This is more interesting than GPT-4 to me, by a great deal. Thank you for sharing!

Optimization and ownership of your full product is important. This is how we combat being locked out of the gated community, providing tangible value through running code.

I am going to check it out this evening!

You don't need a nuclear bomb to hunt elk.

This is a solution you can fully own on top of that.

It has value.

I've actually had pretty good success on using torch.compile for some of the stuff that KeOps works well for!

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Great, thank you so much for a detailed answer. Do you have anything you could point me to (or explain further) about how I could modify a diffusion method to do this?
Also, in terms of the VAE, I was thinking I'd be able to feed 2 channels in and train it to output 3 channels, I believe the encoder wouldn't be useless in this case and hence my latent would be more than merely the missing channel? Feel free to correct me if I'm wrong! My assumption is that even with this a NN may well perform better, or at least a simpler baseline. That said, my images will be similar in certain ways, so being able to model a distribution of the latents could prove useful presumably?

definitely not.

Yes, although I am specifically looking into the reasoning of "at least 10 datapoints per variable."

What is the mathematical reasoning of this minimum?

That's what I think too, but obviously people are free to solve this any way they want!

You're right. They're worse.

You spent more money in the website than the project itself…

Awesome!

Agree 100% that it is important to have people embedded in product teams who have accountability for it.

Ai ethics teams are also useful because they understand and keep track of the metrics and the benchmarks and methods used to evaluate biases, risks and harm. This is a super specialized area of knowledge that the whole company and community can capitalize on. It is also hard to keep it up to date- needs close ties to civic society and academic institutions, etc. . Think of it as if you have to set up a “pipeline”, a supply chain of practices, that start with real world insight and academic research and ends with actionable and implementable methods and code and tools.

In very large orgs, having specialized teams helps scale up company wide processes for incident response, policy work, etc.

You can see some of the the output of this work at Microsoft if you search for Sarah Bird’s presentations.

(cheers from another ML person who also worked w reco)

I am not quite familiar with deep learning but don’t you have loss function where you can maximize recall precision or AUC? I believe accuracy would not apply in this case since you have imbalanced dataset, also over sampling as it dealed in random forest you are making up new images i don’t know how good is that, why don’t you try under sampling better or weight adjustments?

Computers have gotten literally 100 million times faster within my lifetime. I'm not even that old!

man its funny that 250M is a toy now

how far we've come...

What you can try is to start with linear or log Regression and try to learn on Wikipedia. That might be fun and give you decent results.

The value that nanoGPT offers is that it is a self-contained (minimal dependencies), easy to understand code. This repo is essentially a wrapper for huggingface's models, dataset and accelerator, which is not very useful for didactic purposes.

Than I would just use a completely different test dataset. In a paper I would also expect this.

Damn I hope no one got me wrong I wanted to learn the basics in a week (and finish my side project asap) I didn’t claim I could study such a large and complex field in a week.

I'll leave it to the linguists to debate UG and the specifics of what it does and doesn't mean, but commonalities like some sort of hierarchy, recursion, structure-dependence of rules, etc clearly exist, whatever you want to call them. By shared I just mean there's specific things that human cognitive faculties are set up to do and then other (often computationally simpler) things they clearly don't do. But again, if you're just saying natural languages are not formal languages, I guess that's true by definition. It just sounded to me like you were implying something different.

Thanks for funding this, it looks like a cool project.

1 - Data engineering and DevOps
2 - It's way less stressful than ML because you have really clear requirements ( I need to get data from a source in a certain target with those constraints ). This sometimes can be challenging due to business requirements (Time, consistency, and monitoring those pipelines) but I find it better than go into a project where I don't even know if it will be feasible or no.
3 - I was a good programmer before I got to ML, so for me it was like I switched back to what I used to do, so it was not a big deal. ( My curriculum was a lot of software engineering / managing networks and pure dev)

Oh, it is clearly structured. Words and phrases and sentences are all forms of structure and we're using them right now.

What it doesn't have is formal structure; it cannot be fully defined by any set of rules. This is why you can't build a rules-based parser that understands english and have to use an 800GB language model instead.

>shared across essentially every language and dialect

Noam Chomsky thinks this, but the idea of a universal grammar is controversial in modern linguistics.

Neat concept, compile, but still has some limitations for the models I used them on (complex-valued tensors, pykeops, CUDA kernels). Some pretty great advancements otherwise. Will probably help when training transformers.

https://en.m.wikipedia.org/wiki/Nat_Friedman