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This example, and others like it point to the central weakness of neural networks for image recognition: No matter how much data you feed it, they never really

This example, and others like it point to the central weakness of neural networks for image recognition: No matter how much data you feed it, they never really develop concepts or abstractions of what the objects it is classifying really represent or mean. The weight and biases that get fine tuned by gradient descent, are no more than a highly complex function mapping the input pixels to discrete classes. While this may well represent how the visual cortex works at the lowest level, what appears to be missing are higher levels of abstraction and meaning. Perhaps machine learning needs to be coupled with some of the older paradigms of AI which included modeling, logic, reasoning, to achieve understanding. As of right now, a well trained convolutional neural network is no more than a mechanical pattern matching algorithm on steroids.

dahart 9y ago
That’s all true. Sometimes I describe NNs as fancy least squares. Interesting things are bound to happen when you have a few hundred million parameters. Just to play devil’s advocate, we don’t know yet if the model is bad or if we’re just feeding the wrong kind of data, right? These optimization algorithms are good at interpolating; they do well with new data points that land inside the multidimensional convex hull of the training data. They can fail spectacularly when the new data to inference is outside that boundary. But humans aren’t that great at extrapolation either. Maybe NNs will be good enough when we show them everything there is, maybe what we think of as conceptual understanding is just as much simple interpolation of our experiences as our neural networks...?
- pitchups 9y ago
  >maybe what we think of as conceptual understanding is just as much simple interpolation of our experiences as our neural networks...? Possibly - we don't know. Sure if we could train one of these imagenet type classifiers with a million or billion images - close to all known objects in the universe, it may well be able to "recognize" everything. But that still doesn't solve the problem of abstraction or meaning, much less of intuition and generalization. Humans are able to generalize to new domains based on internal models of the world around us. The models used in RNNs for encoding word embeddings, seem to a bit closer to representing meaning. I agree though that NNs are evolving - we are in very early days, and who knows the NNs of the future may reveal that what we consider as understanding is no more than simple interpolation, as you suggest.
agitator 9y ago
I think this might also be due to the fact that the compute for neural nets and the complexity of the networks are still in their infancy. The neural nets in these cases are all simple classifiers, working on a fixed image resolution with fixed training data. What do you expect? You can't train an intelligent machine if your architecture is dumb to begin with. If you showed me an image of a green field with a bunch of fur balls on it. I'd go "Oh look! Floofy Sheep!" but then maybe upon closer inspection, i'd go "heeyyy.... thats actually a herd of cats!" But a neural net isn't designed to make decisions, to say hey maybe I should investigate further, etc. Its just a black box that spits out probabilities of classifiers. I think if we want to get more sophisticated with judgements and something nearing more realistic intelligence, we would need something like nets of neural nets, and for ways to interconnect them. Like here is a model for sheep, it also has interconnections with environment, and here is another model for a sheep's facial features, etc. And maybe a net for decision making or asking questions if confidence is lacking or ambiguous. I can see a toddler going "oooh sheep!", as well and then a parent going, "no, look closer, those are kittens!" And then the kid learns oh, maybe I shouldn't be so quick to conclude! Sometimes I may be deceived!
- joe_the_user 9y ago
  I think this might also be due to the fact that the compute for neural nets and the complexity of the networks are still in their infancy. Well, neural nets may be just starting out but I think one can they're approximation process are not complex. They are very complex in the sense of having many layers and many pseudo-neurons on each layers. What's happening is that the networks are mapping images to high-dimensioned "feature space" and then drawing dividing line in the feature between matching and not-matching images. It is vastly complicated but heuristic process. Essentially, the division between image types are based on both meaningful and meaningless differences between the images. The example classified as "a boy holding a dog" (when it was a goat) and "a herd of giraffes in trees" (when it was goats that had climbed trees happened to have more random characteristics in common with the classification than their real qualities. The thing is the method can be made relatively better but for absolute improvement, you'd want a way to not just have more approximation but to find a way to get rid of garbage approximation, garbage conclusions and so-forth. I suspect that would imply both different algorithms and a different training cycle.
candiodari 9y ago
> a mechanical pattern matching algorithm on steroids. Firstly I would like to point out that there are no (good) "mechanical pattern matching algorithms". I would love for you to point out some, but as far as I know, outside of AI no such algorithms exist. As for the entire argument, the problem with this reasoning is that it only works at the lowest level. And even then, it sort of works for fully connected and CNN based image classification. But autoencoders certainly have what I'd consider "concepts". Not in a language we understand, but they do. They have a signaling mechanism "explaining" high-level features to other neural networks. RNNs have concepts. RL policy networks don't just have concepts, they have strategies. They have lies, truths, and even political lies: truths explicitly designed to make it really really easy to believe something that's not actually happening. Usually they even exhibit meta-lying: systematically not deceiving with just one (but important) deception in an unpredictable location. (and I would like to add that the CNN features, looking at the numbers, look VERY similar to concepts in more abstract neural networks. Perhaps the "difference" is merely one of those philosophical differences we keep hitting). GAE networks have concepts (of course they are autoencoders usually). So in truth this is a matter : neural networks that have no use for concepts have only an incomplete notion of concepts. Neural networks that have to "teach" or otherwise interact at a high level with either humans or other neural nets very much do have concepts. And I would like to say, this is yet another "humans are magical because X" argument. None of those arguments has ever stood the test of time. This one won't either. AGI is coming, sorry to disappoint you, and the current theories of neural networks will be shown to be "insect-level" (or whatever level) AGI.
- pitchups 9y ago
  Yes, RNNs and autoencoders, etc. do seem to be encoding concepts via embedding vectors in higher dimensions. But see the argument made by Doug Hofstadter about language translation using RNNs [2]. We have also seen how CNN layers can "visualize" higher levels of features in images as we go deeper in the network. And many think that some variation of this basic approach done repeatedly, with more data, computing power etc. will be sufficient to lead us to AGI in the future. However, many of the leading figures in AI - including Geoffrey Hinton - the father of deep learning, is very skeptical of the approach to AI that he pioneered. He recently stated - "My view is throw it all away and start again." [0] Francois Chollet - the author of the deep learning framework Keras, has said: "For all the progress made, it seems like almost all important questions in AI remain unanswered. Many have not even been properly asked yet." [1] And of course, Doug Hofstadter, who thinks it is going to take a lot more to come close to human level intelligence & understanding, even when you consider the most advanced RNNs of the day - those that run Google Translate . [0] https://cacm.acm.org/news/221108-artificial-intelligence-pioneer-says-we-need-to-start-over/fulltext https://cacm.acm.org/news/221108-artificial-intelligence-pio... [1] https://twitter.com/fchollet/status/837188765500071937 https://twitter.com/fchollet/status/837188765500071937 [2] https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s...
felippee 9y ago
Though I agree with the spirit of what you are saying, I would add that it is perhaps not the weakness of neural networks per se, but weakness of the current architectures and training (supervised, hence verbalized) paradigms. I think we could do much more to improve things, if we stopped pushing for extra % on benchmarks and instead rethink the problems we'd like to solve and approach them from a new angle.
joe_the_user 9y ago
This example, and others like it point to the central weakness of neural networks for image recognition: No matter how much data you feed it, they never really develop concepts or abstractions of what the objects it is classifying really represent or mean. This is an excellent point but it begs for an answer to the question "what does 'really mean' mean?" What are all the ways a human can determine what a picture "really means" and which of these methods can be used in a given picture? We know dogs have certain shapes and goats have certain shapes. Other entities have different characteristics. We can explain how we think we reach conclusions. How we actually the conclusions is likely different and may or may not involve "pattern matching steroids" for a given case - what's more definite is we try to reconcile our conclusions between the example so they involve a single consistent picture of the world. Is determining "what a picture really means?"
- fizx 9y ago
  Meaning exists in relationships, which its clear that the current generation of AI learns. An example is word2vec, which can learn that king - man + woman = queen, and simultaneously king - man + boy = prince, etc. The current generation of image recognition is really missing an understanding of physics and 3d space. There's no understanding of what would happen if a dog moves its head around. The next generation of algorithms might fix this. Some people are excited about "capsule networks", which are supposed to learn features that are able to be rotated significantly without breaking.
  - Scene_Cast2 9y ago
    I haven't seen any follow-ups on capsule networks since their big splash half a year ago. I'm guessing follow-up projects have a research latency of a year.
- 3pt14159 9y ago
  We're going to get general AI the same way we get I: multi-sensory agents existing with agency, instincts, and guides in real 3D space. I cannot conceive of any other way to understand things deeply. Babies run experiments. How does AI play with a cat? How does it ever understand the concept of a cats mind without ever playing with it? If we want our AI to have conceptualization as we understand it we need AI to have similar sensory inputs and similar arrays of potential actions. And sure, we could copy the code from one AI to the next to have identical minds at t0, but I struggle with the ethics of that and really I'd rather have diversity in AIs than to have a bunch of clones running around thinking with the same types of thought patterns. The problem I have once I think about it is that this line of thinking leads me to be much less sure of the nature of my own existence. Do we first let the mind of an AI develop to appreciate humanity before letting it know that it is an AI? Seems like it would solve a lot of possible problems since Ghandi wouldn't take the murder pill. http://lesswrong.com/lw/2vj/gandhi_murder_pills_and_mental_illness/ http://lesswrong.com/lw/2vj/gandhi_murder_pills_and_mental_i...
chillee 9y ago
I think it's pretty clear that neural networks are developing concepts/abstractions of the object it's classifying. Check https://distill.pub/2017/feature-visualization/appendix/ https://distill.pub/2017/feature-visualization/appendix/ I think some of the filters are pretty clearly developing "higher levels of abstractions".
- argonaut 9y ago
  It's not an interesting question whether networks learn abstractions. It's almost tautological - an image classification network will by definition (attempt to) distill an image into a distribution over categories. So when people criticize abstraction I think they are really criticizing the quality of the abstraction... Because the key phrase in the grandparent post is "really represent or mean." Grass + white strands = sheep, is a hierarchical abstraction, but it's a bogus one. Feature visualizations do not answer this more relevant question.
sgk284 9y ago
It's not entirely clear to me that all of cognition isn't simply pattern matching with learned responses. It seems the human brain may just be doing more nuanced matching / responding.
- idrios 9y ago
  I remember reading (don't remember where) that the current state of machine learning is a lot like the state of bridges a few thousand years ago. People basically try different approaches using intuition and sometimes if the results are good they'll tell other people about it, nowadays in the form of research papers getting published. However, the generalized equations that facilitate modern civil engineering were far from existing. Similarly, we don't yet have general equations to derive or optimize a machine learning algorithm for a given problem as input, we just choose an algorithm, choose parameters that we think make sense, go through a lot of trial and error, then let the algorithm run. There's a lot that's still not understood about the brain. Even at the cellular level, microglia have gained attention recently for their contribution to human learning [1]. The NN algorithm was modeled after human neurons for having n inputs and 1 binary output. It's hard to say how cells that regulate the cellular environment and communicate with each other via cytokines would affect the abstraction of the brain to an algorithm or mathematical model. At least for this example, there's a book called "the other brain" about how glia, which make up ~85% of the brain, perform a myriad of operations beyond just keeping neurons together [2]. But you could also look at the brain through a genetic lens. A lot of the neurosystem is simply hardwired. The knee-jerk response is a reflex arc, which means the signal goes directly from the sensory nerve to the motor nerve (causing you to kick) before it even gets to your brain. That reflex response has been hardcoded into your DNA. How much of the rest of the brain is learned vs. predeterminantly structured? Cognition can probably be reduced to just pattern matching with learned responses, but it's a bit like an ancient Roman looking at New York City skyscrapers and saying "these are just built using arches and a lot of nuance". [1] https://www.sciencedirect.com/science/article/pii/S1074742713001159 https://www.sciencedirect.com/science/article/pii/S107474271... [2] http://www.simonandschuster.com/books/The-Other-Brain/R-Douglas-Fields/9780743291422 http://www.simonandschuster.com/books/The-Other-Brain/R-Doug...
marmaduke 9y ago
My 3 year old makes similar mistakes. I expect he’ll improve in large part because he isn’t just a set of layers but has subcritical areas, fears and motivations and a body to explore the world with. This suggests that we these large ML models do need complementary components to improve. For example, if they were capable of active exploration of an image, they may have said there are sheep and then upon searching for the sheep, realize they aren’t there.
deleted 9y ago
[deleted]
bobthechef 9y ago
> As of right now, a well trained convolutional neural network is no more than a mechanical pattern matching algorithm on steroids. And computers are at best mechanical pattern matching machines. This isn't something that's subject to empirical evidence, and appeals to ignorance or the limits of our current understanding will not do. Computers cannot be any more than that by definition (and arguably the word "matching" is being used in an analogous fashion; rather, a computer configured with an algorithm is such that given an initial state, it will lead to a final state that, when interpreted by a human being, can be interpreted consistently in the desired manner, i.e., a final state of 0 means that the initial configuration encoded two state that match, and a final state of 1 means that they did not). Abstraction is, by definition, NOT reducible to a mechanical process like this. The human interpreter is the one possessing abstract concepts and who interprets by means of meanings conventionally assigned to symbols or machine states. A machine may contain a state that we call an image, but no process can in-principle abstract anything from this aggregation of states. To claim otherwise is to completely misunderstand what symbol manipulation is. (Even a human being couldn't abstract anything from an image in a mode analogous to the way in which a computer operates. By analogy, given a matrix of RGB values, could you tell me what's in the image? Or could you at best compute, say, a value that, when looked up in an already given table of values, gives you a label such as "sheep"?) However, that does not mean that AI cannot perform well, at least within narrow constraints. It may very well be possible to improve AI techniques to such a degree that it can assign the label "sheep" correctly with high accuracy. There simply is an in-principle difference between AI and actual intelligence.
fizx 9y ago
When you talk about mapping (patterns of the) the input pixels to discrete classes, I don't think thats entirely what people do. We have the ability to make "distributed representations" of concepts e.g. word2vec, GloVe, etc which contain the idea that a sheep is pretty similar to a dog. The classes are far from discrete. I'm pretty sure people train image recognizers to output these representations, which can include states like 51% certainty dog, 48% certainty sheep, 1% other, and if you aren't sure, take the best choice. Its such an intuitive idea to combine these things that if it hasn't happened in the literature yet (I only looked for 1 minute), its because 1000 people have tried it, failed to improve the state of the art, and didn't publish. On the other hand, we're generally pretty bad at inferring and generalizing 3d structure out of images, so I tend to blame that.
- fizx 9y ago
  If you look at the fast.ai lesson 11 YouTube video, the very beginning has an amazing example of this with fish!

dahart 9y ago

That’s all true. Sometimes I describe NNs as fancy least squares. Interesting things are bound to happen when you have a few hundred million parameters. Just to play devil’s advocate, we don’t know yet if the model is bad or if we’re just feeding the wrong kind of data, right? These optimization algorithms are good at interpolating; they do well with new data points that land inside the multidimensional convex hull of the training data. They can fail spectacularly when the new data to inference is outside that boundary. But humans aren’t that great at extrapolation either. Maybe NNs will be good enough when we show them everything there is, maybe what we think of as conceptual understanding is just as much simple interpolation of our experiences as our neural networks...?

pitchups 9y ago
>maybe what we think of as conceptual understanding is just as much simple interpolation of our experiences as our neural networks...? Possibly - we don't know. Sure if we could train one of these imagenet type classifiers with a million or billion images - close to all known objects in the universe, it may well be able to "recognize" everything. But that still doesn't solve the problem of abstraction or meaning, much less of intuition and generalization. Humans are able to generalize to new domains based on internal models of the world around us. The models used in RNNs for encoding word embeddings, seem to a bit closer to representing meaning. I agree though that NNs are evolving - we are in very early days, and who knows the NNs of the future may reveal that what we consider as understanding is no more than simple interpolation, as you suggest.

agitator 9y ago

I think this might also be due to the fact that the compute for neural nets and the complexity of the networks are still in their infancy. The neural nets in these cases are all simple classifiers, working on a fixed image resolution with fixed training data. What do you expect? You can't train an intelligent machine if your architecture is dumb to begin with. If you showed me an image of a green field with a bunch of fur balls on it. I'd go "Oh look! Floofy Sheep!" but then maybe upon closer inspection, i'd go "heeyyy.... thats actually a herd of cats!" But a neural net isn't designed to make decisions, to say hey maybe I should investigate further, etc. Its just a black box that spits out probabilities of classifiers. I think if we want to get more sophisticated with judgements and something nearing more realistic intelligence, we would need something like nets of neural nets, and for ways to interconnect them. Like here is a model for sheep, it also has interconnections with environment, and here is another model for a sheep's facial features, etc. And maybe a net for decision making or asking questions if confidence is lacking or ambiguous. I can see a toddler going "oooh sheep!", as well and then a parent going, "no, look closer, those are kittens!" And then the kid learns oh, maybe I shouldn't be so quick to conclude! Sometimes I may be deceived!

joe_the_user 9y ago
I think this might also be due to the fact that the compute for neural nets and the complexity of the networks are still in their infancy. Well, neural nets may be just starting out but I think one can they're approximation process are not complex. They are very complex in the sense of having many layers and many pseudo-neurons on each layers. What's happening is that the networks are mapping images to high-dimensioned "feature space" and then drawing dividing line in the feature between matching and not-matching images. It is vastly complicated but heuristic process. Essentially, the division between image types are based on both meaningful and meaningless differences between the images. The example classified as "a boy holding a dog" (when it was a goat) and "a herd of giraffes in trees" (when it was goats that had climbed trees happened to have more random characteristics in common with the classification than their real qualities. The thing is the method can be made relatively better but for absolute improvement, you'd want a way to not just have more approximation but to find a way to get rid of garbage approximation, garbage conclusions and so-forth. I suspect that would imply both different algorithms and a different training cycle.

candiodari 9y ago

> a mechanical pattern matching algorithm on steroids. Firstly I would like to point out that there are no (good) "mechanical pattern matching algorithms". I would love for you to point out some, but as far as I know, outside of AI no such algorithms exist. As for the entire argument, the problem with this reasoning is that it only works at the lowest level. And even then, it sort of works for fully connected and CNN based image classification. But autoencoders certainly have what I'd consider "concepts". Not in a language we understand, but they do. They have a signaling mechanism "explaining" high-level features to other neural networks. RNNs have concepts. RL policy networks don't just have concepts, they have strategies. They have lies, truths, and even political lies: truths explicitly designed to make it really really easy to believe something that's not actually happening. Usually they even exhibit meta-lying: systematically not deceiving with just one (but important) deception in an unpredictable location. (and I would like to add that the CNN features, looking at the numbers, look VERY similar to concepts in more abstract neural networks. Perhaps the "difference" is merely one of those philosophical differences we keep hitting). GAE networks have concepts (of course they are autoencoders usually). So in truth this is a matter : neural networks that have no use for concepts have only an incomplete notion of concepts. Neural networks that have to "teach" or otherwise interact at a high level with either humans or other neural nets very much do have concepts. And I would like to say, this is yet another "humans are magical because X" argument. None of those arguments has ever stood the test of time. This one won't either. AGI is coming, sorry to disappoint you, and the current theories of neural networks will be shown to be "insect-level" (or whatever level) AGI.

pitchups 9y ago
Yes, RNNs and autoencoders, etc. do seem to be encoding concepts via embedding vectors in higher dimensions. But see the argument made by Doug Hofstadter about language translation using RNNs [2]. We have also seen how CNN layers can "visualize" higher levels of features in images as we go deeper in the network. And many think that some variation of this basic approach done repeatedly, with more data, computing power etc. will be sufficient to lead us to AGI in the future. However, many of the leading figures in AI - including Geoffrey Hinton - the father of deep learning, is very skeptical of the approach to AI that he pioneered. He recently stated - "My view is throw it all away and start again." [0] Francois Chollet - the author of the deep learning framework Keras, has said: "For all the progress made, it seems like almost all important questions in AI remain unanswered. Many have not even been properly asked yet." [1] And of course, Doug Hofstadter, who thinks it is going to take a lot more to come close to human level intelligence & understanding, even when you consider the most advanced RNNs of the day - those that run Google Translate . [0] https://cacm.acm.org/news/221108-artificial-intelligence-pioneer-says-we-need-to-start-over/fulltext https://cacm.acm.org/news/221108-artificial-intelligence-pio... [1] https://twitter.com/fchollet/status/837188765500071937 https://twitter.com/fchollet/status/837188765500071937 [2] https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s...

felippee 9y ago

Though I agree with the spirit of what you are saying, I would add that it is perhaps not the weakness of neural networks per se, but weakness of the current architectures and training (supervised, hence verbalized) paradigms. I think we could do much more to improve things, if we stopped pushing for extra % on benchmarks and instead rethink the problems we'd like to solve and approach them from a new angle.

joe_the_user 9y ago

This example, and others like it point to the central weakness of neural networks for image recognition: No matter how much data you feed it, they never really develop concepts or abstractions of what the objects it is classifying really represent or mean. This is an excellent point but it begs for an answer to the question "what does 'really mean' mean?" What are all the ways a human can determine what a picture "really means" and which of these methods can be used in a given picture? We know dogs have certain shapes and goats have certain shapes. Other entities have different characteristics. We can explain how we think we reach conclusions. How we actually the conclusions is likely different and may or may not involve "pattern matching steroids" for a given case - what's more definite is we try to reconcile our conclusions between the example so they involve a single consistent picture of the world. Is determining "what a picture really means?"

fizx 9y ago
Meaning exists in relationships, which its clear that the current generation of AI learns. An example is word2vec, which can learn that king - man + woman = queen, and simultaneously king - man + boy = prince, etc. The current generation of image recognition is really missing an understanding of physics and 3d space. There's no understanding of what would happen if a dog moves its head around. The next generation of algorithms might fix this. Some people are excited about "capsule networks", which are supposed to learn features that are able to be rotated significantly without breaking.
- Scene_Cast2 9y ago
  I haven't seen any follow-ups on capsule networks since their big splash half a year ago. I'm guessing follow-up projects have a research latency of a year.
3pt14159 9y ago
We're going to get general AI the same way we get I: multi-sensory agents existing with agency, instincts, and guides in real 3D space. I cannot conceive of any other way to understand things deeply. Babies run experiments. How does AI play with a cat? How does it ever understand the concept of a cats mind without ever playing with it? If we want our AI to have conceptualization as we understand it we need AI to have similar sensory inputs and similar arrays of potential actions. And sure, we could copy the code from one AI to the next to have identical minds at t0, but I struggle with the ethics of that and really I'd rather have diversity in AIs than to have a bunch of clones running around thinking with the same types of thought patterns. The problem I have once I think about it is that this line of thinking leads me to be much less sure of the nature of my own existence. Do we first let the mind of an AI develop to appreciate humanity before letting it know that it is an AI? Seems like it would solve a lot of possible problems since Ghandi wouldn't take the murder pill. http://lesswrong.com/lw/2vj/gandhi_murder_pills_and_mental_illness/ http://lesswrong.com/lw/2vj/gandhi_murder_pills_and_mental_i...

chillee 9y ago

I think it's pretty clear that neural networks are developing concepts/abstractions of the object it's classifying. Check https://distill.pub/2017/feature-visualization/appendix/ https://distill.pub/2017/feature-visualization/appendix/ I think some of the filters are pretty clearly developing "higher levels of abstractions".

argonaut 9y ago
It's not an interesting question whether networks learn abstractions. It's almost tautological - an image classification network will by definition (attempt to) distill an image into a distribution over categories. So when people criticize abstraction I think they are really criticizing the quality of the abstraction... Because the key phrase in the grandparent post is "really represent or mean." Grass + white strands = sheep, is a hierarchical abstraction, but it's a bogus one. Feature visualizations do not answer this more relevant question.

sgk284 9y ago

It's not entirely clear to me that all of cognition isn't simply pattern matching with learned responses. It seems the human brain may just be doing more nuanced matching / responding.

idrios 9y ago
I remember reading (don't remember where) that the current state of machine learning is a lot like the state of bridges a few thousand years ago. People basically try different approaches using intuition and sometimes if the results are good they'll tell other people about it, nowadays in the form of research papers getting published. However, the generalized equations that facilitate modern civil engineering were far from existing. Similarly, we don't yet have general equations to derive or optimize a machine learning algorithm for a given problem as input, we just choose an algorithm, choose parameters that we think make sense, go through a lot of trial and error, then let the algorithm run. There's a lot that's still not understood about the brain. Even at the cellular level, microglia have gained attention recently for their contribution to human learning [1]. The NN algorithm was modeled after human neurons for having n inputs and 1 binary output. It's hard to say how cells that regulate the cellular environment and communicate with each other via cytokines would affect the abstraction of the brain to an algorithm or mathematical model. At least for this example, there's a book called "the other brain" about how glia, which make up ~85% of the brain, perform a myriad of operations beyond just keeping neurons together [2]. But you could also look at the brain through a genetic lens. A lot of the neurosystem is simply hardwired. The knee-jerk response is a reflex arc, which means the signal goes directly from the sensory nerve to the motor nerve (causing you to kick) before it even gets to your brain. That reflex response has been hardcoded into your DNA. How much of the rest of the brain is learned vs. predeterminantly structured? Cognition can probably be reduced to just pattern matching with learned responses, but it's a bit like an ancient Roman looking at New York City skyscrapers and saying "these are just built using arches and a lot of nuance". [1] https://www.sciencedirect.com/science/article/pii/S1074742713001159 https://www.sciencedirect.com/science/article/pii/S107474271... [2] http://www.simonandschuster.com/books/The-Other-Brain/R-Douglas-Fields/9780743291422 http://www.simonandschuster.com/books/The-Other-Brain/R-Doug...

marmaduke 9y ago

My 3 year old makes similar mistakes. I expect he’ll improve in large part because he isn’t just a set of layers but has subcritical areas, fears and motivations and a body to explore the world with. This suggests that we these large ML models do need complementary components to improve. For example, if they were capable of active exploration of an image, they may have said there are sheep and then upon searching for the sheep, realize they aren’t there.

deleted 9y ago

[deleted]

bobthechef 9y ago

> As of right now, a well trained convolutional neural network is no more than a mechanical pattern matching algorithm on steroids. And computers are at best mechanical pattern matching machines. This isn't something that's subject to empirical evidence, and appeals to ignorance or the limits of our current understanding will not do. Computers cannot be any more than that by definition (and arguably the word "matching" is being used in an analogous fashion; rather, a computer configured with an algorithm is such that given an initial state, it will lead to a final state that, when interpreted by a human being, can be interpreted consistently in the desired manner, i.e., a final state of 0 means that the initial configuration encoded two state that match, and a final state of 1 means that they did not). Abstraction is, by definition, NOT reducible to a mechanical process like this. The human interpreter is the one possessing abstract concepts and who interprets by means of meanings conventionally assigned to symbols or machine states. A machine may contain a state that we call an image, but no process can in-principle abstract anything from this aggregation of states. To claim otherwise is to completely misunderstand what symbol manipulation is. (Even a human being couldn't abstract anything from an image in a mode analogous to the way in which a computer operates. By analogy, given a matrix of RGB values, could you tell me what's in the image? Or could you at best compute, say, a value that, when looked up in an already given table of values, gives you a label such as "sheep"?) However, that does not mean that AI cannot perform well, at least within narrow constraints. It may very well be possible to improve AI techniques to such a degree that it can assign the label "sheep" correctly with high accuracy. There simply is an in-principle difference between AI and actual intelligence.

fizx 9y ago

When you talk about mapping (patterns of the) the input pixels to discrete classes, I don't think thats entirely what people do. We have the ability to make "distributed representations" of concepts e.g. word2vec, GloVe, etc which contain the idea that a sheep is pretty similar to a dog. The classes are far from discrete. I'm pretty sure people train image recognizers to output these representations, which can include states like 51% certainty dog, 48% certainty sheep, 1% other, and if you aren't sure, take the best choice. Its such an intuitive idea to combine these things that if it hasn't happened in the literature yet (I only looked for 1 minute), its because 1000 people have tried it, failed to improve the state of the art, and didn't publish. On the other hand, we're generally pretty bad at inferring and generalizing 3d structure out of images, so I tend to blame that.

fizx 9y ago
If you look at the fast.ai lesson 11 YouTube video, the very beginning has an amazing example of this with fish!