14 ms·

The state of Computer Vision and AI: we are really, really far (2012)

190 points by harperlee 12y ago

nobody_nowhere 12y ago
It's not just vision.
- solve 12y ago
  I have no hesitation in arguing that our textual understanding is currently far worse. E.g. modern deep-learning classifiers can get high 90s F1-score on identifying entities in images, but what's our capability for recognizing e.g. product entities being mentioned in Amazon reviews? Good luck getting even 70% F1-score. It's still incredibly awful. And this is just talking about very basic entity recognition in realistic settings. Lets not even consider relation extraction, and more meaningful tasks.
charlysisto 12y ago
The flash you get out of a joke is build up on a huge framework of human interaction that takes literally years to acquire for the "dedicated" BI of a brain. The fact the AI is only starting to reach the power of facial recognition which is for us a seemingly "low level automatic" function tells us the amount of learning left for an AI to come close to our understanding of the world. Not only in terms of computational power but in length and diversity of the learning process. Assuming sufficiently powerful Neural Networks, they will probably go through years of learning our world through interaction, just like a kid does, before it "gets" the joke. Doesn't mean it's impossible, and that's quit scary (in a good and bad sense I guess).
- nileshtrivedi 12y ago
  Years _worth_ of learning. They could learn by watching videos, or playback of sensory data of early AIbots and by playing video games created for the specific purpose of training them which will be much more efficient.
  - charlysisto 12y ago
    Good point. The thought actually actually popped into my mind after I wrote the post : "Wall E" and many more films/books have suggested this accelerated learning path. However this is information only, not interaction, I suspect this will have a serious distorsion effect on how the AI "perceives". It's a wild guess, but I believe interaction is at the root of understanding. Edit : yes you also mention interaction through video games, which I skipped when I scanned your comment. But then again video games might be still far from the depth of real world interaction, more of a learning enforcer than the source of it - like books are for us....
    - fapjacks 12y ago
      But there is already so much content on the internet, that accelerated learning could still happen by way of proxied interactions visible in "old" content. Does your AI have to interact with people to learn how to interact? Or is watching interaction good enough?
      - charlysisto 12y ago
        In a nutshell I don't see how any kind of adaptive intelligence can bypass the reinforcement process of trial and error through interaction. Then again you could have simulated interaction, but that may be the equivalent of the machine dreaming :-)
        fapjacks 11y ago
        You can learn to not touch a hotplate by watching someone burn their hand on it.
    - darkmighty 11y ago
      Interaction in a sense acts as a method of preventing over-fitting. If you can't interact and are given a fixed batch to learn from you can find trivial overfitted good predictive models (e.g. the identity model). One way of reproducing this aspect of interactions is simply using standard ML techniques to prevent overfitting, such as cross validation. There's another aspect that's more difficult to reproduce that is the "online learning" aspect of interactions. If you can interact, you can form hypothesis in real time, test and modify them. This can greatly enhance learning efficiency I suppose -- you may directly explore fails in your models and improve in an optimal way. This aspect also might be reproduced I believe simply through a large enough dataset. The learner could be given some capability to explore this dataset in a non-sequential way and look for informative results in it. Interesting stuff.
1971genocide 12y ago
One of the problems is we still do not have the hardware to begin doing the needed computation to solve the problem. The brain in reality is quite slow. We know neurons require at least 5 ms to do any computation. But the real power lies in the shear parallelism. Essentially biology set a limit of 5 ms but evolution worked around it by creating billions of neurons. Even If they are slower, because there are so many of them, they can do more computation in that 5 ms gap than all computers in the world combined ! Its truly marvellous when you sit back and think about that. When you think about how that gets done just using that information, the modern computer architecture works completely differently , What that means is we do not even have the adequate hardware to start working on this problem. We can push the current limit of computation to solve sub-problems and it seems under that constrain we have done very well. But its a slow evolution. GPUs gave us a clue, now we have FPGAs and soon we will have better hardware to create "better" intelligent machines. The machinery that is the brain is vastly complex and beautiful, but not well understood. Its a slow and incremental process but we will get there sometime this or the next century.
- danieltillett 12y ago
  One of the most important thing to remember is the efficiency of the human brain is layered. The ancient parts (vision for example) have been honed to an amazing level of efficiency so even the most powerful computers can't come close, while the other modules (say simple arithmetic) are so inefficient that a $2 calculator needs to uses only 1/100,000th the energy to solve the same problem. The general rule is anything we find easy as humans to accomplish is amazingly hard and ancient, while anything we find hard is relatively easy and recent (evolutionary).
  - 1971genocide 12y ago
    One thing to point out that even if the "module" that does arithmetic is slow, the brain understands the context of that calculation. 2 + 2 can be viewed as a computation that happens in nature and our brain all the time. But the real magic doesn't lie in the pure computation. Our brain understands what that 2 means in the context of all the knowledge of the entire Universe. 2 cows, 2 sheeps, 2 planets ? Understanding context is what we are really good at, as said in the article "prior" knowledge. So its not surprising that a cheap calculator can outsmart me in computation when it doesn't actually compute the context of its computation, which is sort if cheating.
jctrope 12y ago
>I have a really cool idea for a mobile local social iPhone app. I wonder if this was tongue in cheek since I thought people didn't start making fun of mo-lo-so until "Silicon Valley" aired: https://www.youtube.com/watch?v=J-GVd_HLlps https://www.youtube.com/watch?v=J-GVd_HLlps.
Simp 12y ago
3 years later and computers now outperform humans on Imagenet. While we still have a lot of work ahead, it shows how fast things can change in the exponential world of computers.
- quonn 12y ago
  Can you please give a link showing an exponential improvement between 2012 and 2015?
  - twelfthnight 12y ago
    http://image-net.org/challenges/LSVRC/2012/results.html http://image-net.org/challenges/LSVRC/2012/results.html One team at 15% http://www.image-net.org/challenges/LSVRC/2014/results http://www.image-net.org/challenges/LSVRC/2014/results Most teams are below 15%, GooLeNet is at 6% http://www.image-net.org/challenges/LSVRC/2014/results http://www.image-net.org/challenges/LSVRC/2014/results Microsoft is now below 5% http://arxiv.org/pdf/1502.03167.pdf http://arxiv.org/pdf/1502.03167.pdf Google is now below 5% I can't really argue whether that's exponential or not, but it amazing progress in a short amount of time.
ColinWright 12y ago
Something related - the title is: The state of Computer Vision and AI: we are really, really far. (2012) To me, that means we are really close to achieving it, because we are really, really far along the path. But reading the article immediately creates a cognitive dissonance - that can't be right, can it? No, the author means "we are really, really far away from our objective," which is not the same thing at all. Was this deliberate, or did the author, in his focus on the question of interpreting images, simply not notice that his text was ambiguous as well? Or is it just me?
- draven 12y ago
  Same here, and I opened the article thinking "cool, an optimistic article" as in the tech industry we tend to be overly critical of ... everything. I was disappointed.
  - efuquen 12y ago
    hmmm, I feel if anything tech people are more optimistic then warranted about AI. I found it refreshing this made it clear there are still a lot more to intelligence then just recognizing a face in an image. Now the tech industry being pessimistic about many other things, sure, but in my experience that hasn't been the case with AI.
- aidos 12y ago
  Not just you; I also went through exactly the same thought process.
- ghshephard 12y ago
  I came to the same conclusion - I wonder what an AI assessment would have made of the title.
  - mineshaftgap 12y ago
    Exactly. Us meatbags will be useful for a while still even if just to interpret other meatbags.
deleted 12y ago
[deleted]
Netcob 12y ago
I'm not sure how well we can estimate how difficult a task is just based on our amazement at "iceberg complexity". Just the fact that I was able to read that article the way I did is the product of something enormous. Discovery of electricity and semiconductors, mathematics, development of CPUs and memory, global computer networks, the entire software stack that allows me to display what someone wrote three years ago on a glowing rectangle with no involvement of any paper at all. The entire social and economic development that allows me to read this instead of walking through the woods and trying to impale some creature on an arrow. That's a lot of solved problems, most of them figured out during the past century. I don't think some image segmentation, pose estimation and semantic reasoning is going to take quite as long, especially with more and more people working on it.
vishnuharidas 12y ago
The image makes totally different ideas in a baby and adult. Babies doesn't understand most of the parts, but adults do. Because, adults learned everything from their experience. We need systems that learn from experience.
- gldalmaso 12y ago
  Though in order to build systems that learn from experience, we first need to grasp a whole lot more clearly just how learning works for us, and how exactly is it that we persist our experience data. To me it seems we are still in the stage that we have to understand ourselves better, because we simply can't devise and algorithm to solve a problem we cannot solve ourselves. We can do clever stuff with statistics and math, but that seems to me like more of a hack. For instance, people create models from very small datasets, if you have kids you surely can watch in amazement just how efficient we are wired in that regard. We try to mimic that by feeding huge datasets to algorithms but it still pales in comparison.
  - vishnuharidas 12y ago
    True, we still don't know how "learning" works in brains. But it works in our neuronal network. What about combining some real neuronal cells with technology? I remember one documentary in Discovery Channel, where some scientists make use of rat brain cells cultured on a circuit to build a small robot, which learns to avoid obstacles on it's path. A similar video is here -https://www.youtube.com/watch?v=1-0eZytv6Qk https://www.youtube.com/watch?v=1-0eZytv6Qk.
- ifdefdebug 12y ago
  Part of the experience adults use to interpret that image is that they, at one point in their life, either stepped the scale of someone else, had their scale stepped by someone else or assisted to such a scene or even a different kind of prank suitable to draw the analogy, laughing or getting angry about it. How are you going to provide such experience to a system? Edit: typo
juanuys 12y ago
A related post on the front page right now: "Neural network chip built using memristors (arstechnica.com)" https://news.ycombinator.com/item?id=9501119 https://news.ycombinator.com/item?id=9501119
tehwalrus 12y ago
> How can we even begin to go about writing an algorithm that can reason about the scene like I did? You are doing AI wrong. AI should learn all of that context by itself, from a large amount of stimulus. If it was a good one, it might be able to learn enough in less than N years, where N is the age of a human who would laugh at the photo.
- visarga 12y ago
  If we could make AI study the relationship between objects in images and videos, it could learn a lot of raw common sense. Also, it could be useful to add a different domain to the mix by cross referencing that with information extracted from text.
- Varcht 12y ago
  It strikes me that a forum such as this one might make a great training tool for AI. So many good cues for following branched reasoning, dealing with ambiguity etc. I usually"read" the articles through the comments.
- murbard2 12y ago
  Not just "stimulus", but active exploration of the environment, communicating with and learning from other people, etc.
- scott_s 12y ago
  I think the author was using a linguistic-shorthand. He is an active researcher in the field: http://cs.stanford.edu/people/karpathy/ http://cs.stanford.edu/people/karpathy/
- Florin_Andrei 12y ago
  > AI should learn all of that context by itself, from a large amount of stimulus. That's basically what Nature is doing anyway.
emddudley 12y ago
Advances in computer vision are being made every day. Image understanding is a big challenge, and the way we tackle big challenges is in little steps. I was impressed by some video segmentation and object classification results that Microsoft showed off the other day at its Ignite conference. We're a lot farther along than some people realize. Picture here: https://twitter.com/MS_Ignite/status/595365048547180545 https://twitter.com/MS_Ignite/status/595365048547180545 Video clip at the 1:02:00 mark: https://channel9.msdn.com/Events/Ignite/2015/KEY02 https://channel9.msdn.com/Events/Ignite/2015/KEY02
- colincsl 12y ago
  You're right that the video looks very impressive. However, I'm hesitant to believe that this actually works as well as one might perceive. In this area (called semantic segmentation in the computer vision world) it is common for a people to highlight both the good cases (where the labels/segmentation are correct) and the bad cases (where they are incorrect). In the MS video they only show the good. It's easy to cherry-pick examples where it works -- even if the overall accuracy is very low. Furthermore, I don't know which dataset they're using. Perhaps it only works on a small set of objects such as those shown in the video. I don't mean to knock their results but it will still take time to get this to work on a more broad set of videos.
- balakk 12y ago
  In fact, this blog author had published a paper pretty recently on scene understanding. http://cs.stanford.edu/people/karpathy/deepimagesent/ http://cs.stanford.edu/people/karpathy/deepimagesent/ This got a lot of press: http://www.nytimes.com/2014/11/18/science/researchers-announce-breakthrough-in-content-recognition-software.html http://www.nytimes.com/2014/11/18/science/researchers-announ...
ohitsdom 12y ago
Why are we grouping together computer vision and AI? They are two distinctly different fields, in my opinion. Computer vision is really hard. Great work has been done in the last 5 years with tech like the Kinect, but there's certainly a long way to go. AI has made small steps with projects like IBM's Watson, but still seems to be in its infancy (and even that may be generous).
- natch 12y ago
  Is there a good term for a system that integrates both fields to achieve the goal of, say, matching human performance at visual scene understanding?
abetusk 12y ago
At the risk of stating the obvious, even if this were true and we were actually really far away from AI or good computer vision classification, Moore's law should take effect. For every order of magnitude of difficulty it is, by whatever measure, we should only need to wait a linear amount of time. Also, notice that for this particular picture a lot of what the article is talking about doesn't matter: - Does it matter that they're in a hallway? - Does it matter there are mirrors? - Does it matter that one person is the president? The main gag is having one person standing on a scale with the intent of making a weight measurement while the other is subverting that intent, presumably with the knowledge of bystanders. My bet is that this picture could be classified as funny, even correctly labelling the joke, if the picture was tagged and a classifier were run on a large database of other tagged pictures.
- arbitrage 12y ago
  There's the explicit gag itself, and it's an old one, of stepping on a scale. The real joke, however, is in fact that it is the president. It's more humorous because it shows the president as a real person. It's unexpected, and out of context of how we typically understand the holder of the office of the president of the united states should behave. That's the main joke. So yes, the finer details are important. It's not just a funny picture.
- ifdefdebug 12y ago
  I doubt a system could be written with today's technology capable of deciding if a picture is funny/not funny based on a huge database of funny/not funny hand tagged pictures. Too much is involved. What does "is funny" even mean?
bra-ket 12y ago
just step out of 'Machine Learning' bubble for a sec there have been decades of research into 'Cognitive Architectures' (http://en.wikipedia.org/wiki/Cognitive_architecture http://en.wikipedia.org/wiki/Cognitive_architecture) and 'Artificial Consciousness' (http://en.wikipedia.org/wiki/Artificial_consciousness http://en.wikipedia.org/wiki/Artificial_consciousness) there is massive amount of experimental observations on learning and cognition in neuroscience and cognitive sciences (from neurophysiology to psychology) that is largely ignored by Artificial General Intelligence and Machine Learning communities. On the other hand the progress in Deep Learning, Computer Vision, NLP and Robotics is largely ignored by neuroscientists because these learning models do not respect biological constraints There is a whole group of narrow domains like Formal Concept Analysis, Statistical Relational Learning, Inductive Logic Programming, Commonsense Reasoning, Probabilistic Graphical Models that don't talk to each other but all deal with cognition and conceptual reasoning using different tools I think we have a chance to make progress if these fragmented domains converge.
- 1971genocide 12y ago
  Not really, There are researchers in all the different fields who's sole job is to report what other communities and doing and be the agents of cross pollination. Everyone agrees that artificial general intelligence is a difficult problem. Practically its not possible to converge all the different fields and also what is the point of that ? Each researcher is interested in solving their own sets of problems what they find interesting or have the motivation to be part of the solution. Progress is being made - maybe not at the rate of silicon valley start-ups but hard problems require time to solve. It would not be ideal that Computer Vision people suddenly stop doing their research and take the massive risk of putting all their shoes into Deep Learning. People doing Computer Vision have their sets of constraints and goals. If tomorrow the garbage man, cleaner, cook, etc all stop working and stay "we are all going to work on deep learning". The world will stop working. As absurd as that sounds that is what the implications would be if theses separate fields try to converge. Even if we do solve the problem of AGI today, what direct change or improvement in human condition would be see tomorrow ? When that AGI needs to be integrated in a framework like computer vision, robotic, or search engine you need that domain experts, practitioners in the those various fields to still exist tomorrow to maximize the economical benefit of such a technology.
coco1989 12y ago
There is no reason for a "computer" to do all that and if it could it would not - all that he talked about is about dealing with the limitations of our brains - our mind the machine would not care because it would not care about its limits - we are as a computer one big limited system constrained by the process that brought us into being - chance and natural selection. An ai is not constrained by this anymore than a dog is constrained from finding the same thing funny. humor is not a problem for us because we are part of the problem - we are a constantly derived huge word puzzle. an AI would not be that - AI would have a purpose.
JDDunn9 12y ago
To be fair, the author is evaluating a computer based on a task our brains have specifically evolved to be good at, facial recognition, social interaction, and familiar settings. You could turn it around and find tasks that computers are good at. e.g. Watch a month's worth of highway videos and count how many cars passed.
jere 12y ago
Oh, but Ray Kurzweil says we'll have an AI beating the turing test in 14 years, so I guess we're fine.
dicroce 12y ago
We might be really far from a ASI that completely grok every nuance of that picture... but we already have AI that can understand pieces of the picture... and those AI's can be used to do interesting things today. This isn't an all or nothing endeavor.
- loopbit 12y ago
  A properly trained AI would have recognized Obama in that picture much faster than I did. As for the foot in the scale, I only noticed when the author mentioned it. So much for the 'quick glance'. Which brings me to another matter. One of the reasons the author can extract all that information from that picture is because all the elements in it have been 'seen' already. A machine might not be able to extract the whole context, but things like the people involved and that they seem happy? Easy (-ish).
meric 12y ago
I don't find the picture funny, even though I understand all the bullet points from looking at the picture. Yes, the man is confused because he's being pranked by the president. It would be funny if I was there. But looking at this photo? It's not funny to me. I suppose I would rank this picture a lot higher than a picture of a skyscraper in the list of funniest pictures, but neither would be above the "laugh" threshold. Does everyone else here find it funny?
- sweezyjeezy 12y ago
  You're missing the point entirely. You can explain why people might find it funny, that's the point.
  - meric 11y ago
    Thanks...I thought it was slam-dunk laugh out loud funny for everyone except me and something was wrong with my sense of humour...
- efuquen 12y ago
  I'm not sure when he says it's funny he means laugh out loud funny. Do you find it amusing? I think the point is as humans most of us can get the joke this image is conveying and it would be very difficult for AI to understand that. How funny you actually find it is a matter of personal taste and I don't believe the author really cares about that in this post.
zk00006 12y ago
The article nicely illustrates the complexity present in a single image and I agree that we are far from automatically and fully understand images like this. I believe we would need to replicate the full brain to do that. But still, there are many interesting applications that are possible with state of the art computer vision. The question is whether state of the art is useful for industry, not whether the ultimate goal is close or far.
dave_sullivan 12y ago
He wrote this more recently when convnets surpassed "human level performance": http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/ http://karpathy.github.io/2014/09/02/what-i-learned-from-com... Gains have continued in 2015. Also, more recent reflections on his research which I think gives a bit more color to the OP: http://karpathy.github.io/2014/07/03/feature-learning-escapades/ http://karpathy.github.io/2014/07/03/feature-learning-escapa... Kind of like with the rise of electricity, the microprocessor, the PC, or internet -- in the beginning, only the people building it understood what all the fuss was about. But that changed quickly over the course of N years (where N ends up being sooner than everyone thinks). If you had started a career in any of those fields before they were obvious in hindsight, you would have probably done quite well. The author of the post has not quit to go start a photo app as far as I know, he's still doing research on the cutting edge of deep learning because that's where the most promise is.
- joe_the_user 11y ago
  I don't think his later comments really negate his earlier comments. Neural networks continue to make progress in the narrow field they are designed for and researching these I'm sure continues to be interesting. That doesn't change the point that human don't interpret an image as a couple of annotations but as rich fabric of information far beyond what computer vision current does. Basically, there is a ocean of interesting, useful and excite things computers can do before they arrive at what humans can do.
mollerhoj 12y ago
The argument in the article seems to be that we are really far away from having an AI with social intelligence. However, AI's do not need social intelligence to be interesting / dangerous. My best guess would be that creating artificial humour is much harder than creating a machine that is dangerously intelligent.
- erikb 12y ago
  A great argument! In fact humour is a tough task for humans as well. We do it all the time but only few people can be consistently funny in the eyes of other people. Also different cultures experience humour differently. E.g., German jokes aren't funny to Chinese and the other way around. It's nearly 100% excluding.
ChuckMcM 12y ago
Presumably the author is familiar with the Google paper - http://arxiv.org/pdf/1411.4555v2.pdf http://arxiv.org/pdf/1411.4555v2.pdf which should get the people, scale, and laughing bits out of the image. There are a remarkable number of technologies converging here.
bsaul 12y ago
The funny thing is, you could just as well think about the complexity of any kind of animal behavior. AI isn't not just very far from human intelligence, it is also very far from any kind of living intelligence.
- peter303 12y ago
  Roundworm elegans has just 302 fully mapped nerve cells. A people are still trying to figure out how that small system works.
  - juliangregorian 12y ago
    If you're interested you can join in! http://www.openworm.org/ http://www.openworm.org/
ilaksh 12y ago
Article is from a few years ago, missed existing AGI research at the time. Deep learning is now mainstream and awareness of AGI research that has been going on for years is slowly creeping into mainstream AI. Anyway he mentioned what needs to be done at the very end of the article which is to emulate the embodied human development process, and there is quite a lot of progress in that area.
noreasonw 12y ago
To understand the picture the machine should reason like this. First: Look for focus, detect zone in which eyes are looking. Result: The focus is on the man at the right since eyes are directed at him. Second: Sentiment Analysis Whatever he is doing people find it funny. People like to play jokes that make you experience strange things that you can't understand at the moment. Hypothesis: The man B near the main character A is interacting with A. It seems that the foot of B is interacting with the machine M. So B interacts with M that interacts with A. Generated question: What kind of interaction could be exerted by B on M to cause M get A confused? Hypothesis: B's foot is making the machine to malfunction in such a way to give a false message to A. Hypothesis: To make this image more noticeable the men are well known people or famous people. Data: The more serious the role this people play at society the more funny is the image, since their behavior is more unexpected. Reasoning like this the machine could get a plausible hypothesis of what is happening in the scene: A famous man B that probably do a serious and responsible role in society is playing a trick on a man A by making a machine M to malfunction in such a way that it gives a false message or information to A. The malfunction is caused by putting the foot on the machine. To make it funnier, the main character can't see that, ... Having a general context like this the machine could look for machines and people which could play such a role and give a heavier weight to those that make the joke a better one. The next generation of machines could modify the image to make the joke better since it can understand perfectly the context and purpose of it.
tehchromic 12y ago
The article implies that we will or ought to arrive at the point where a machine can appreciate why an image is funny in the same complex way that a human can. Not to discount the value of AI Vision research and technology, but at some point we must ask "why". Partly the question goes to the difference between artificial intelligence, and artificial consciousness. According to some definitions, the former is ability to produce relevant information, while the latter is an autonomous system capable of using that information for self interest. For example, no matter how complex a system like Watson is, it's no more conscious than a rock. Meanwhile, rudimentary life form is something we are nowhere near replicating artificially. This distinction is quite important, and very intelligent AI pundits seem to fail to make it (or understand it?). While we are quite capable of producing intelligence artificially, the capacities associated with the consciousness become confused easily in the mind. While some intelligence is easy to produce with machines, there are some problems of experience that simply cannot be solved by artificial intelligence, and require consciousness. But let's be clear: producing an artificial consciousness is orders of magnitude more complex an engineering challenge than building an artificial intelligence machine. It is also potentially enormously destructive: many times more difficult to create, and at least as destructive as atomic bomb.
putzdown 11y ago
You know, there are some problems that are simply unsolvable. Star Trek-style teleportation, I'm inclined to think, will never happen. I think "true" AI of the sort described in this article is, likewise a pipe dream. Not in a 100 million years of scientific effort could we create the hardware and software necessary to do this. If you think otherwise, that's because you have an enduring and overriding faith in the power of science to overcome all barriers and achieve all goals. But on what basis could you place that faith? Sure, science is generally good at solving problems and advancing technology, but there are some "technologies" that are simply not attainable: indeed, not even clearly definable. I think that deep AI of this kind is one of those. We think we know what it would mean for a computer to think like a person. It's not so clear that we do. Given that we deeply don't understand how we ourselves not only think, but also feel, want, assess, moralize, and experience, we're unlikely to produce thinking machines. "Ah, but the Power of Science, may it be praised forever!" you say. "Science will help us to understand how we understand!" Yeah, maybe, nah, I don't think so. Just look at the construction itself. It's loopy. "Understand how we understand"? I doubt it's possible for any instrument to ever really comprehend itself; in other words, for any thinker to ever think accurately about how it thinks. We should keep AI research within the realm of observable, measurable, useful utilities. "Comprehension" of any kind... it's never going to happen. There I've said it. Now roast me at the stake, O ye of great faith.
Houshalter 11y ago
The author wrote this in 2011: >My impression from this exercise is that it will be hard to go above 80%, but I suspect improvements might be possible up to range of about 85-90%, depending on how wrong I am about the lack of training data. (2015 update: Obviously this prediction was way off, with state of the art now in 95%, as seen in this Kaggle competition leaderboard. I'm impressed!) Thats more impressive than it sounds because each percentage point is exponentially harder than the last. Getting 95% accuracy is not 5% harder than getting 90%. Just recently machine vision starting beating humans on imagenet. Imagenet is 1,000 classes, high resolution images, taken randomly from the internet. No one would have predicted that few years ago. Sometimes a notable researcher like Hinton says that something like transcription of images into sentences might be possible in five years, only for researchers to demonstrate it in five months. I remember reading something about early engineers working on computers were extremely skeptical of the rate of computer advancement. They were so focused on narrow technical problems they didnt see the big picture.
CyberDildonics 11y ago
The state of Computer Vision and AI: we are really, really far _away_

190 points by harperlee 12y ago

nobody_nowhere 12y ago

It's not just vision.

solve 12y ago
I have no hesitation in arguing that our textual understanding is currently far worse. E.g. modern deep-learning classifiers can get high 90s F1-score on identifying entities in images, but what's our capability for recognizing e.g. product entities being mentioned in Amazon reviews? Good luck getting even 70% F1-score. It's still incredibly awful. And this is just talking about very basic entity recognition in realistic settings. Lets not even consider relation extraction, and more meaningful tasks.

charlysisto 12y ago

The flash you get out of a joke is build up on a huge framework of human interaction that takes literally years to acquire for the "dedicated" BI of a brain. The fact the AI is only starting to reach the power of facial recognition which is for us a seemingly "low level automatic" function tells us the amount of learning left for an AI to come close to our understanding of the world. Not only in terms of computational power but in length and diversity of the learning process. Assuming sufficiently powerful Neural Networks, they will probably go through years of learning our world through interaction, just like a kid does, before it "gets" the joke. Doesn't mean it's impossible, and that's quit scary (in a good and bad sense I guess).

nileshtrivedi 12y ago
Years _worth_ of learning. They could learn by watching videos, or playback of sensory data of early AIbots and by playing video games created for the specific purpose of training them which will be much more efficient.
- charlysisto 12y ago
  Good point. The thought actually actually popped into my mind after I wrote the post : "Wall E" and many more films/books have suggested this accelerated learning path. However this is information only, not interaction, I suspect this will have a serious distorsion effect on how the AI "perceives". It's a wild guess, but I believe interaction is at the root of understanding. Edit : yes you also mention interaction through video games, which I skipped when I scanned your comment. But then again video games might be still far from the depth of real world interaction, more of a learning enforcer than the source of it - like books are for us....
  - fapjacks 12y ago
    But there is already so much content on the internet, that accelerated learning could still happen by way of proxied interactions visible in "old" content. Does your AI have to interact with people to learn how to interact? Or is watching interaction good enough?
    - charlysisto 12y ago
      In a nutshell I don't see how any kind of adaptive intelligence can bypass the reinforcement process of trial and error through interaction. Then again you could have simulated interaction, but that may be the equivalent of the machine dreaming :-)
      - fapjacks 11y ago
        You can learn to not touch a hotplate by watching someone burn their hand on it.
  - darkmighty 11y ago
    Interaction in a sense acts as a method of preventing over-fitting. If you can't interact and are given a fixed batch to learn from you can find trivial overfitted good predictive models (e.g. the identity model). One way of reproducing this aspect of interactions is simply using standard ML techniques to prevent overfitting, such as cross validation. There's another aspect that's more difficult to reproduce that is the "online learning" aspect of interactions. If you can interact, you can form hypothesis in real time, test and modify them. This can greatly enhance learning efficiency I suppose -- you may directly explore fails in your models and improve in an optimal way. This aspect also might be reproduced I believe simply through a large enough dataset. The learner could be given some capability to explore this dataset in a non-sequential way and look for informative results in it. Interesting stuff.

1971genocide 12y ago

One of the problems is we still do not have the hardware to begin doing the needed computation to solve the problem. The brain in reality is quite slow. We know neurons require at least 5 ms to do any computation. But the real power lies in the shear parallelism. Essentially biology set a limit of 5 ms but evolution worked around it by creating billions of neurons. Even If they are slower, because there are so many of them, they can do more computation in that 5 ms gap than all computers in the world combined ! Its truly marvellous when you sit back and think about that. When you think about how that gets done just using that information, the modern computer architecture works completely differently , What that means is we do not even have the adequate hardware to start working on this problem. We can push the current limit of computation to solve sub-problems and it seems under that constrain we have done very well. But its a slow evolution. GPUs gave us a clue, now we have FPGAs and soon we will have better hardware to create "better" intelligent machines. The machinery that is the brain is vastly complex and beautiful, but not well understood. Its a slow and incremental process but we will get there sometime this or the next century.

danieltillett 12y ago
One of the most important thing to remember is the efficiency of the human brain is layered. The ancient parts (vision for example) have been honed to an amazing level of efficiency so even the most powerful computers can't come close, while the other modules (say simple arithmetic) are so inefficient that a $2 calculator needs to uses only 1/100,000th the energy to solve the same problem. The general rule is anything we find easy as humans to accomplish is amazingly hard and ancient, while anything we find hard is relatively easy and recent (evolutionary).
- 1971genocide 12y ago
  One thing to point out that even if the "module" that does arithmetic is slow, the brain understands the context of that calculation. 2 + 2 can be viewed as a computation that happens in nature and our brain all the time. But the real magic doesn't lie in the pure computation. Our brain understands what that 2 means in the context of all the knowledge of the entire Universe. 2 cows, 2 sheeps, 2 planets ? Understanding context is what we are really good at, as said in the article "prior" knowledge. So its not surprising that a cheap calculator can outsmart me in computation when it doesn't actually compute the context of its computation, which is sort if cheating.

jctrope 12y ago

>I have a really cool idea for a mobile local social iPhone app. I wonder if this was tongue in cheek since I thought people didn't start making fun of mo-lo-so until "Silicon Valley" aired: https://www.youtube.com/watch?v=J-GVd_HLlps https://www.youtube.com/watch?v=J-GVd_HLlps.

Simp 12y ago

3 years later and computers now outperform humans on Imagenet. While we still have a lot of work ahead, it shows how fast things can change in the exponential world of computers.

quonn 12y ago
Can you please give a link showing an exponential improvement between 2012 and 2015?
- twelfthnight 12y ago
  http://image-net.org/challenges/LSVRC/2012/results.html http://image-net.org/challenges/LSVRC/2012/results.html One team at 15% http://www.image-net.org/challenges/LSVRC/2014/results http://www.image-net.org/challenges/LSVRC/2014/results Most teams are below 15%, GooLeNet is at 6% http://www.image-net.org/challenges/LSVRC/2014/results http://www.image-net.org/challenges/LSVRC/2014/results Microsoft is now below 5% http://arxiv.org/pdf/1502.03167.pdf http://arxiv.org/pdf/1502.03167.pdf Google is now below 5% I can't really argue whether that's exponential or not, but it amazing progress in a short amount of time.

ColinWright 12y ago

Something related - the title is: The state of Computer Vision and AI: we are really, really far. (2012) To me, that means we are really close to achieving it, because we are really, really far along the path. But reading the article immediately creates a cognitive dissonance - that can't be right, can it? No, the author means "we are really, really far away from our objective," which is not the same thing at all. Was this deliberate, or did the author, in his focus on the question of interpreting images, simply not notice that his text was ambiguous as well? Or is it just me?

draven 12y ago
Same here, and I opened the article thinking "cool, an optimistic article" as in the tech industry we tend to be overly critical of ... everything. I was disappointed.
- efuquen 12y ago
  hmmm, I feel if anything tech people are more optimistic then warranted about AI. I found it refreshing this made it clear there are still a lot more to intelligence then just recognizing a face in an image. Now the tech industry being pessimistic about many other things, sure, but in my experience that hasn't been the case with AI.
aidos 12y ago
Not just you; I also went through exactly the same thought process.
ghshephard 12y ago
I came to the same conclusion - I wonder what an AI assessment would have made of the title.
- mineshaftgap 12y ago
  Exactly. Us meatbags will be useful for a while still even if just to interpret other meatbags.

deleted 12y ago

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Netcob 12y ago

I'm not sure how well we can estimate how difficult a task is just based on our amazement at "iceberg complexity". Just the fact that I was able to read that article the way I did is the product of something enormous. Discovery of electricity and semiconductors, mathematics, development of CPUs and memory, global computer networks, the entire software stack that allows me to display what someone wrote three years ago on a glowing rectangle with no involvement of any paper at all. The entire social and economic development that allows me to read this instead of walking through the woods and trying to impale some creature on an arrow. That's a lot of solved problems, most of them figured out during the past century. I don't think some image segmentation, pose estimation and semantic reasoning is going to take quite as long, especially with more and more people working on it.

vishnuharidas 12y ago

The image makes totally different ideas in a baby and adult. Babies doesn't understand most of the parts, but adults do. Because, adults learned everything from their experience. We need systems that learn from experience.

gldalmaso 12y ago
Though in order to build systems that learn from experience, we first need to grasp a whole lot more clearly just how learning works for us, and how exactly is it that we persist our experience data. To me it seems we are still in the stage that we have to understand ourselves better, because we simply can't devise and algorithm to solve a problem we cannot solve ourselves. We can do clever stuff with statistics and math, but that seems to me like more of a hack. For instance, people create models from very small datasets, if you have kids you surely can watch in amazement just how efficient we are wired in that regard. We try to mimic that by feeding huge datasets to algorithms but it still pales in comparison.
- vishnuharidas 12y ago
  True, we still don't know how "learning" works in brains. But it works in our neuronal network. What about combining some real neuronal cells with technology? I remember one documentary in Discovery Channel, where some scientists make use of rat brain cells cultured on a circuit to build a small robot, which learns to avoid obstacles on it's path. A similar video is here -https://www.youtube.com/watch?v=1-0eZytv6Qk https://www.youtube.com/watch?v=1-0eZytv6Qk.
ifdefdebug 12y ago
Part of the experience adults use to interpret that image is that they, at one point in their life, either stepped the scale of someone else, had their scale stepped by someone else or assisted to such a scene or even a different kind of prank suitable to draw the analogy, laughing or getting angry about it. How are you going to provide such experience to a system? Edit: typo

juanuys 12y ago

A related post on the front page right now: "Neural network chip built using memristors (arstechnica.com)" https://news.ycombinator.com/item?id=9501119 https://news.ycombinator.com/item?id=9501119

tehwalrus 12y ago

> How can we even begin to go about writing an algorithm that can reason about the scene like I did? You are doing AI wrong. AI should learn all of that context by itself, from a large amount of stimulus. If it was a good one, it might be able to learn enough in less than N years, where N is the age of a human who would laugh at the photo.

visarga 12y ago
If we could make AI study the relationship between objects in images and videos, it could learn a lot of raw common sense. Also, it could be useful to add a different domain to the mix by cross referencing that with information extracted from text.
Varcht 12y ago
It strikes me that a forum such as this one might make a great training tool for AI. So many good cues for following branched reasoning, dealing with ambiguity etc. I usually"read" the articles through the comments.
murbard2 12y ago
Not just "stimulus", but active exploration of the environment, communicating with and learning from other people, etc.
scott_s 12y ago
I think the author was using a linguistic-shorthand. He is an active researcher in the field: http://cs.stanford.edu/people/karpathy/ http://cs.stanford.edu/people/karpathy/
Florin_Andrei 12y ago
> AI should learn all of that context by itself, from a large amount of stimulus. That's basically what Nature is doing anyway.

emddudley 12y ago

Advances in computer vision are being made every day. Image understanding is a big challenge, and the way we tackle big challenges is in little steps. I was impressed by some video segmentation and object classification results that Microsoft showed off the other day at its Ignite conference. We're a lot farther along than some people realize. Picture here: https://twitter.com/MS_Ignite/status/595365048547180545 https://twitter.com/MS_Ignite/status/595365048547180545 Video clip at the 1:02:00 mark: https://channel9.msdn.com/Events/Ignite/2015/KEY02 https://channel9.msdn.com/Events/Ignite/2015/KEY02

colincsl 12y ago
You're right that the video looks very impressive. However, I'm hesitant to believe that this actually works as well as one might perceive. In this area (called semantic segmentation in the computer vision world) it is common for a people to highlight both the good cases (where the labels/segmentation are correct) and the bad cases (where they are incorrect). In the MS video they only show the good. It's easy to cherry-pick examples where it works -- even if the overall accuracy is very low. Furthermore, I don't know which dataset they're using. Perhaps it only works on a small set of objects such as those shown in the video. I don't mean to knock their results but it will still take time to get this to work on a more broad set of videos.
balakk 12y ago
In fact, this blog author had published a paper pretty recently on scene understanding. http://cs.stanford.edu/people/karpathy/deepimagesent/ http://cs.stanford.edu/people/karpathy/deepimagesent/ This got a lot of press: http://www.nytimes.com/2014/11/18/science/researchers-announce-breakthrough-in-content-recognition-software.html http://www.nytimes.com/2014/11/18/science/researchers-announ...

ohitsdom 12y ago

Why are we grouping together computer vision and AI? They are two distinctly different fields, in my opinion. Computer vision is really hard. Great work has been done in the last 5 years with tech like the Kinect, but there's certainly a long way to go. AI has made small steps with projects like IBM's Watson, but still seems to be in its infancy (and even that may be generous).

natch 12y ago
Is there a good term for a system that integrates both fields to achieve the goal of, say, matching human performance at visual scene understanding?

abetusk 12y ago

At the risk of stating the obvious, even if this were true and we were actually really far away from AI or good computer vision classification, Moore's law should take effect. For every order of magnitude of difficulty it is, by whatever measure, we should only need to wait a linear amount of time. Also, notice that for this particular picture a lot of what the article is talking about doesn't matter: - Does it matter that they're in a hallway? - Does it matter there are mirrors? - Does it matter that one person is the president? The main gag is having one person standing on a scale with the intent of making a weight measurement while the other is subverting that intent, presumably with the knowledge of bystanders. My bet is that this picture could be classified as funny, even correctly labelling the joke, if the picture was tagged and a classifier were run on a large database of other tagged pictures.

arbitrage 12y ago
There's the explicit gag itself, and it's an old one, of stepping on a scale. The real joke, however, is in fact that it is the president. It's more humorous because it shows the president as a real person. It's unexpected, and out of context of how we typically understand the holder of the office of the president of the united states should behave. That's the main joke. So yes, the finer details are important. It's not just a funny picture.
ifdefdebug 12y ago
I doubt a system could be written with today's technology capable of deciding if a picture is funny/not funny based on a huge database of funny/not funny hand tagged pictures. Too much is involved. What does "is funny" even mean?

bra-ket 12y ago

just step out of 'Machine Learning' bubble for a sec there have been decades of research into 'Cognitive Architectures' (http://en.wikipedia.org/wiki/Cognitive_architecture http://en.wikipedia.org/wiki/Cognitive_architecture) and 'Artificial Consciousness' (http://en.wikipedia.org/wiki/Artificial_consciousness http://en.wikipedia.org/wiki/Artificial_consciousness) there is massive amount of experimental observations on learning and cognition in neuroscience and cognitive sciences (from neurophysiology to psychology) that is largely ignored by Artificial General Intelligence and Machine Learning communities. On the other hand the progress in Deep Learning, Computer Vision, NLP and Robotics is largely ignored by neuroscientists because these learning models do not respect biological constraints There is a whole group of narrow domains like Formal Concept Analysis, Statistical Relational Learning, Inductive Logic Programming, Commonsense Reasoning, Probabilistic Graphical Models that don't talk to each other but all deal with cognition and conceptual reasoning using different tools I think we have a chance to make progress if these fragmented domains converge.

1971genocide 12y ago
Not really, There are researchers in all the different fields who's sole job is to report what other communities and doing and be the agents of cross pollination. Everyone agrees that artificial general intelligence is a difficult problem. Practically its not possible to converge all the different fields and also what is the point of that ? Each researcher is interested in solving their own sets of problems what they find interesting or have the motivation to be part of the solution. Progress is being made - maybe not at the rate of silicon valley start-ups but hard problems require time to solve. It would not be ideal that Computer Vision people suddenly stop doing their research and take the massive risk of putting all their shoes into Deep Learning. People doing Computer Vision have their sets of constraints and goals. If tomorrow the garbage man, cleaner, cook, etc all stop working and stay "we are all going to work on deep learning". The world will stop working. As absurd as that sounds that is what the implications would be if theses separate fields try to converge. Even if we do solve the problem of AGI today, what direct change or improvement in human condition would be see tomorrow ? When that AGI needs to be integrated in a framework like computer vision, robotic, or search engine you need that domain experts, practitioners in the those various fields to still exist tomorrow to maximize the economical benefit of such a technology.

coco1989 12y ago

There is no reason for a "computer" to do all that and if it could it would not - all that he talked about is about dealing with the limitations of our brains - our mind the machine would not care because it would not care about its limits - we are as a computer one big limited system constrained by the process that brought us into being - chance and natural selection. An ai is not constrained by this anymore than a dog is constrained from finding the same thing funny. humor is not a problem for us because we are part of the problem - we are a constantly derived huge word puzzle. an AI would not be that - AI would have a purpose.

JDDunn9 12y ago

To be fair, the author is evaluating a computer based on a task our brains have specifically evolved to be good at, facial recognition, social interaction, and familiar settings. You could turn it around and find tasks that computers are good at. e.g. Watch a month's worth of highway videos and count how many cars passed.

jere 12y ago

Oh, but Ray Kurzweil says we'll have an AI beating the turing test in 14 years, so I guess we're fine.

dicroce 12y ago

We might be really far from a ASI that completely grok every nuance of that picture... but we already have AI that can understand pieces of the picture... and those AI's can be used to do interesting things today. This isn't an all or nothing endeavor.

loopbit 12y ago
A properly trained AI would have recognized Obama in that picture much faster than I did. As for the foot in the scale, I only noticed when the author mentioned it. So much for the 'quick glance'. Which brings me to another matter. One of the reasons the author can extract all that information from that picture is because all the elements in it have been 'seen' already. A machine might not be able to extract the whole context, but things like the people involved and that they seem happy? Easy (-ish).

meric 12y ago

I don't find the picture funny, even though I understand all the bullet points from looking at the picture. Yes, the man is confused because he's being pranked by the president. It would be funny if I was there. But looking at this photo? It's not funny to me. I suppose I would rank this picture a lot higher than a picture of a skyscraper in the list of funniest pictures, but neither would be above the "laugh" threshold. Does everyone else here find it funny?

sweezyjeezy 12y ago
You're missing the point entirely. You can explain why people might find it funny, that's the point.
- meric 11y ago
  Thanks...I thought it was slam-dunk laugh out loud funny for everyone except me and something was wrong with my sense of humour...
efuquen 12y ago
I'm not sure when he says it's funny he means laugh out loud funny. Do you find it amusing? I think the point is as humans most of us can get the joke this image is conveying and it would be very difficult for AI to understand that. How funny you actually find it is a matter of personal taste and I don't believe the author really cares about that in this post.

zk00006 12y ago

The article nicely illustrates the complexity present in a single image and I agree that we are far from automatically and fully understand images like this. I believe we would need to replicate the full brain to do that. But still, there are many interesting applications that are possible with state of the art computer vision. The question is whether state of the art is useful for industry, not whether the ultimate goal is close or far.

dave_sullivan 12y ago

He wrote this more recently when convnets surpassed "human level performance": http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/ http://karpathy.github.io/2014/09/02/what-i-learned-from-com... Gains have continued in 2015. Also, more recent reflections on his research which I think gives a bit more color to the OP: http://karpathy.github.io/2014/07/03/feature-learning-escapades/ http://karpathy.github.io/2014/07/03/feature-learning-escapa... Kind of like with the rise of electricity, the microprocessor, the PC, or internet -- in the beginning, only the people building it understood what all the fuss was about. But that changed quickly over the course of N years (where N ends up being sooner than everyone thinks). If you had started a career in any of those fields before they were obvious in hindsight, you would have probably done quite well. The author of the post has not quit to go start a photo app as far as I know, he's still doing research on the cutting edge of deep learning because that's where the most promise is.

joe_the_user 11y ago
I don't think his later comments really negate his earlier comments. Neural networks continue to make progress in the narrow field they are designed for and researching these I'm sure continues to be interesting. That doesn't change the point that human don't interpret an image as a couple of annotations but as rich fabric of information far beyond what computer vision current does. Basically, there is a ocean of interesting, useful and excite things computers can do before they arrive at what humans can do.

mollerhoj 12y ago

The argument in the article seems to be that we are really far away from having an AI with social intelligence. However, AI's do not need social intelligence to be interesting / dangerous. My best guess would be that creating artificial humour is much harder than creating a machine that is dangerously intelligent.

erikb 12y ago
A great argument! In fact humour is a tough task for humans as well. We do it all the time but only few people can be consistently funny in the eyes of other people. Also different cultures experience humour differently. E.g., German jokes aren't funny to Chinese and the other way around. It's nearly 100% excluding.

ChuckMcM 12y ago

Presumably the author is familiar with the Google paper - http://arxiv.org/pdf/1411.4555v2.pdf http://arxiv.org/pdf/1411.4555v2.pdf which should get the people, scale, and laughing bits out of the image. There are a remarkable number of technologies converging here.

bsaul 12y ago

The funny thing is, you could just as well think about the complexity of any kind of animal behavior. AI isn't not just very far from human intelligence, it is also very far from any kind of living intelligence.

peter303 12y ago
Roundworm elegans has just 302 fully mapped nerve cells. A people are still trying to figure out how that small system works.
- juliangregorian 12y ago
  If you're interested you can join in! http://www.openworm.org/ http://www.openworm.org/

ilaksh 12y ago

Article is from a few years ago, missed existing AGI research at the time. Deep learning is now mainstream and awareness of AGI research that has been going on for years is slowly creeping into mainstream AI. Anyway he mentioned what needs to be done at the very end of the article which is to emulate the embodied human development process, and there is quite a lot of progress in that area.

noreasonw 12y ago

To understand the picture the machine should reason like this. First: Look for focus, detect zone in which eyes are looking. Result: The focus is on the man at the right since eyes are directed at him. Second: Sentiment Analysis Whatever he is doing people find it funny. People like to play jokes that make you experience strange things that you can't understand at the moment. Hypothesis: The man B near the main character A is interacting with A. It seems that the foot of B is interacting with the machine M. So B interacts with M that interacts with A. Generated question: What kind of interaction could be exerted by B on M to cause M get A confused? Hypothesis: B's foot is making the machine to malfunction in such a way to give a false message to A. Hypothesis: To make this image more noticeable the men are well known people or famous people. Data: The more serious the role this people play at society the more funny is the image, since their behavior is more unexpected. Reasoning like this the machine could get a plausible hypothesis of what is happening in the scene: A famous man B that probably do a serious and responsible role in society is playing a trick on a man A by making a machine M to malfunction in such a way that it gives a false message or information to A. The malfunction is caused by putting the foot on the machine. To make it funnier, the main character can't see that, ... Having a general context like this the machine could look for machines and people which could play such a role and give a heavier weight to those that make the joke a better one. The next generation of machines could modify the image to make the joke better since it can understand perfectly the context and purpose of it.

tehchromic 12y ago

The article implies that we will or ought to arrive at the point where a machine can appreciate why an image is funny in the same complex way that a human can. Not to discount the value of AI Vision research and technology, but at some point we must ask "why". Partly the question goes to the difference between artificial intelligence, and artificial consciousness. According to some definitions, the former is ability to produce relevant information, while the latter is an autonomous system capable of using that information for self interest. For example, no matter how complex a system like Watson is, it's no more conscious than a rock. Meanwhile, rudimentary life form is something we are nowhere near replicating artificially. This distinction is quite important, and very intelligent AI pundits seem to fail to make it (or understand it?). While we are quite capable of producing intelligence artificially, the capacities associated with the consciousness become confused easily in the mind. While some intelligence is easy to produce with machines, there are some problems of experience that simply cannot be solved by artificial intelligence, and require consciousness. But let's be clear: producing an artificial consciousness is orders of magnitude more complex an engineering challenge than building an artificial intelligence machine. It is also potentially enormously destructive: many times more difficult to create, and at least as destructive as atomic bomb.

putzdown 11y ago

You know, there are some problems that are simply unsolvable. Star Trek-style teleportation, I'm inclined to think, will never happen. I think "true" AI of the sort described in this article is, likewise a pipe dream. Not in a 100 million years of scientific effort could we create the hardware and software necessary to do this. If you think otherwise, that's because you have an enduring and overriding faith in the power of science to overcome all barriers and achieve all goals. But on what basis could you place that faith? Sure, science is generally good at solving problems and advancing technology, but there are some "technologies" that are simply not attainable: indeed, not even clearly definable. I think that deep AI of this kind is one of those. We think we know what it would mean for a computer to think like a person. It's not so clear that we do. Given that we deeply don't understand how we ourselves not only think, but also feel, want, assess, moralize, and experience, we're unlikely to produce thinking machines. "Ah, but the Power of Science, may it be praised forever!" you say. "Science will help us to understand how we understand!" Yeah, maybe, nah, I don't think so. Just look at the construction itself. It's loopy. "Understand how we understand"? I doubt it's possible for any instrument to ever really comprehend itself; in other words, for any thinker to ever think accurately about how it thinks. We should keep AI research within the realm of observable, measurable, useful utilities. "Comprehension" of any kind... it's never going to happen. There I've said it. Now roast me at the stake, O ye of great faith.

Houshalter 11y ago

The author wrote this in 2011: >My impression from this exercise is that it will be hard to go above 80%, but I suspect improvements might be possible up to range of about 85-90%, depending on how wrong I am about the lack of training data. (2015 update: Obviously this prediction was way off, with state of the art now in 95%, as seen in this Kaggle competition leaderboard. I'm impressed!) Thats more impressive than it sounds because each percentage point is exponentially harder than the last. Getting 95% accuracy is not 5% harder than getting 90%. Just recently machine vision starting beating humans on imagenet. Imagenet is 1,000 classes, high resolution images, taken randomly from the internet. No one would have predicted that few years ago. Sometimes a notable researcher like Hinton says that something like transcription of images into sentences might be possible in five years, only for researchers to demonstrate it in five months. I remember reading something about early engineers working on computers were extremely skeptical of the rate of computer advancement. They were so focused on narrow technical problems they didnt see the big picture.

CyberDildonics 11y ago

The state of Computer Vision and AI: we are really, really far _away_