of the cofounders is Dileep George, previously the CTO and cofounder of Numenta.

uejdiws 16y ago

I don't know much about AI, but I should say that a good amount of money for an intelligent person is a good way for developing AI. Unfortunately freedom implies that results are not guaranteed.