What? How do you read that from my text?

I think most of them probably cares, just as much I'd asume you and I do, about how the next number of years play out for the benefit of man.

the AI Diversity, Equity and Inclusiveness community (AI Ethics)

Now that's a petition I can stand for.

Democratization of LLMs and its derivatives, is in fact, the AI safe way - Counterintuitive as it may sound to the AI DEI folks.

Is that right? I some how end up here when trying to assess what the F.multi_head_attention call does in the Class definition.

But I trust you're right, it would only make sense, I just couldn't identify the calls myself.

+1

Did you create an account, just to ask this question?

I don't think neither CV nor NLP is going away. CV is yet to be solved to the same extent as NLP, but I agree it might just be a matter of time.

Research wise, there are still tons of problems around uncertainty, complexity, causality, 'real-world' problem solving (domain adaptation) and so forth.

Just don't compete on having the largest cluster of GPUs.

Does anyone know why they didn't add the flashattention directly into the MultiheadAttention-modules? Seems to be integrated, awesome!

I find it quite ridiculous to discount RL. Optimal control problems have existed since the beginning of time, and for the situations in which you cannot formulate a set of differential equations, optimizing obtuse functions with value or policy optimization could be a way forward.

It reminds me of the people who discount GANs due to their lack of a likelihood. Sure, but can it be useful regardless? Yes, actually, it can.

If all you do is following trends, and whats in the "spotlight" you probably don't care about your research, but care about the accolades.

Yes, indeed. While the lightbulb may contain properties which may or may not exhibit the Quantum Tunnel Effect (QTE), one must take great care not to confuse this with the Superposition Lightspeed Diffraction (SDL), as it is of paramount importance, that we do not make light of such phenomena - Essentially making all of humanity into sub-particle atoms in the progress towards enlightenment.

This reads like some of those posts criticising OS-frameworks that don't always behave intuitively.

While I don't disagree that there are bugs, Hugging Face is doing more for Open ML than many large tech companies are doing.

HuggingFace, FastAI and similar frameworks are designed to lower the barrier to ML, such that any person with programming skills can harness the power of SoTA ML progress.

I think that's a great mission tbh, even if there are some inevitable bumps on the road.

You're hired!

Sure, could very well be.

Just have to leave all your p-values at the door.

This would be inherently bad, and create great opportunities for China, US, UK and elsewhere.

I'd like to believe they are smarter than this, but then again, I don't.

I strongly disagree. Having an understanding of seq2seq prior Transformers, goes a long way.

Certainly, one hundred per cent agree, if I understand you correctly.

Don't know about human entitlement, but from a simple time/energy-limitation perspective:

• The more time and energy you have in surplus, the more you're able to achieve. Like what is stopping human kind from populating the universe?

I'm sure time and energy is some of the reasons.

History has shown what happens at technological breaking points. Yes, you may not want to earn a living as a horse carriage chauffeur, however, there are opportunity to become a car chauffeur.

I think your premise is wrong, it’s not about replacement, it’s about evolution.

It’s not about ‘threatening’ jobs, but improving certain aspects of it.

GPUs my friend. GPUs. I pray everyday, one day, an H100 may come my way. And yet, everyday, I pray, no H100 is yet here to stay.

Can we do a bot to autodelete these kind of posts?

Well, somehow I expected TD's conclusion to be "Skip the current gen, wait for newer gen"

And yet, here we are.

I continue working on that long backlog of things I'd like to implement:

• Additional models (change of encoder/decoder for future runs)
• Additional loss parameterisations (because you can never get enough)
• Additional dataloaders for the inclusion of more datasets (because without killing penguins, no paper)
• Additional bug-squashing/re-factoring which I've put off using TODOs as comments odd places in my code

Wow, super cool.

Now why do I feel like all my ideas are being gobbled up by OpenAI hahaha.

Bravo, nevertheless.

God I love this post.

More genuine passion in this sub, please!

Keep us updated on your progress, would be great to follow.

Speed, quite literally.

Not computation, but ease of implementing a new idea and making a proof of concept.

Researchers try to maximize time spent on iterating through failure, rather than spending a lot of time to perfect a technique. (Generally speaking)

Yeah, maybe. I think people will find there is no better alternative - For now.