Comment by achow | Hacker News Reader

Comment by achow | Hacker News Reader

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Sam Altman addresses that when he was asked that question in 'How I Built This' Podcast. Essentially: OpenAI did not realize the massive scale they needed for them to be successful. When they realized this, they could not raise any funding as 'non-profit'. They asked govt, who did not want to fund it, and other sources, at the end they did not have any other recourse.

Sam Altaman says: 90% of funding was needed for compute power, but also was needed for things like buying dataset and then to pay employee so that they can compete with likes of Google to retain them. If they would not have done this, then very soon they would have become irreverent.

So to retain the earlier intent (for greater good) they put in bunch of 'safety features' around funding - Ex. 'Profit Cap' - after 100X the profit would be distributed to the world (in some way). Similarly, there were few others he talked about.

The relevant portion starts at 32:39 mark in the following podcast: https://open.spotify.com/episode/3oOX1QHLPw9uvLL5LmBk28?si=s...

It sounds like you agree with me that OpenAI is poorly named.

"We wanted to be open, but we couldn't - we kept the name anyway."

The argument then becomes how to interpret "Open", not that you both agree.

If they keep publishing their research, then OpenAI seems like a fine name to me.

And Russia never invaded Ukraine. You can stretch the meaning of a word all you want, it doesn't change the fact that the people misrepresenting the word are acting in bad faith.

What?

According to Russia, they never invaded Ukraine. Russia is acting in bad faith. The point is you can misconstrue a word all you want, but you're only damaging your own credibility. OpenAI is likewise acting in bad faith.

It's the same story with Google, and Facebook, and Tesla, and on and on and on. When the leaders are unethical you shouldn't expect anything else.

They've largely stopped publishing.

To be fair, they still published papers casually and published a ton of useful OOS recently. Whisper, Tiktoken, Point-E, you named it.

Are these papers reproducible, or do they have source attached to them, if applicable?

Very few DL papers are 100% fully reproducible, in that you can get identical results and generate identical checkpoints, even when source is attached and benchmark datasets are used.

I would say the vast majority published are not even close to be able to be reproduced.

Full size LLMs are not able to be reproduced because the datasets are copyright and not distributable and you need millions of dollars worth of compute to fully train them.

If a company is publishing enough detail about SoTA architectures for researchers to ballpark reproduce, that is a win in my book.

I'm not actually expecting them to be 100% reproducible. I'm more than familiar with the work required to do that.

What I'm expecting is not holding back the secret sauce, as seen in some (or many) publications.

My gripe is not limited to DL/ML/AI scope either. When I try to compare my results with the papers I cite, I generally can't find the formulae or the detailed method to reproduce the numerical method the paper claims, and this leaves us at the dark.

All I can do is saying "Paper23 cites these results, and we surpass them at this, we are even at that, and they are better at the other thing", which I'm not comfortable doing. Not because they are not telling the truth, but I want to test my method against other methods on an even ground.

> My gripe is not limited to DL/ML/AI scope either. When I try to compare my results with the papers I cite, I generally can't find the formulae or the detailed method to reproduce the numerical method the paper claims, and this leaves us at the dark.

I agree. So many don't even both including hyperparams, even when they publish the code. The Github Issues for their code are littered with questions asking about hyperparams.

> All I can do is saying "Paper23 cites these results, and we surpass them at this, we are even at that, and they are better at the other thing", which I'm not comfortable doing.

If you are achieving better results, on the same dataset, and you are not cheating in anyway, and others can reproduce your results, then I don't know what is wrong with saying you got a better result.

Issues somewhat arise when you are using better hardware with more parameters or larger batch sizes than the original authors could have attempted. I think this accounts for the results in many papers.

It’s not even open source lol

I get the need to make money but at least contribute OSS to the community they’re building a product on….

Semantic debates are largely irrelevant. It is what it is.

It works, I was watching some "normie" youtube creator using Chat-GPT in a face-off with him over some domain specific question and answers. He was new to the LLM thing you could tell, but said how it was an open source right in the intro. Probably a common mistake for a company with "Open" in the name

as much shit as we can give them for something as trivial as their name, their contributions to AI with GPT-1 (which beat all benchmarks & which led to BERT & was basically the "real" BERT itself), GPT-2, which was really impressive back then, GPT-3 which was an incredibly improvement that people had not see coming, PPO in reinforcement learning, reinforcement learning from human feedback in NLP, Instruct GPT (the real ChatGPT before ChatGPT), and now chatGPT

I created a snip with the relevant portion of the conversation: https://share.snipd.com/post/0bb82eea-e7a0-4d4e-b234-8471dc5...

Next level gaslighting.

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