Which team is that? The one at Microsoft that made up the human performance figures in a completely ridiculous way? Basically "We didn't like that pass rates were too high for humans for the hard problems that the model fails on completely so we just divided the accepted number by the entire user base" oh yeah brilliant

The "human" pass rates are also composed of people learning to code trying to see if their solution works. Its a completely idiotic metric, why not go test randos on the street and declare that represents the human coding performance metric while we're at it

Every time I logged on to lambdalabs in the past year all their instances were full. Not that available in my experience

Are they now? Why are you writing empty stuff. Why is this inane stuff so upvoted. Jfc

Not misleading. The fact it performs so differently on easy problems it has seen Vs not , specially when it fails so spectacularly on the latter does raise big doubts about how corrupted and unreliable their benchmarks might be

Idk why people downvoted you, you are right.

The way they defined human performance there is just funny.

Dividing the number of accepted answers by total users.. might as well just make up a number

Yeah just need enough training time and data to be able to train those 32k context layers effectively........................

This a very silly semantic game that i have no interest in engaging with

The original transformers (ie the foundational model architecture all GPTs are based on) were also commercial products (they're used for search, summarization, translation,etc) we got them and the paper all the same.

It's written right after that

>Second, the instruction data is based on OpenAI’s text-davinci-003, whose terms of use prohibit developing models that compete with OpenAI

That's alpaca, a finetuning on llama and you're just pointing to another of openai's shameless behaviours. Alpaca couldn't be commercial because openai thinks it can forbid usage of outputs from their model to train competing models. Meanwhile they also argue that they can take whatever and any and all copyrighted data from the internet with no permission or compensation needed.

They think they can have it both ways, at this point i'm 100% rooting for them to get screwed as hard as possible in court on their contradiction

> If we want everything to be open sourced then chatgpt as it is now probably wouldn't be possible at all

All of the technology concepts behind chatGPT are openly accessible and have been for the past decade, as was the work before, a lot of it came from big tech companies that work for profit, the profit motive is not an excuse. Only unprecedented greed in the space.

Though it comes to no surprise from the company that thinks it can just take any copyrighted data from the internet without any permission while at the same time forbid others from training models from data they get from the company's products. It's just sleaziness at every level.

>But anyway I think basic theoretical breakthroughs like a new architecture for AI will still be shared among academia since those aren't directly related to money

This is exactly what hasn't happened, they refused outright to share any architectural detail, no one was expecting the weights or even code. This is what people are upset about, and rightly so

> Google Deepmind did go that route of secrecy with AlphaGo

AlphaGo had a proper paper released, what are you talking about?

This action by OpenAI to completely refuse to share their procedure for training GPT-4 very much breaks precedent and is horrible for the field as a whole. It shouldn't be glossed over

Closed beta invites isn't shipping no

And bing isn't getting the full fledged version unless Microsoft feels like bleeding millions per day

???

ChatGPT hasn't really "shipped" either. It's out free because they feel hemorrhaging millions per month is an okay cost for the research and PR they're getting out of it. it's not viable in the slightest

open ai will not offer it for free either

I seriously doubt they have been able to do what you just described.

Not to mention a rented double gpu setup, even the one you described would run you into the dozen(s) of dollars per day, not 2.

Well OpenAI also, in that scenario, got a massive on demand compute infrastructure at cost, that's a good deal both ways.

The 10 Billion dollar deal is, reportedly, giving microsoft 75% of OpenAI's profits until a certain threshold, that's more than just any given model

I don't think the billion was for gpt alone, it was to build out an entire AI ecosystem within azure and a big chunk of it was handed out as azure credits anyway

GPT-3 didn't cost a billion to train

It does cost a LOT of money to run, which is why you're unlikely to "see better" for the short and medium term future. Unless you're into paying hundreds to thousands per month for this functionality

Integrating cut down version of GPTs into premium products.. more or less what was obvious to come from this.

This isn't really relevant. Newer, larger LLMs generalize better than smaller ones yet they also regurgitate training data better. it's not exclusive

It's incredibly easy to make giant LLMs regurgitate training data near verbatim. There's very little reason to believe that this won't just start happening more frequently with image models as they grow in scale as well.

Personally i just hope it brings a reality check in the courts to these companies that think they can just monetize generative models trained on copyrighted material without permission