Because it requires the least amount of human intervention

Also because it subjectively sounds like magic to people who don't really understand it, so it both sells to management and to consumers.

At least it's easier for humans to cope it is like magic than to accept that a lot of what AI can do is just stuff that is trivial and doesn't require humanity to solve.

Then why DL?

But why DL beats the curse? Why is DL the only class?

Yes a very finite grid size will approximate any digital image. But this is an approximation of an image in grids. How will it lead to approximation by NN?

Anything that scales sub-quadraticaly?

Anything "big-data"

But DL is not a linear model. Then what will be the limit of DL?

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Then why not use decision trees instead of DL?

Yes but is DL the unique mechanism? Why DL?

Then what will be the limitation of transformers?

I don't know if it is the current best AI to clone voices, but there is a zero-shot model named YOUR-TTS, it has pre-trained weights available and you only need around 1 min of your voice to make it sound quite similar. But you can always retrain it with more samples of your voice to improve the performance even more.

https://github.com/Edresson/YourTTS

I think it was also added to the Coqui-TTS toolkit.

https://github.com/coqui-ai/TTS

However I only played around with the demos in the original repo, and therefore don't know how to use it if you are serious about voice cloning.

Define scale

Language models? Sure. Images? Sure. Huge amounts of transaction data to search for fraud? Xgboost all the way lol.

Church no free lunch theorem: there is no single approach best for every possible problem. Djeezes I hate it when marketing takes over. You learn this principle in the first chapter of literally every data course

Wrong.

We know how it works. Someone designed it. What he’s talking about is a lack of interpretability around what goes on in the hidden layers and why the model produces specific outputs. It’s not magic.

Apparently* decision trees are also capable of [universal function approximation](https://cstheory.stackexchange.com/a/46405).

Whether the algorithms for training them do that as well as the ones for deep NNs in practice is a separate issue.

*Haven't seen (& probably wouldn't understand) a proof.

This is correct for two-layer NNs, not general NNs.

Universal approximation is not enough, you need efficiency to make things work.

DL is the only class of algorithms that beats the curse of dimensionality for discovering certain (very general) class of high dimensional functions(something related to Barron space). Point me out if this is not accurate.

And on my 8 GB GTX 1080.

Dr. Zhang, thank you so much. Please can you tell us more about your model's performance ? How would it do on standard MMLU ? Can it be improved by increasing parameters count ? The paper didn't mention if the human testers were average human or experts ?

Thanks a lot :)

For example, in IRIS dataset, the class label is not a linear combination of the input. Therefore, if your model class is all linear models, you won't find the optimal or in this case, even a good solution.

If you extend the model class to include non-linear functions, then your hypothesis space now at least contains a good solution, but finding it might be a bit more trickly.

Thank you. I understand the math. But I meant a real world example that "the solution is not in the model class."

Linear models make an assumption that the solution is in the form of y=ax+b. If the solution is not in this form then the best solution will is likely to be a poor solution.

I think Emma Brunskill's notes are quite good at explaining this. Essentially the model will underfit as it is too simple. I am making an assumption though, that a large dataset implies a more complex non-linear solution, but this is generally the case.

Really interesting! What are the minimum requirements to run this?

>Large linear models tend not to scale well to large datasets if the solution is not in the model class

Will you provide me a reference?