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> the required information was within the model the whole time. This, being a restatement of the concept of "latent space", though... makes me realize even mor

> the required information was within the model the whole time. This, being a restatement of the concept of "latent space", though... makes me realize even more how much I hate that concept. Yes, every color or every pixel in a 2D square exists in the enormous multidimensional latent space of a model. But when that space is essentially "all images never seen before, all books never written," then consigning all that to potential model output if you rub the genie the right way is the same as wasting your three wishes on nothing. For your and my latent creative space is just as multidimensional and broad, but has the benefit of intersecting with reality. Intersecting with reality narrows all our potential output down significantly and also increases the chances that we'll fill a blank square with more signal than noise, so therefore, we know that limits are good and beneficial to structured thought... and yet still, there's this crazy obsession with the unbounded latent space. Is it a signal that we've become unmoored from reality, that we'd build a tower to heaven out of actual babble?

haswell 4y ago
> Is it a signal that we've become unmoored from reality, that we'd build a tower to heaven out of actual babble? I think there’s a case to be made that we’ve been unmoored from reality for quite some time now. Recent developments seem closer to finishing that unmooring than starting it.
- twic 4y ago
  And once we are finally unmoored from reality, we can set sail for whatever ultraterrestrial destination our cybernetic helmsmen have in mind.
gnramires 4y ago
The latent space is useful because it reduces the dimensionality, in some cases to one-hot encoding. Yes, the "latent space" (it's not latent if it's in the input :) ) of a pixel map is of very little information (but not zero[1]). But neural nets can build very informative latent dimensions, which can correspond to abstractions: the network can abstract things like "Is this object a car?", "how red is this object?", "how large is it?", and so on, as latent parameters. Abstraction is one of the fundamental properties of (efficient) thought, that allow dealing with such large amounts of information efficiently by reducing its complexity, reducing it to essential data, that's essentially what latent parameters can be. Indeed I believe several experiments have shown emergence of such latent parameters (which confirms expectations). Also several papers do "latent space interpolation" for generative modeling, which allows mixing of abstract notions (such as gender, size, age, and so on) right in latent space to produce interpolated results (such as images or even text, etc.) that are not pixel-space averages but "conceptual averages" (conceptual interpolation) of some results. This possibility is also evidence of the validity of interpreting the latent space as abstracted data[2]. [1] For example, you can use the pixel space directly when comparing very similar images, say affected by additive noise, using Euclidean distance in pixel space is a pretty good distortion metric for human vision (and you can do classification in some cases as well of say digits using euclidean KNN in pixel space but depending on very large datasets). [2] I think it's important to note that abstraction isn't necessarily quite 'dimensionality reduction', or removal of redundant data. I think abstraction is more like computational simplification, and in some cases you might need even more intermediary simplified data than you began with (consider the memory usage of some efficient algorithms that far exceeds the dataset size). So the abstract classes and dimensions of the data could be even more than the data size itself, but they tend to signal computationally independent characteristics, like say 'is there a car in the image?' and 'is there a person in the image?' -- you could have thousands of such classes each represented in a single dimension in binary encoding.
rjbwork 4y ago
>Is it a signal that we've become unmoored from reality, that we'd build a tower to heaven out of actual babble? This is more or less the thesis of Hypernormalization (link below) - that the intelligentsia and political classes have, instead of contending with the enormous complexity of reality, constructed a kind of facsimile of reality with which to interact with. https://www.youtube.com/watch?v=thLgkQBFTPw https://www.youtube.com/watch?v=thLgkQBFTPw

haswell 4y ago

> Is it a signal that we've become unmoored from reality, that we'd build a tower to heaven out of actual babble? I think there’s a case to be made that we’ve been unmoored from reality for quite some time now. Recent developments seem closer to finishing that unmooring than starting it.

twic 4y ago
And once we are finally unmoored from reality, we can set sail for whatever ultraterrestrial destination our cybernetic helmsmen have in mind.

gnramires 4y ago

The latent space is useful because it reduces the dimensionality, in some cases to one-hot encoding. Yes, the "latent space" (it's not latent if it's in the input :) ) of a pixel map is of very little information (but not zero[1]). But neural nets can build very informative latent dimensions, which can correspond to abstractions: the network can abstract things like "Is this object a car?", "how red is this object?", "how large is it?", and so on, as latent parameters. Abstraction is one of the fundamental properties of (efficient) thought, that allow dealing with such large amounts of information efficiently by reducing its complexity, reducing it to essential data, that's essentially what latent parameters can be. Indeed I believe several experiments have shown emergence of such latent parameters (which confirms expectations). Also several papers do "latent space interpolation" for generative modeling, which allows mixing of abstract notions (such as gender, size, age, and so on) right in latent space to produce interpolated results (such as images or even text, etc.) that are not pixel-space averages but "conceptual averages" (conceptual interpolation) of some results. This possibility is also evidence of the validity of interpreting the latent space as abstracted data[2]. [1] For example, you can use the pixel space directly when comparing very similar images, say affected by additive noise, using Euclidean distance in pixel space is a pretty good distortion metric for human vision (and you can do classification in some cases as well of say digits using euclidean KNN in pixel space but depending on very large datasets). [2] I think it's important to note that abstraction isn't necessarily quite 'dimensionality reduction', or removal of redundant data. I think abstraction is more like computational simplification, and in some cases you might need even more intermediary simplified data than you began with (consider the memory usage of some efficient algorithms that far exceeds the dataset size). So the abstract classes and dimensions of the data could be even more than the data size itself, but they tend to signal computationally independent characteristics, like say 'is there a car in the image?' and 'is there a person in the image?' -- you could have thousands of such classes each represented in a single dimension in binary encoding.

rjbwork 4y ago

>Is it a signal that we've become unmoored from reality, that we'd build a tower to heaven out of actual babble? This is more or less the thesis of Hypernormalization (link below) - that the intelligentsia and political classes have, instead of contending with the enormous complexity of reality, constructed a kind of facsimile of reality with which to interact with. https://www.youtube.com/watch?v=thLgkQBFTPw https://www.youtube.com/watch?v=thLgkQBFTPw