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It amazes me people think their brain works differently. It's pretty close to believing in body/mind dualism, the only thing in neuroscience more outdated than

It amazes me people think their brain works differently. It's pretty close to believing in body/mind dualism, the only thing in neuroscience more outdated than Freud. Your brain works within the same laws of physics as the outside world. We don't know how the brain works exactly. But once we understand it, it is unlikely to be qualitatively different from a neural network. On the other side of the equation, the emergent behaviour of deep networks make them fundamentally different from the sort of statistics that came before. So even if the building blocks are old and boring, the larger system isn't.

visarga 6y ago
What we need here, I think, is a GAN-like approach. Use a generator + a discriminator. The generator could be like GPT-3, but it needs another model to filter out the nonsense. We work the same way - come up with crazy ideas, then check them out to see if they hold ... 'I had an idea, but nah, it wouldn't work'. It's a process supported by different skills.
snowwrestler 6y ago
The issue isn't whether neural networks work, the issue is the dataset. Look again at what the parent wrote: > I'm always amazed that some people think performing statistical analysis (training neural networks) on text can lead to actual intelligence and knowledge about the world Emphasis mine. The human brain does not develop what we know as human intelligence by analyzing text.
tsimionescu 6y ago
As another poster wrote, the main problem is not the neural network itself, it is the training set, and very likely the training algorithm as well. My claim is that working knowledge of the world is simply not encoded in human text. All human text (and speech) presupposes a certain model of the world that is learned from experiencing the world, likely based on a model that itself is partially pre-trained by evolutionary processes.
- hackinthebochs 6y ago
  If a text corpus correlates with human knowledge (your "presupposes a certain model of the world"), then it necessarily encodes the portions of the knowledge it correlates with. That is, correlations drive the search of model parameters that explain the correlations. The stronger or more numerous the correlations, the more accurate a model can be computed. But the search space informed by this network of correlations is much more restricted than a naive brute force search, thus the correlations provide information about, i.e. encodes, the correct model.
  - tsimionescu 6y ago
    Then I used the wrong words. My point is that human text is essentially encrypted using a particular model of the world as the encryption/decryption key. You can't derive the key from the cipher text. For example, if I give you the text 'the car is red', there is no watly to imagine the meaning of this phrase (in any sense of the word 'meaning') unless you have some understanding of what cars are, what red is, and what it means for a car to be red (which is somewhat different from what it means for an apple to be red, for example). It is interesting that with enough statistical analysis you can begin to decipher certain parts of the true message without knowing the cipher key. But this only works for those parts where you have a large corpus of text talking about them, and there are many common human needs or experiences that do not have such a corpus. It is also very easy to capture answer/response pairs in the corpus and repeat them, seeming to have high accuracy, but in fact failing to generalize more than meets the eye. For example, with enough compute power, you could build a machine that, for any query, would find the most similar such query in all of its training data, and respond with the continuation of that query (which could be an answer or a follow-up question). This could be quite impressive in conversation, while doing nothing to elucidate anything about human intelligence, and being wholly unable to adapt to any novel situation.
    - hackinthebochs 6y ago
      Your encryption analogy doesn't really work either. Encrypted text encodes the key used to encrypt it. This is why it takes 2^N computational steps to recover the plain text, where N is the key size rather than where N is the text size. It's just that the computational cost of decrypting with sufficiently large keys is intractable. But human languages have much more exposed regularity than ciphertext from good encryption algorithms, so the computational cost is much lower. The "key", i.e. world model, in case of human languages is plausibly much bigger, but since human languages have significant exposed regularity, it is still tractable to compute, with the trade off that you need much more text to do it.
mjburgess 6y ago
> it is unlikely to be qualitatively different from a neural network I'm sorry but this shows a profound misunderstanding of what a NN is, and what the brain is. There are no "neural network"s. The NN algorithm is a method for optimizing the parameters of a piece-wise linear regression model. These regression models have no homology to any brain structure and the process of producing them ("training") has no neurological analogue either. They can be produced with a variety of algorithms. Here's one: f(x) = max(0, 2max(0, 3x - 0.1) - 0.5) The phrase "neural network" is, as peddled by the media and poorly informed lecturers, a lie. It is neither neural nor a network. It's just gradient descent with more parameters. A model of the brain would model, at least: neuroplasticity, biochemical signalling, activation frequency, etc. There is nothing about a peice-wise linear regression model which does any of that. No matter how many circles and lines you draw. (NB. essentially any mathematical function can be drawn as a "neural network". The "network" is just a way of diagramming function application & dot-products). Aside from all that, the body-brain system of animals is a physical process whose properties are not abstract. The reason we are intelligent is because we have bodies capable of causal analysis; and that capability is biophysical. To put it another way, no algorithm which runs on a digital computer will turn it into gold. Not even one called, "the midas algorithm".
- tsimionescu 6y ago
  Very good points! I would add that we don't yet have a complete model of the computational properties of even a non-neural cell, even though we know that all cells exhibit simplistic perception and decision-making. This is not to say that we should expect some non-turing weirdness or anything 'mystical' like that, but that we just haven't taken the time to analyze the exact mechanisms and to model cells at this level well enough to be able to say that they are similar to any particular computational structure. Thus, to claim that the brain in its entirety is similar to any particular computational structure such as an NN is either wrong or trivial - the only level at which 'similarity' can be claimed is that they are both useful in computation, and likely turing complete. Anything more specific is fanciful thinking.
- i-am-curious 6y ago
  It's amazing just how many people believe that neural networks have a close resemblance to the human brain.

visarga 6y ago

What we need here, I think, is a GAN-like approach. Use a generator + a discriminator. The generator could be like GPT-3, but it needs another model to filter out the nonsense. We work the same way - come up with crazy ideas, then check them out to see if they hold ... 'I had an idea, but nah, it wouldn't work'. It's a process supported by different skills.

snowwrestler 6y ago

The issue isn't whether neural networks work, the issue is the dataset. Look again at what the parent wrote: > I'm always amazed that some people think performing statistical analysis (training neural networks) on text can lead to actual intelligence and knowledge about the world Emphasis mine. The human brain does not develop what we know as human intelligence by analyzing text.

tsimionescu 6y ago

As another poster wrote, the main problem is not the neural network itself, it is the training set, and very likely the training algorithm as well. My claim is that working knowledge of the world is simply not encoded in human text. All human text (and speech) presupposes a certain model of the world that is learned from experiencing the world, likely based on a model that itself is partially pre-trained by evolutionary processes.

hackinthebochs 6y ago
If a text corpus correlates with human knowledge (your "presupposes a certain model of the world"), then it necessarily encodes the portions of the knowledge it correlates with. That is, correlations drive the search of model parameters that explain the correlations. The stronger or more numerous the correlations, the more accurate a model can be computed. But the search space informed by this network of correlations is much more restricted than a naive brute force search, thus the correlations provide information about, i.e. encodes, the correct model.
- tsimionescu 6y ago
  Then I used the wrong words. My point is that human text is essentially encrypted using a particular model of the world as the encryption/decryption key. You can't derive the key from the cipher text. For example, if I give you the text 'the car is red', there is no watly to imagine the meaning of this phrase (in any sense of the word 'meaning') unless you have some understanding of what cars are, what red is, and what it means for a car to be red (which is somewhat different from what it means for an apple to be red, for example). It is interesting that with enough statistical analysis you can begin to decipher certain parts of the true message without knowing the cipher key. But this only works for those parts where you have a large corpus of text talking about them, and there are many common human needs or experiences that do not have such a corpus. It is also very easy to capture answer/response pairs in the corpus and repeat them, seeming to have high accuracy, but in fact failing to generalize more than meets the eye. For example, with enough compute power, you could build a machine that, for any query, would find the most similar such query in all of its training data, and respond with the continuation of that query (which could be an answer or a follow-up question). This could be quite impressive in conversation, while doing nothing to elucidate anything about human intelligence, and being wholly unable to adapt to any novel situation.
  - hackinthebochs 6y ago
    Your encryption analogy doesn't really work either. Encrypted text encodes the key used to encrypt it. This is why it takes 2^N computational steps to recover the plain text, where N is the key size rather than where N is the text size. It's just that the computational cost of decrypting with sufficiently large keys is intractable. But human languages have much more exposed regularity than ciphertext from good encryption algorithms, so the computational cost is much lower. The "key", i.e. world model, in case of human languages is plausibly much bigger, but since human languages have significant exposed regularity, it is still tractable to compute, with the trade off that you need much more text to do it.

mjburgess 6y ago

> it is unlikely to be qualitatively different from a neural network I'm sorry but this shows a profound misunderstanding of what a NN is, and what the brain is. There are no "neural network"s. The NN algorithm is a method for optimizing the parameters of a piece-wise linear regression model. These regression models have no homology to any brain structure and the process of producing them ("training") has no neurological analogue either. They can be produced with a variety of algorithms. Here's one: f(x) = max(0, 2max(0, 3x - 0.1) - 0.5) The phrase "neural network" is, as peddled by the media and poorly informed lecturers, a lie. It is neither neural nor a network. It's just gradient descent with more parameters. A model of the brain would model, at least: neuroplasticity, biochemical signalling, activation frequency, etc. There is nothing about a peice-wise linear regression model which does any of that. No matter how many circles and lines you draw. (NB. essentially any mathematical function can be drawn as a "neural network". The "network" is just a way of diagramming function application & dot-products). Aside from all that, the body-brain system of animals is a physical process whose properties are not abstract. The reason we are intelligent is because we have bodies capable of causal analysis; and that capability is biophysical. To put it another way, no algorithm which runs on a digital computer will turn it into gold. Not even one called, "the midas algorithm".

tsimionescu 6y ago
Very good points! I would add that we don't yet have a complete model of the computational properties of even a non-neural cell, even though we know that all cells exhibit simplistic perception and decision-making. This is not to say that we should expect some non-turing weirdness or anything 'mystical' like that, but that we just haven't taken the time to analyze the exact mechanisms and to model cells at this level well enough to be able to say that they are similar to any particular computational structure. Thus, to claim that the brain in its entirety is similar to any particular computational structure such as an NN is either wrong or trivial - the only level at which 'similarity' can be claimed is that they are both useful in computation, and likely turing complete. Anything more specific is fanciful thinking.
i-am-curious 6y ago
It's amazing just how many people believe that neural networks have a close resemblance to the human brain.