Geez, that's really harsh.

I don't think any single thing you've claimed is factually wrong, and I don't speak for Eleuther nor am I attempting to justify their claims.

But.

As I understand it (mostly from lurking on their discord and reading publicly available materials) this is a group of volunteer academic types trying to replicate something great and awesome, with the only goal of giving it to the world. You could cut them some slack.

I can't speak for you, but as a "for free, weekend project" what they've done certainly makes me feel I need to up my game.

This has nothing to do with the good work, awesome intentions, nor the fact that they have no financial incentives behind this.

Claiming something that is not true is in itself wrong.

I am sorry that I was misinformed about the state of our DALL-E replication when I made that tweet. It was not malicious - I was reporting what I had been told by someone else.

Yes I was wrong. That said, I had hoped that maybe after two an a half months Shawn would stop holding it over my head.

> Claiming something that is not true is in itself wrong.

I 100% agree with this.

I also think that one catches more flies with honey than vinegar, and the criticism in the parent comment, while possibly valid, could be phrased more encouragingly and less combatively. It's easy to criticize, it's hard to create, and it's even harder to release.

> Claiming something that is not true is in itself wrong.

yup, in any project, and especially the one done for the community where the only you get is satisfaction and fame, the success is super tied to communication. Good, honest communication is what builds trust.

"Claiming something that is not true is in itself wrong." Does this apply to butterflies disguising themselves as dead leaves?

Wouldn't it be nice if OpenAI were like .. actually open? :P

Not just that. To even get access to their API, you need to apply. That is the future of AI I'm afraid without projects like this. That is Elites controlling AI and deciding who is "worthy" to use it.

I'm sure they have the best of intentions but "worthiness" is subjective.

Depends on who you mean by "they". If you mean the researchers, then sure, they probably actually believe whatever's written in their ethics statement.

Now, the actual owners? I don't believe it for a second

Considering last decade social consequences due to easy access to APIs and data, I am quite happy that these initiatives are cautious around opening up software which can have huge impact on society.

Unfortunately, the cat is out of the bag. Their methods are documented and the results exciting, so to a bad actor (especially state-sponsored) it's completely justified to spend millions attempting to replicate their results from what is publicly available.

Good perspective but I would like to hear the response from the developers before concluding too much.

This is not meant as a goad to you, but more just as more info for everyone, my understanding is it is an open source community of like minded people type project (as opposed to a bigco) and actively solicits contributions (by which I mean code and data) so anyone seeing room for improvement is welcome to step in from what I can tell.

I did find your comment helpful and informative; just adding another angle here.

It's literally a couple people hanging out in a discord channel and doing this as a way to procrastinate their jobs.

Peak of laziness - build ai to do your job so you have more time building ai.

I think it would help your PR efforts to let people know that more often. People hear "we are <org_name>" and assume you're, you know, an organization. That comes with some amount of expected bug fixes, documentation, verifying results _before_ you release, etc.

I'm not really sure how much you gain by attracting tons of people to the discord if you finally release and everyone has unreasonable expectations due to the way you advertise the group as a whole.

This'll probably sound silly, but we weren't really expecting much of a response. We've been sitting on these models (they were trained for the Pile paper) for months for no particular reason besides being focused on other things. We figured we'd put it out there in case anyone was interested and then Aran's tweet blew up hard.

Fair enough! I can personally attest to the results being quite impressive now. Fantastic work and apologies for the criticism. It must be a strange situation to get dragged in to all this due to a viral tweet.

People are always claiming to release replicated models by replicating the architecture (or main parts of it) but not testing whether it produces the same level of results. It's maddening, especially when the level of results is so directly measurable (just measure what the paper did, not that it's easy, just concrete).

Our README has comparisons with GPT-2 and GPT-3

Is there anything on its few shot learning performance? I took few shot learning as the main point of GPT-3. Sorry if I just overlooked it, but I don’t see anything on few shot learning in the readme.

Disclaimer: I know absolutely nothing about machine learning.

Isn't GPT-3 the architecture? Are they doing something different or why would it not scale?

I would guess that an average FAANG ML engineer could code up and successfully execute a forward/backward pass on a GPT-1 or GPT-2 model with a day of effort or less. (GPT-3 a little harder, but not significantly). But is that model actually going to perform well? Most likely no. Model performance varies significantly due to subtle details in data processing implementations, seemingly insignificant details in code, and even from different numerical methods of calculating the same semantics.

If you don't believe me, consider that many ML researchers track their commits (or exact code versions) extremely carefully, because oftentimes they will make some change (or changes) they think are inconsequential and later find that actually, their model broke. If they made too many changes, whoops, guess you have to binary search over the diff to see what happened since your last "good run".

If the people who spent months (if not years) tuning a model can't tell whether it will work from the code, how could anyone else? Most ML researchers will not bother with most code that doesn't give proof of results (in terms of a model that can actually be evaluated) because it is just so unlikely that it will actually work well. Now, it might "work" in the sense that it converges and does something when you prompt it with examples. But will this GPT-3 reimplementation actually outperform say, the 10x smaller T5 checkpoint that was released by Google, or the other smaller language models others have released? If it doesn't, it's hard to argue that its very useful at all.

I think that's the spirit of why the original commenter said what they did, but I still do applaud the efforts of this team (and hope that their implementation is, in fact, highly performant!)

GPT-3 is the name for the architecture, but there are a few different versions/sizes. The OpenAI version that impressed us all was ~170B parameters, this is far smaller.

To go from 2.7B to 170B parameters will need more than just a few config tweaks. There's a whole bunch of hacks and tricks needed to coax a model to train at that scale, the Eleuther version is almost guaranteed to fail out-of-the-box.

It's worth noting that the GPT-3 paper did train models with more sane sizes (e.g. 1.5B) as a point of comparison. I am surprised/annoyed they never released them though.

It's because OpenAI sells them for profit. The "Ada" model is the same size as the larger of these two EleutherAI models.

Huh, I was wondering what the size of the non-davinci models were; guess that make sense.

It's still telling that a "small" GPT-3 model can risk cannibalizing a larger model.

Ada is 2.7B, Babbage is 6.7B, Curie is 13.0B, and DaVinci is 175B. The new one they announced last month is in the 20-50B range I think, not totally sure though.

It's the model, not the architecture, but you could say the model contains the architecture.

Almost all the challenges with GPT-sized models are engineering and training challenges, not architectural.

How do you train a model too big to fit in a single GPU? It's doable, but not simple. How do you update weights across your cluster? etc etc

What I find interesting about their marketing(?) is that they identified a market niche that they want to position themselves in.

Enterprise customers that have no idea about the technical details will just hear about OpenAI's success in this fancy new model and assume that Eleuther can deliver.

I mean, most use cases for "big data" projects that are tiny in comparison with Alphabet's datasets will just work with GPT2 fine, probably.

And Enterprise customers that hear those claims and see some code, maybe some demo, is enough for them to start the consultancy process.

In my opinion that's a policy problem that OpenAI introduced by not requiring the absolute reproducability of both the code and model, and both training and dataset of their models upon release.

Stakes are pretty high in the AI industry, and OpenAI actively influences it. My dream was in the beginning that they are a source of verification, audits and "proof" that models are legit...yet I have the feeling lately that they just buzz around like everyone else.

To this date I haven't seen anyone replicate any of the DNC results, for example.

Anyways, just my two cents on this one.

To date, EleutherAI as an "organization" (read: basically a Discord server) has not really attempted any kind of marketing. It has no PR dept, just individuals tweeting about the work that Eleuther does.

Also I'm pretty sure there is no consultancy process.