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Compression is a necessary but not sufficient condition for 'comprehension'. The whole 'compression' idea here is a bit of a misdirection; it will be a side eff

Compression is a necessary but not sufficient condition for 'comprehension'. The whole 'compression' idea here is a bit of a misdirection; it will be a side effect of any success. The data fed into these systems is just measurements of target systems (eg., of light for photographs). This data is radically incomplete, so no compression of it will be an accurate (eg., 3d) model. To reconstruct the world you need to measure the measuring device (ie., the body) as it interacts with the target system. In most cases you need, also, the hypothesis behind the action to resolve ambiguity in the measurement data. Eg., you need to know you moved your hand to touch the fireplace to properly interpret what 'finger pain' means. It is for reasons of this kind that 'comprehension' is an engineering problem, not a programme for a universal turing machine (which has no device boundaries). It is engineering in the sense that 'compression' has to occur under the right measurement procedures, with hypothesis-laden action, etc. Focusing on mathematical abstracta completely misses the problem. All deep systems do is compress their training data to form archetypes and compare novel input to compressed archetypes. Since the data itself is necessarily profoundly ambiguous there is only a trivial sense of 'generalisation' achievd.

0x008 6y ago
Such a great comment, thank you
space_rock 6y ago
I will nitpick and say that models don't compress data but extract useful information. Compression lossly or lossless is for data reconstruction. Machine learning models retain information that generalises https://arxiv.org/abs/1906.05849 https://arxiv.org/abs/1906.05849
- jhgb 6y ago
  Isn't lossy compression pretty much the same thing as extracting some amount of useful information?
  - Retric 6y ago
    No, because lossy compression has the goal of preserving elements for replay. At the extreme you could turn a movie into subtitles, but not a costume from the movie alone. The costume might contain more information from the movie, but you can meaningfully recreate the movie from a single costume.
    - jhgb 6y ago
      If I derive Kepler's laws from positions of planets, I have preserved elements of their motion for replay as well.
      - Retric 6y ago
        The laws alone don’t have enough information to replay their motion without each planets location, a foci, the orbital period, and an associated timestamp or equivalent information. So, include that data and it’s lossy compression, exclude it and it’s not.
  - teruakohatu 6y ago
    According to Wikipedia [data] compression "is the process of encoding information using fewer bits than the original representation" [1] while lossy compression is "the class of data encoding methods that uses inexact approximations and partial data discarding to represent the content" [2] There is a difference between feature extraction and compression. The former selects the most unique and important elements from a set of data, while the latter attempts, in the case of lossy compression, to find a smaller approximation of the original data set. For example imagine a data set of paintings. Feature extraction might simply identify what color paints were used in each painting. That alone might allow classification of paintings with respect to the painter, style or period. But this would not be an approximation of the original picture, unless you consider resizing a Jackson Pollock into an 8x1 pixel image to be compression. [1] https://en.wikipedia.org/wiki/Data_compression https://en.wikipedia.org/wiki/Data_compression [2] https://en.wikipedia.org/wiki/Lossy_compression https://en.wikipedia.org/wiki/Lossy_compression
- viraptor 6y ago
  Is there a meaningful / well defined difference? One could say that lossy compression is extraction of useful information. You need to identify unnecessary information to know what to discard safely.
  - mjburgess 6y ago
    One is a subset of the other. Compression being the larger category. A mean is a compression of a dataset and useful information. My issue in my comment is that 'compression' corresponds to a massive class of technquies and there isnt a lot of content in the observation that useful information is compressive. However there is some hypey people out there who think this observation has legs. Precisely the people who think intelligence is a mathematical problem, and not an engineering one -- which is my view. ie., that a body isnt incidental to intelligence, but the heart of it. Or: devices matter.
    - typon 6y ago
      I tend to think about it the same way as you. Having an algorithm for multiplying two numbers is qualitatively different than having a lossy compression of a huge dataset of multiplication tables. The latter is what GPT3 has and it just doesn't scale.
  - space_rock 6y ago
    Consider a generative adversarial network for faces. Photos have let's say some unique scars A successful model could create faces with scars in them. But not the exact scar and face and background that they trained on without additional information. What you are looking for is mutual information between the images not compression Yes you could use a very well trained GAN for face compression. But a GAN model itself would not be able to reconstruct its training input without being shown the images again
wokwokwok 6y ago
> Focusing on mathematical abstracta completely misses the problem... I don’t think it does. We’ve been studying these networks for a long time now, and “common sense” interpretations are largely “completely bs” when examined closely. These investigations have a tangible impact on how to prune and design networks. High level hand waving about measuring the measuring device may seem profound, but I fail to see any meaningful application. You can reconstruct 3D images from a few samples; see neural radiance fields. The observer is irrelevant.
- mjburgess 6y ago
  My comment here wasnt aimed at the problem of understanding and improving DNNs; rather, of generalisation and 'comprehension' proper. 3d image reconstruction is trivially possible, with the right data and with the right assumptions encoded into the right algorithm. My target of attack here is the equivocation that 'compression is comprehension'. In my view comprehension isnt about 'generalization via compressed archetypes' as in ordinary NNs. And it isnt about 3d reconstruction given the relevant modification to data and approach. Rather generalization is abduction. It is the ability to, via a single instance, form law-like provisional universalising models which explain your environment. This process will lead to a 'compressed', 'representation', but nothing like the sense in which compression is here used. It is this that intuitively is naively assumed of these systems. They do not, and cannot, abduct. Abduction isnt a statistical process; involving at the very least, counterfactual reasoning and hypothetical action. This is the problem that such equivocations miss (ie. that c = c). And in my view this is an engineering challenge; not something to be specified for a universal computer. The relevant capacities missing arent better means of compression.
  - wokwokwok 6y ago
    > In my view comprehension isnt about 'generalization via compressed archetypes' as in ordinary NNs... How is this helpful? Be specific. The point being made is that inductions can be made by a compressed representation of the data. No one is claiming this is a key insight to AGI; you’re just taking the word “comprehension” and arbitrarily assigning it some personal meaning. This is why mathematicial descriptions are useful; you’re personal view of what “comprehension” means isn’t relevant here. The article doesn’t discuss comprehension. That is something you’ve, as far as I can tell, meaninglessly brought into this discussion. How are your insights helpful for improving DNNs? Be specific.
logicchains 6y ago
>Compression is a necessary but not sufficient condition for 'comprehension'. Actually, algorithmic information theory shows that maximum compression necessarily entails maximum comprehension, because the only way to maximally compress something is to exactly understand the process producing it.
- mjburgess 6y ago
  Relative to a device to perform the understanding. A string X maximally compresses datsets Y iff X is a 'comprehending' of Y. 'OK'... but what produces and evaluates X? ie., comprehension. This is the problem with defining these terms mathematically; you state the problem in basically useless ways. Yes, you can specify what eqn produces the mass of the higgs boson. Thats basically no guide to building the LHC. The production of such understanding is not abstract. Comprehension isnt a relation between two binary strings; it is an action taken in an environment with a goal.
  - logicchains 6y ago
    >Relative to a device to perform the understanding. It uses Kolmogorov complexity, which is defined as the length of a shortest computer program in a predetermined programming language that produces the object as output. Note that this measure is relative to a programming language, not a program, and the exact choice of language doesn't matter too much. Compression means creating a smaller program that produces the same output, and to produce the same output with less code necessarily requires more understanding. As a concrete example, imagine the output is [1,2,fizz,4,buzz,fizz,7,8,fizz,buzz,11,fizz,13,14,fizzbuzz..1000). The longest program to output this would just hard-code it in the source code (much as a very inexperienced programmer might solve the problem, or a large neural net). Someone with a better understanding would write a program using iteration and the modulus operator, which would be shorter.
- gbrown 6y ago
  But what if we drop the word comprehension, and we just go with “functional approximation X -> Y, computed from a finite dataset, which minimizes a predictive risk “? It’s unclear why compression is necessary there, except as a practical benefit.
sfink 6y ago
> Focusing on mathematical abstracta completely misses the problem. That rather depends on what you think the problem is or ought to be. I'm not sure that the author or many readers agree with you. It seemed fairly clear to me that they are interested in how well they can generate outputs corresponding to novel inputs that do well on some utility function. And for that, the "mathematical abstracta" is rather important and useful. "Comprehension" might also be important and useful, but is itself neither necessary nor sufficient. A self-driving car will hopefully try to avoid running over a two-headed person crossing a road, without needing to worry about whether a two-headed person is mythical vs a miscomprehension of a woman carrying a child. Besides, the layer at which comprehension occurs is not fixed. Our measuring devices themselves take in ambiguous inputs and process them. Do you stop at the surface of the skin? The nerve impulses? Various layers within the brain and nervous system? Your complaint is reasonable, yet is not really that different from saying that computers are pointless without any I/O capabilities -- it doesn't matter what they compute if it is unobservable and has no effect on the world. That is true, yet doesn't mean that the whole of computer science is "a bit of a misdirection".

0x008 6y ago

Such a great comment, thank you

space_rock 6y ago

I will nitpick and say that models don't compress data but extract useful information. Compression lossly or lossless is for data reconstruction. Machine learning models retain information that generalises https://arxiv.org/abs/1906.05849 https://arxiv.org/abs/1906.05849

jhgb 6y ago
Isn't lossy compression pretty much the same thing as extracting some amount of useful information?
- Retric 6y ago
  No, because lossy compression has the goal of preserving elements for replay. At the extreme you could turn a movie into subtitles, but not a costume from the movie alone. The costume might contain more information from the movie, but you can meaningfully recreate the movie from a single costume.
  - jhgb 6y ago
    If I derive Kepler's laws from positions of planets, I have preserved elements of their motion for replay as well.
    - Retric 6y ago
      The laws alone don’t have enough information to replay their motion without each planets location, a foci, the orbital period, and an associated timestamp or equivalent information. So, include that data and it’s lossy compression, exclude it and it’s not.
- teruakohatu 6y ago
  According to Wikipedia [data] compression "is the process of encoding information using fewer bits than the original representation" [1] while lossy compression is "the class of data encoding methods that uses inexact approximations and partial data discarding to represent the content" [2] There is a difference between feature extraction and compression. The former selects the most unique and important elements from a set of data, while the latter attempts, in the case of lossy compression, to find a smaller approximation of the original data set. For example imagine a data set of paintings. Feature extraction might simply identify what color paints were used in each painting. That alone might allow classification of paintings with respect to the painter, style or period. But this would not be an approximation of the original picture, unless you consider resizing a Jackson Pollock into an 8x1 pixel image to be compression. [1] https://en.wikipedia.org/wiki/Data_compression https://en.wikipedia.org/wiki/Data_compression [2] https://en.wikipedia.org/wiki/Lossy_compression https://en.wikipedia.org/wiki/Lossy_compression
viraptor 6y ago
Is there a meaningful / well defined difference? One could say that lossy compression is extraction of useful information. You need to identify unnecessary information to know what to discard safely.
- mjburgess 6y ago
  One is a subset of the other. Compression being the larger category. A mean is a compression of a dataset and useful information. My issue in my comment is that 'compression' corresponds to a massive class of technquies and there isnt a lot of content in the observation that useful information is compressive. However there is some hypey people out there who think this observation has legs. Precisely the people who think intelligence is a mathematical problem, and not an engineering one -- which is my view. ie., that a body isnt incidental to intelligence, but the heart of it. Or: devices matter.
  - typon 6y ago
    I tend to think about it the same way as you. Having an algorithm for multiplying two numbers is qualitatively different than having a lossy compression of a huge dataset of multiplication tables. The latter is what GPT3 has and it just doesn't scale.
- space_rock 6y ago
  Consider a generative adversarial network for faces. Photos have let's say some unique scars A successful model could create faces with scars in them. But not the exact scar and face and background that they trained on without additional information. What you are looking for is mutual information between the images not compression Yes you could use a very well trained GAN for face compression. But a GAN model itself would not be able to reconstruct its training input without being shown the images again

wokwokwok 6y ago

> Focusing on mathematical abstracta completely misses the problem... I don’t think it does. We’ve been studying these networks for a long time now, and “common sense” interpretations are largely “completely bs” when examined closely. These investigations have a tangible impact on how to prune and design networks. High level hand waving about measuring the measuring device may seem profound, but I fail to see any meaningful application. You can reconstruct 3D images from a few samples; see neural radiance fields. The observer is irrelevant.

mjburgess 6y ago
My comment here wasnt aimed at the problem of understanding and improving DNNs; rather, of generalisation and 'comprehension' proper. 3d image reconstruction is trivially possible, with the right data and with the right assumptions encoded into the right algorithm. My target of attack here is the equivocation that 'compression is comprehension'. In my view comprehension isnt about 'generalization via compressed archetypes' as in ordinary NNs. And it isnt about 3d reconstruction given the relevant modification to data and approach. Rather generalization is abduction. It is the ability to, via a single instance, form law-like provisional universalising models which explain your environment. This process will lead to a 'compressed', 'representation', but nothing like the sense in which compression is here used. It is this that intuitively is naively assumed of these systems. They do not, and cannot, abduct. Abduction isnt a statistical process; involving at the very least, counterfactual reasoning and hypothetical action. This is the problem that such equivocations miss (ie. that c = c). And in my view this is an engineering challenge; not something to be specified for a universal computer. The relevant capacities missing arent better means of compression.
- wokwokwok 6y ago
  > In my view comprehension isnt about 'generalization via compressed archetypes' as in ordinary NNs... How is this helpful? Be specific. The point being made is that inductions can be made by a compressed representation of the data. No one is claiming this is a key insight to AGI; you’re just taking the word “comprehension” and arbitrarily assigning it some personal meaning. This is why mathematicial descriptions are useful; you’re personal view of what “comprehension” means isn’t relevant here. The article doesn’t discuss comprehension. That is something you’ve, as far as I can tell, meaninglessly brought into this discussion. How are your insights helpful for improving DNNs? Be specific.

logicchains 6y ago

>Compression is a necessary but not sufficient condition for 'comprehension'. Actually, algorithmic information theory shows that maximum compression necessarily entails maximum comprehension, because the only way to maximally compress something is to exactly understand the process producing it.

mjburgess 6y ago
Relative to a device to perform the understanding. A string X maximally compresses datsets Y iff X is a 'comprehending' of Y. 'OK'... but what produces and evaluates X? ie., comprehension. This is the problem with defining these terms mathematically; you state the problem in basically useless ways. Yes, you can specify what eqn produces the mass of the higgs boson. Thats basically no guide to building the LHC. The production of such understanding is not abstract. Comprehension isnt a relation between two binary strings; it is an action taken in an environment with a goal.
- logicchains 6y ago
  >Relative to a device to perform the understanding. It uses Kolmogorov complexity, which is defined as the length of a shortest computer program in a predetermined programming language that produces the object as output. Note that this measure is relative to a programming language, not a program, and the exact choice of language doesn't matter too much. Compression means creating a smaller program that produces the same output, and to produce the same output with less code necessarily requires more understanding. As a concrete example, imagine the output is [1,2,fizz,4,buzz,fizz,7,8,fizz,buzz,11,fizz,13,14,fizzbuzz..1000). The longest program to output this would just hard-code it in the source code (much as a very inexperienced programmer might solve the problem, or a large neural net). Someone with a better understanding would write a program using iteration and the modulus operator, which would be shorter.
gbrown 6y ago
But what if we drop the word comprehension, and we just go with “functional approximation X -> Y, computed from a finite dataset, which minimizes a predictive risk “? It’s unclear why compression is necessary there, except as a practical benefit.

sfink 6y ago

> Focusing on mathematical abstracta completely misses the problem. That rather depends on what you think the problem is or ought to be. I'm not sure that the author or many readers agree with you. It seemed fairly clear to me that they are interested in how well they can generate outputs corresponding to novel inputs that do well on some utility function. And for that, the "mathematical abstracta" is rather important and useful. "Comprehension" might also be important and useful, but is itself neither necessary nor sufficient. A self-driving car will hopefully try to avoid running over a two-headed person crossing a road, without needing to worry about whether a two-headed person is mythical vs a miscomprehension of a woman carrying a child. Besides, the layer at which comprehension occurs is not fixed. Our measuring devices themselves take in ambiguous inputs and process them. Do you stop at the surface of the skin? The nerve impulses? Various layers within the brain and nervous system? Your complaint is reasonable, yet is not really that different from saying that computers are pointless without any I/O capabilities -- it doesn't matter what they compute if it is unobservable and has no effect on the world. That is true, yet doesn't mean that the whole of computer science is "a bit of a misdirection".