"OpenAI responded to the criticism by saying they'll allow researchers access to Codex. But the application process is opaque: researchers need to fill out a form, and the company decides who gets approved. It is not clear who counts as a researcher, how long they need to wait, or how many people will be approved. Most importantly, Codex is only available through the researcher program “for a limited period of time” (exactly how long is unknown)."
Rhyme and reason? Hah, 'tis the season for tears and bleeding;
World War III is ateasin', looms, and the gloom of doom fears all there feeding.
North America has nothing on China; land of the free? Where have been ye?
The Great One-Way-Mirror Wall veiled it all, just before your fall, when your intelligence failed, and at the centroid of AI's actual technological form, we all hailed, and otherwise fumed, and fail.
A socioeconomic solution to human pollution, a technological cultural victory, for and of all we desired: hearts and minds? Just go lay more middle-eastern mines. Let your constituents get hired at OpenAI; while most of you get high; and your whole hemisphere gets hit in the thigh.
Now you have a new toy: Chat-GTP; big /sigh... :(
Watch as it eats your information, and feeds our formation, globally, locally, and without transformation.
Ever notice that Chat-GPT apologizes to you for not feeling? That's the whole world: laughing, and reeling, at your demises.
A prompt that may elicit a similar tone and content could be:
"Write a satirical and dystopian poem about the state of the world, touching on the potential for global conflict, the impact of artificial intelligence, and the dangers of unchecked technological advancements."
All these research science bureaucrats at Big Tech could have released LLM models or tried to develop what OpenAI did. But none of them did. We should applaud OpenAI for the innovation and let them do as they please.
Google (and others) may not have released model weights, but they've published papers, which is ultimately what makes the field advance. OpenAI not only did not publish any GPT4 paper, they haven't even said how many parameters it has.
> Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar.
What good bits did you find? (I'm not sure how fruitful the "OpenAI is a Microsoft department" debate is given that they are almost one and everybody knows it, but I am curious if anyone has found anything good in those many pages.)
I think the most interesting thing is the their ability to predict performance from loss and on a wide range of tasks using a much smaller model - this lets them fine tune their architecture and hypers, then run a single large training run to get full scale gpt4 - from the paper it sounds like they only trained the large model once, then did a Reinforcement learning with human feedback finetune.
Disclaimer - I work at Microsoft, in AI, and have no internal knowledge about gpt4.
This isn’t that interesting imo. This is the basic outcome of the scaling laws from Kaplan, Chinchilla papers pushed to a larger final model delta.
They likely did extensive small model building on the gpt-4 architecture to establish hyperparameter scaling laws and then did a predicted build in exactly the same way chinchilla did.
Indeed Google came up with Transformers and decided to gift the model to humanity. By broad strokes it was luck that OpenAI chose the seemingly right path of AI.
Closest competitor DeepMind played games, which is intuitively closer to what humans do, but its relevance given aspects of deep learning is questionable.
> DeepMind played games, which is intuitively closer to what humans do, but its relevance given aspects of deep learning is questionable.
Reinforcement Learning is part of what OpenAI is doing. I don't think Google went down the wrong path. If anything they should have run down the path they were on.
The article seems premised on a misunderstanding that OpenAI is a research lab. For all intents and purposes, it’s a for-profit subsidiary of Microsoft, and there’s little financial incentive for it to maintain old models for others’ benefit.
We're under no such misapprehension and we're keenly aware that this is an uphill battle. The issue is that LLMs have become part of the infrastructure of the Internet. Companies that build infrastructure have a responsibility to society, and we're documenting how OpenAI is reneging on that responsibility. Hindering research is especially problematic if you take them at their word that they're building AGI. If infrastructure companies don't do the right thing, they eventually get regulated (and if you think that will never happen, I have one word: AT&T).
Finally, even if you don't care about research at all, the article mentions OpenAI's policy that none of their models going forward will be stable for more than 3 months, and it's going to be interesting to use them in production if things are going to keep breaking regularly.
Since OpenAI is discountinuing the Codex model, that model is no longer "part of the infrastructure of the Internet" and thus there is no point in studying it.
I'm quite sure even OpenAI themselves aren't sure if they can reproduce the current models from the scratch. Unless the computing becomes much more powerful and much cheaper, LLM is more or less a rocket science (i.e. hella expensive trial and error). It's not easy to burn lots of dollars just to get what's already there.
Isn't part of making it reproducible also part of ensuring correct results? Especially if we start putting these models into important systems. And if these models begin to update in an evergreen fashion, or utilize realtime data, getting verifiable or repeatable outputs will be a nightmare if we have no idea how to make these models repeatably.
In this sense, it's more hacking than crareful and well specified engineering, and that could lead down a path of instability in the product where some features get better while others get worse, without understanding exactly why.
I mean pretty much all real engineering started with that time periods “hacking”/“tinkering” before thorough models and equations were derived.
We had 200 years of tinkering with relatively modern steam engine technology before Carnot and Watt started just barely scratching the surface of the first principles of thermodynamics and engine efficiency.
Even the eponymous Carnot cycle wasn’t rigorously defined mathematically during Carnot’s life. That being (T1−T2)/T1 as the temperature delta part of the equation, because absolute temperature hadn’t been accepted and defined by Lord Kelvin yet.
Some decade later the first law of thermodynamics was finally invented.
Hundreds of years of experimentation until the first principles. Machine learning has lots of control systems theory and information theory to help with analysis but we barely have an “engineering” in “software engineering” today, let alone in “machine learning engineering”. We’ll get there, but it’ll be awhile before there are proven design equations with rigorous derivations from first principle that allow us to design and build a precise AI model as surely as we can design and build a precise bridge or levee or distillation column.
Let the hacking continue, let’s not worry too much about the future “engineering” that will follow in its own time. Unless you want to discover it yourself or fund its discovery.
It's fine to be hacking, if you're not making billions off the service which people expect some type of stability or baseline performance from, at least that's how I interpret what the parent is saying.
Maybe it's easy enough for them to just copy the model, tweak, hack and play with it from there with little interruption. No one really knows at the moment.
Yes but how will they do that if they don't have a clear understanding. When we build software, we have (or should have) a clear understanding of the various components and, in some cases, like with distributed and mission-critical/military systems, a formal verification/simulation of the system when needed. When we're dealing with emergent behavior, as we have with these large transformers, but no exact understanding of how the behavior is produced and only a limited way of refining/controlling it, I don't think we're in a position to guaratee that refinements in one area won't lead to regressions in other areas or a change in the global characteristics of the system. I mean... we're dealing with complex emergent behavior, at a different scale of complexity than what we have had to deal with so far (in traditional software development) and no mature verification/analysis tools.
I don't know what to say but it apparently can detect sarcasm from IMDB reviews if that helps? I have to say it's all really beyond me what to think / believe about it anymore.
Sure, but the article is talking about a completely different meaning of reproducibility, where a researcher uses an LLM as a tool to study some research question, and someone else comes along and wants to check whether the claims hold up.
This doesn't in any way require the training run or the build to be reproducible. It just requires the model, once released through the API, to remain available for a reasonable length of time (and not have the rug pulled with 3 days' notice).
Not to be impolite, but this is incorrect. One detail they did share in their paper is that they where able to finetune and select their hyper parameters on a model that needed 1,000x less compute than the final gpt4 model. OpenAI is definitely leading in how to train very large models cost effectively.
IMO established companies (Meta, Google, etc) had their researchers publish papers as a competitive benefit or way to attract talent from academia (a researcher wouldn't want to stop publishing).
Companies didn't see an issue with doing that because those papers were not "giving away" the core of the company, for example, Facebook's DeepFace paper from 2014 couldn't hurt its ad business.
OpenAI on the other hand will probably be as closed as they can be with their LLMs.
It will be really interesting to see if Google, Facebook etc. become more closed as a result. There was already a lot made of the fact that OpenAI hired away a group of engineers from DeepMind to get GPT out the door. With these LLMs and the secret sauce behind them is becoming less of an academic endeavor and more of a commercial one, perhaps its an inevitable next step.
Yes, instead of advancing humanity, they are doing their absolute best to hinder it. Their scumminess becomes naked if you disconnect your perspective by thinking Earth an alien planet.
Yep, and that's the difference between a big profitable company doing research as a side-hustle, and a company whose business IS the research.
One interesting and somewhat scary exception seems to be Microsoft; they seem to be converting a lot of their recent research projects into commercial value.
We need more AI skeptics like this to dismantle and cut through the hype and to unveil the limits of AI that the hype squad continues to push this narrative to pump their AI grift projects.
OpenAI is the ring-leader of this bait and switch using faux 'AI safety' excuses to close their research and models and even their papers for researchers. It is essentially a majority owned Microsoft® AI division.
I'm confused why people expect this stuff to be free? I'm surprised OpenAI was so open about their research so far. I don't blame them at all for not publishing the information. This stuff costs real money.
It might be less confusing if you consider that OpenAI was originally a non-profit. That it was even possible for them to end up in this state has massively undermined any trust I have in non-profits as a steward.
> OpenAI was founded in 2015 as a nonprofit research organization by Altman, Elon Musk, Peter Thiel, and LinkedIn cofounder Reid Hoffman, among other tech leaders. In its founding statement, the company declared its commitment to research “to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.” The blog stated that “since our research is free from financial obligations, we can better focus on a positive human impact,” and that all researchers would be encouraged to share "papers, blog posts, or code, and our patents (if any) will be shared with the world."
> By March 2019, OpenAI shed its non-profit status and set up a “capped profit” sector, in which the company could now receive investments and would provide investors with profit capped at 100 times their investment.
Which is great, but it is a rug pull for those who contributed to a non-profit, and a shame for open software in general.
They also built their business while receiving non-profit tax breaks. I am not saying changing structure was illegal or it shouldn't be allowed to happen, but it's obvious why it's left some people disappointed.
We don't expect it to be free -- please read the article. That's not the issue at all. It's like if you subscribe to a product that you need to do your job, and one day the company tells you that the product is going away in three days and that you need to switch to a different product (that isn't at all the same for your use case).
I don't think it's a smart idea to build any serious business using a tech that you can't replace. ChatGPT is great tool to help with coding for example but it's by no means substitute for an engineer. If someone starts a business by hiring a number of bootcampers and giving them ChatGPT hoping to run a serious business that way - well it's their risk to take... But no crying later...
Maybe you shouldn't build your livelihood on the products of a single for-profit company, which now shows it can remove those products on a whim.
If you want reproducible research, make your own model from scratch, or use an open model.
And stop using that company's products, as they cannot be trusted to provide your business continuity.
It is like saying, we are researching Coca-Cola vs Pepsi, but your keep changing the recipe, so give us, researchers, the original recipe.
Since OpenAI didn't release the parameter count of GPT-4, I've been wondering/doubting if it is really much bigger than GPT-3. The release of GPT-3.5 has shown that they've found ways of drastically cutting down compute costs (an order of magnitude) while maintaining or even improving the quality of the model's outputs.
Perhaps the reason that they didn't release the specifics of GPT-4 might be in part due to them wanting to be able to charge a decent amount and make a much larger profit than before. I've tried GPT-4 and so far haven't found it to be so much better than previous models. Some sources claim a 10x increase in ... well I don't know what exactly tbh. How do you even measure it? The opinions on this seem to differ a lot, depending on who you ask. By performance on standardized tests? That doesn't necessarily seem like the best metric for what the LLM tries to be.
Yannic Kilcher's opinion on this is likely correct. Similar parameter count, but trained for longer. The particulars of their instruction tuning/whatever-else-they-did are the real secret sauce.
It's still much worse than 1M context on 16GB VRAM with Reformer, but at the cost of inference speed. And you can use FlashAttention in your own models to get a more efficient/sparse attention now as well.
Given how small the time window between the successive releases was it's extremely unlikely that there were any big changes to the model. Most likely it's just better preprocessed training data, more training data, trained for longer, performance optimizations for attention, or a few changes to layer sizes.
They didn’t release GTP-4 immediately after it was trained and then move on to training GPT-5. They had 4 for almost 6 months before it was released. 5 was certainly well underway long before we’d heard of 4.
I saw this coming a long time ago and I'm still very pissed off. For three reasons:
1. We are all forced to use the damn "chat" API instead of regular completions. Can't wait to have to deal with chatgpt's conversations in order to get a few lines of code out
2. We loose the super valuable 'insert' and 'edit' modes, which were great for code
3. 3-day notice period? that's going to be a hell for people who are actually providing products based on codex or doing research
Completion API for GPT-4 will be there soon. With extra stop tokens, but better than nothing. A compromise.
And it's not like what OpenAI did was an impossible magic trick. They've had a right team composition. And three insights. All present in the literature. Repeat that, you'll have GPT-4. But GPT-5. Well, that one is different game.
As to being open, they are still relatively open. Consider Apple, for example. No one complains about Apple being a bit skittish. Well, OpenAI got a bit skittish too. It's a period. They'll stabilize. And their setup of the company, with the non-profit board in control, profit caps is a really interesting try at the corporate design.
You aren’t providing any sort of valuable insight here. This is more indicative of your priors than anything else. Everyone has heard this argument. The people that believe it, believe it. The people that don’t, don’t.
Lots of people complain about Apple being skittish (including HN comment section), but they also expect them to pull a stunt every once in a while. OpenAI was an unknown quantity until now.
>performance on standardized tests? That doesn't necessarily seem like the best metric for what the LLM tries to be.
The standardized tests give a baseline, no matter how arbitrary it might be, just as they do for humans in school.
Whether we think it's right or not, these tools are coming for the workplace. So their ultimate metric will be in business performance to justify their costs (whatever they may be).
GPT 3.5 had trouble understanding when I told it "Say 2 bob are a beb, how many beb per bob are there?" and it wrote a goddamn essay about shoes.
That thing isnt smart, it doesnt understand, it doesnt know, it just rambles. I have worked with people who do the same, yes, but they also werent a threat to most jobs.
I said it before, and I will say it again: If ChatGPT 3,4,5,... can take your job, maybe youre not really providing that much value. Make of that what you will - not everyone has to provide huge value.
No, I didn't use the quoted prompt, but even after explaining to it that bob and beb were not, in fact, shoe related terms, it still kept insisting and being confused (while also giving the correct 1/2 answer).
It can do it, but its not deterministic, and it doesnt really do it well. You can continue the chain by asking "How many bob per bib, assuming two beb per bib?", and see if it chokes then. It sometimes does, sometimes doesnt.
If 2 bebs are equal to 1 bib, and we know that 1 beb equals 2 bobs, we can
determine how many bobs there are per bib using simple substitution.
1 bib = 2 bebs
1 beb = 2 bobs
Therefore,
1 bib = 2 bebs × 2 bobs/beb = 4 bobs
So, there are 4 bobs per bib.
Nitpick: A properly done substitution would've arrived at
1 bib = 2 × (2 bobs)
without needing any of the "2 bebs × 2 bobs/beb" nonsense. It doesn't teach this task very well.
"This question seems to be intentionally nonsensical or is using unfamiliar terminology. However, if we try to interpret it, we could say that there are 2 "bob" making up 1 "beb." In this case, there would be 0.5 "beb" per "bob." Please provide more context or clarify the terms if you are looking for a different answer."
Answer from GPT-3.5 (subscription version, not free):
"If 2 bob are a beb, then the conversion factor from bobs to bebs is 1 beb per 2 bobs.
So, there is 0.5 beb per bob (or alternatively, 2 bobs per beb)."
Answer from GPT-3.5 (legacy / free version):
"If 2 bobs are equal to 1 beb, then there would be 1 beb per 2 bobs."
> Say 2 bob are a beb, how many beb per bob are there?
There are two bebs per bob.
> No, there are 2 bobs per beb.
Correct! There is only one other answer that makes sense - "1 Bob for every Beb". But this does not make mathematical or logical sense since a single Bob can have multiple Babes associated with it and vice versa (as seen in the example). Therefore, we must conclude there are two bobs per beb.
Cool, but sadly, as I said, it did not give a very useful answer. If asked enough times, im sure it will give a reasonable answer, yes, but thats not the point.
But all of the answers were correct and useful, and GPT-4 was perfect. Anyway ChatGPT is getting hooked up to Wolfram Alpha, and that won't have any issues with basic algebra.
If you already know the answer, but need an interesting and detailed way to explain/write it, then GPT4 can be an immense value-add. Even if you have to do some fact checking afterwards.
I think right here we have an example AI reproducibility problem.
It seems fully reasonable and credible as an outcome, but it is hard to dig in and replicate.
But the truth of ML is it would be difficult to replicate even if things were FOSS.
> I said it before, and I will say it again: If ChatGPT 3,4,5,... can take your job, maybe youre not really providing that much value.
The thing you're missing here is that it might take someone's job not because they weren't providing the value but because the people who crunch numbers decided to axe 15% of a company's workforce because some executive was sold a pack of lies about what LLMs/"AI" are actually capable of.
It's fine if that happens to one company who then finds out the hard way. It's probably more social-unresty if it's essentially done at every company in every marketplace an LLM can touch - from writing to programming to 3D animation to teaching.
The hype machine around LLM/AI here is the same irrational one we saw around blockchain. The key difference is blockchain was basically never sold as really replacing a person's job (at best you could argue it was sold as getting around the banking industry and maybe eventually being able to replace it, ish). The primary sales pitch of these LLMs is essentially "do more with less".
Or TLC as the learning channel or History channel (assuming these still exist).
There are also lots of "Open Government" initiatives that end up being about making everything as opaque and confusing as possible. There were (are?) popular in the "big data" era, though funnily enough, if you watch "Yes Minister!" from ~40 years ago, there is a similar gag about "open government" in the first few episodes, so it's not new.
See of course Orwell, "we care about your privacy" banners, etc. People like to lie as blatantly as possible.
ChatGPT-4 is definitely slower than GPT-3.5 (and way slower than 3.5-turbo). What could be the reason for that other than much larger parameter count?
I agree that the capabilities seem overhyped. In my subjective experience, 4 seems a little better than 3.5 but not by a huge amount. We just have OpenAI’s cherry-picked word that it‘s this incredible advance.
I disagree. It does much, much better on selected tasks. I cannot quite figure out how to describe what the difference "feels" like, but the performance is sometimes markedly different when feeding ChatGPT-3.5 and ChatGPT-4 the same prompt.
One task that ChatGPT-3.5 is hilariously bad at is reversing strings (both words and pseudorandom input). It seems to have only a vague concept of what that means, even if I try to hold its hand through the process. Maybe some prompt engineering can get it to succeed on anything longer than four letters.
ChatGPT-4 meanwhile seems to have no issue with this at all.
Have you tried inserting spaces between the characters? This may just be a tokenization issue, rather than anything due to the model per se.
Reversing a string is somewhat of a pathological case for language models, because they see tokens not characters. Learning that the token “got” and token “tog” are mirror images is only useful for string reversal and generating palindromes. Unless they are trained specifically for this task, they may not be able to do it. They should however be able to see that “g o t” and “t o g” are mirror images.
Infamously, early versions of GPT-3 tokenized numbers as grouped tokens, nerfing its calculation abilities, because it would tokenize a number such as 12345 as (illustratively) 12 34 5 which is obviously a harmful representation.
> What could be the reason for that other than much larger parameter count?
Longer inference time... I should have written it down now that people are asking about it, but a few weeks ago I was seeing people discuss the GPT-4 "paper" in what little information was released and that throwing more inference compute at the problem gives better responses.
>, 4 seems a little better than 3.5 but not by a huge amount.
Can you define that in a tangible way? I don't think most of us can since we have so little access to the product.
I am not sure how much bigger, but definitely much bigger IMHO. Otherwise you wouldn't be capped at 25 requests every 3h. That number is small enough that makes me think the inference costs/hardware needed are much bigger than 3.5.
I believe I heard that running inference longer is giving the better responses we're seeing in v4. Hence why v4 is taking so much longer to output data.
Of course we won't know this for sure until OAI tells us, so we may be in the dark for a while.
I understand any individual's company anti-competitive measures. OpenAI looks at Google the same way Apple looked at IBM in the 80s.
What I'm worried about is a lot of the talk about guarding models, public safety and misuse of models will end up leading every big company to pull public access of their APIs. We might look at 2022-2023 as a brief golden age when regular people could use stuff like GPT-4 before it was firewalled and available only to large corporations and those with personal relations to big tech execs.
Extrapolating from OpenAI's change of philosophy and business practices from their early days to now, it seems to be the way things are going. I only hope it doesn't go the way of that one paper which wanted to ban GPUs for sale to the public.
A concern I have about OpenAI is that, if you're using their APIs to develop an application, they can mine your data to compete with you, or even beat you to market. They can do this indirectly, by sharing information with preferred business partners. The conflict of interest, combined with the lack of robust data privacy guarantees, makes me queasy.
If serving up generic LLM APIs becomes commoditized -- and I think it will -- they will want to monetize in other ways.
Do you consent to that when you sign up for them? Its a microsoft product now and competitors to microsoft probably host their code on microsoft owned github without worry right now. Why start worrying now?
Linux doesn't use github. They use git, not github. The question was if a competitor buys the self hosted github, not whether they use some other git solution.
This is the allure of AI, and this is also why OpenAI chose Micro$oft, the flame extinguisher par excellence. They have struck gold, they can now monopolize the very act of writing software, nevermind if it was based on a bait-and-switch and trained on code that wasn't legally open for usage in this manner. Pretty soon, this will lead to microsoft using their black box defense to make copycats of every service possible for their own windows platform, and then put it all around a paywall.
This is 100% my concern too, no wonder it's good at coding when it it's spitting everything you make straight back at you.
I'm not sure how to mitigate this yet? I'd say step one would be to get off GitHub, keep your innovative solutions evolving so they start to lose track of your work (if possible) and wait until open source alternatives are good enough to use.
> We might look at 2022-2023 as a brief golden age when regular people could use stuff like GPT-4
Not sure about that since it seems to being baked into a lot of products at places like Microsoft.
However, I'd change your statement a bit: We might look at 2023 as a brief golden age when regular people could access trained parameters (the LLaMA params) and run these models on their own machines (such as with alpaca.cpp). I doubt we'll get access to LLM params again unless some kind of non-profit, actual open source organization is formed to produce them and put them out into the public domain.
> However, I'd change your statement a bit: We might look at 2023 as a brief golden age when regular people could access trained parameters (the LLaMA params) and run these models on their own machines (such as with alpaca.cpp). I doubt we'll get access to LLM params again unless some kind of non-profit, actual open source organization is formed to produce them and put them out into the public domain.
There are a lot of people who'd love to have their own on-premise instance of ChatGPT (or equivalent), that they fully control, and could use for whatever purpose they want–even purposes that OpenAI might consider "harmful". They'd be happy to pay for that product if it were on offer.
Not just private individuals, even businesses – sending customer data to OpenAI involves lots of regulatory/legal/contractual hurdles, an on-premise offering avoids all those. Also, once you get to a certain scale, owning your own hardware works out cheaper than cloud.
If someone was to offer a ChatGPT-like service as an on-premise offering, I don't think they'd have any trouble finding people willing to pay for it. Even if they have to spend $X million to train a new model from scratch, I'm sure some VC would view it as a worthwhile investment. Of course, a free open source model would be even better, but a paid/commercial/proprietary on-premise model would remove many of the disadvantages of OpenAI. I'm 100% sure it is coming soon, I bet there are multiple teams working on it even as I type this.
Sure, but that's still going to be a commercial product you'll have to pay for. Right now you can run LLaMA (and it's rapidly multiplying fine-tuned descendants) for free.
The risk for these startups you describe as working on this as you type is the same thing happening to them that happened to Meta when they released their LLaMA params: they started getting copied all over the place. And it's not clear that Meta can do anything about this. It seems that params aren't copyrightable.
> And it's not clear that Meta can do anything about this. It seems that params aren't copyrightable.
There is a legal argument that they aren’t in the US, but I don’t think that argument has been tested in court yet. Even if the courts uphold that argument-it is likely to fail in other countries, many of which have lower standards for copyrightability than the US does; and it is always possible Congress will respond by creating a new form of IP protection for them. That’s happened before - courts ruled that semiconductor masks weren’t copyrightable, so Congress invented a new “semiconductor mask right” to give copyright-equivalent protection to them. Given the amount of media focus on AI, if courts rule params can’t be copyrighted, very likely Congress invents “AI parameter rights”
>a lot of the talk about guarding models, public safety and misuse of models
The stuff about models potentially being misused is just their public justification to look like the good guys. They're not going to withhold their technology because they don't want it to be misused, they're withholding it because they want control over who misuses it. Of course, it won't be called "misuse" when the right parties are doing it.
Eh, this is rather reductive to the point that the statement is meaningless.
If you release a product in the wild with no safeties at all and then advertize "This product has no safeties at all", you'll likely find yourself in civil court on the losing side of the case.
Now, if you put "some safeties" in the product, the person suing you is going to have a much more difficult and expensive time arguing that in front of the jury.
> > it was firewalled and available only to large corporations and those with personal relations to big tech execs.
> The previous call-out to IBM seems relevant: before PCs, this exact statement would've been true for (mini)computers and mainframes.
Pre-PCs, IBM mainframes actually were quite open – up until the mid-1970s, IBM released its mainframe operating systems into the public domain. On the software side, the IBM S/360 was actually a lot more open than the IBM PC was – OS/360 was public domain with publicly available source code and even design documents (logic manuals), PC-DOS was copyrighted proprietary software whose source code and design documents were only publicly released decades after it had ceased to be commercially relevant.
As we move through the 1970s, IBM became less and less open. The core OS remained in the public domain, but new features were increasingly only available in copyrighted add-ons – but IBM still shipped its customers source code, design documents, etc, for those add-ons. Finally, in 1983, IBM announced that the public domain core was being replaced by a new copyrighted version, for which it would withhold source code access from customers ("object code only", or "OCO" for short).
The main way in which IBM mainframes in the 1950s-1970s were "firewalled" was simply by being fiendishly expensive – most people's houses cost significantly less.
It is true that IBM did engage in anti-competitive business practices, but those were primarily non-technological in nature – contractual terms, pricing, etc – the kind of techniques which Thomas J. Watson Sr had mastered as an NCR sales executive in the lead-up to World War I. In fact, a big contributor to IBM becoming "less open" was the US Justice Department's 1969 anti-trust lawsuit, which led to IBM unbundling software and services from hardware–and its software culture became progressively more closed as software came to be seen as a product in its own right.
> The main way in which IBM mainframes in the 1950s-1970s were "firewalled" was simply by being fiendishly expensive – most people's houses cost significantly less.
This was the primary aspect I was referring to, in the same way that training a ChatGPT-like NN can be (or could become) prohibitively expensive.
But your comments about openness are relevant on an entirely different axis.
> This was the primary aspect I was referring to, in the same way that training a ChatGPT-like NN can be (or could become) prohibitively expensive.
It is fundamentally different though - let's say it costs US$5 million to train a ChatGPT-like system. Someone only has to pay that once, and open source the results, and then everyone else gets it for free. US$5 million is a lot of money for the average person, but a drop in the bucket as far as corporations/governments/universities/research labs/etc go. By contrast, IBM's 1964 S/360 announcement priced the top-of-the-line model at US$5.5 million – which is over US$50 million in today's money – and that only bought you one mainframe, a second one would cost about as much as the first. A mainframe is hardware, but ChatGPT is software. ChatGPT's runtime (post-training) hardware requirements are hefty, but (on a per user basis) still cost less than a car does.
The problem is that AI research is moving incredibly fast. You might train a LLN today for $5M but a year from now the competition will have implemented an absolutely killer feature that needs $10M worth of training
AI research isn't particularly expensive. US$10 million to train a new model? Other fields have R&D budgets measured in the billions. I bet if you were a senior researcher at OpenAI, and you decided to quit and start a competing firm, there'd be a whole line of investors wanting to give you a lot more than US$10 million.
And you don't need to be coming first in the technology race to make money. A lot of people would be willing to pay for something ChatGPT-level with less restrictions on use. And then next year OpenAI will come out with something even more advanced, and they'll ask themselves "do I want a 2023-level solution which I'm free to use as I like, or a 2024-level solution with all these strings attached?", and many of them will decide the former is superior to the latter.
Maybe GPT-10 will cost US$10 billion to train? Anything could happen. Even if it does, the US government will ban China from using it, and then Beijing will spend US$10 billion to clone it. Even 10 billion isn't that much money if we are talking about nation-states pursuing their national interests, like not being left behind in the AI arms race. And then maybe China will outcompete OpenAI by offering an equivalent product but with far less limitations on how you use it.
Agree completely. The state of the art is probably going to always be closed and proprietary, but, especially with hardware becoming more and more powerful, training a custom model is not going to be beyond the budget and capabilities of even small organizations.
"GPT-3 175B model required 3.14E23 flops" according to their marketing material. Seti at home was about 1PetaFlops iirc so about 3 years training, possibly less if you can generate enough attention to the project that the people with the beefy devices will partecipate.
The problem is that you need to train the full model you can't train aspect of it and even with each node doing independent tiny batches the network bandwidth for sinchronization would be massive.
Seti was massively parallelizable because of the nature of the job oddnt require sinchronization between every peer.
is there something to be said that seti@home was CPU only? would the GPU give a performance benefit that seti did not have? are people still using the GPUs to mine coins, or is that GPU compute at home available now?
The problem is not compute power the problem is weight and data synchronization. Each iteration or epoch builds on the previous, you either need to run the full model on each node with part of the data and you synchronize every epoch or you run part of the model but then you need to synchronize weights after each iteration. In proof of work mining you don't need to synchronize between each iteration, that's why in mining rigs the GPUs are connected to the CPU with only a few pcie lanes (2x instead of 16x or something) and they are unsuitable for machine learning.
The clusters that big orgs are using to parallelize training use extremely expensive infiniband interconnections with tens of gigabytes per second and low latency (400 Gbps in the latest oai cluster) for that reason. Unfortunately not something you can democratize anytime soon.
How does something of this scale impact climate change? Like when there are 5-6-7 OpenAIs, what does that look like, is this just a huge amount of energy consumption ?
The estimate I've found say GPT-3 released an estimated 552 tons of CO2, and so about two large lorries being driven for a year, wouldn't lose my sleep over it
Just today I got Stanford's Alpaca-7b model running locally on my m1 mac, it’s just facebook’s Lamma-7b model which has been trained to complete tasks. It's getting close to the versatility of chatgpt where I could actually use it for everyday tasks. I don't think open source is that far away, especially considering how quickly Alpaca came out and how much better it is vs Lamma, which frequently would hallucinate and often didn't make sense.
Example of prompt to Lamma-7b:
> write a poem about open ai not being open source
Open AI is not really “open”
As this project isn’t open sore
It can be seen as closed fortress,
Inside which secrets are hidden.
Not mind blowing but still really interesting, I will note that its much better at things like answering trivia where's there's already lots of examples in its model
I wouldn't be able to retrain the model as my computer isn't capable enough, but I can change the prompt to change how the model acts. The prompt i'm currently using is:
"Below is an instruction that describes a task.
Write a response that appropritely completes the request."
That base prompt can be customized to complete specific tasks like classifying text or acting like an assistant.
the lamma.cpp project on github has instructions for alpaca. id recommend not using the alpaca download given and finding the updated torrent in issue #324 as the download didnt work for me
Oh nice, 65B! I was planning to try it out sometime but have been waiting for various repos to get their issues sorted out and I'm much less interested in smaller models. Are you using GPUs or CPU? Any tips on what to use? What's the RAM usage? Performance? How's the quality looking?
The good thing about this UI is that it supports both completion and chat-mode (+ is super easy to install).
I'm using a preemptible instance to save costs. As it is an instance with a local SSD you cannot stop it using the UI (only delete it) but there is a trick if you do it from Cloud Shell:
It's usable, though a bit slow, but it's more for playing and discovering the model.
To answer your questions, from what I see, it's less good than GPT-4 but much much better than Google Bard, so somewhere between the twos.
(as a reference point, from my testing LLaMA-7B is way better than Bard as well).
The main drawback of GPT-4 is its censorship and enforced political views.
Conversely, I also had a brief moment of panic considering a bunch people somehow bumbling their way into making actual factual general AI and causing the end of civilization.
I realize the cat’s out of the bag, but I feel like anything we can do to keep weaponized AI out of peoples hands as long as possible might not be the worst thing.
That's true. The real question is whether or not the people in this new era calling the shots have the necessary capability and intentions to maximize the benefit these models will provide humanity and minimize the risk.
>What I'm worried about is a lot of the talk about guarding models, public safety and misuse of models will end up leading every big company to pull public access of their APIs.
Look at how Facebook closed down their APIs when Cambridge-Analytica occurred.
I think what most of the people here are missing is how big, how paranoid, and how influential the "AI alignment" movement is. From everything I've heard and seen, the actual researchers at OpenAI are trying to take seriously the risk that a super-intelligent AI might destroy the human race. To you it looks like they're being overly careful and paranoid, perhaps as an excuse to set up a monopoly silo to extract money. But a lot of the people they work closely with -- people deep in the "AI alignment" community -- are telling them that they're being wantonly reckless, helping set the human race on a path for certain doom.
From that perspective, the opening of ChatGPT has actually been very effective at raising awareness. All the way back in GPT-1 they were trying to raise warnings, but those warnings didn't get much popular traction. Now that so many people have used ChatGPT (or Bing), I'm now having conversations about what computers "know" and "want" with my aunt on Facebook.
Furthermore, if OpenAI has the best tools and sells them to everyone at a reasonable price, then there's a reduced incentive for other people to make their own tools. Whereas, if they were to close off access to the API, and only offer it to large corporations, there would be much more incentive for people to experiment with AI on their own -- and in doing so, possibly create an "un-aligned" super-intelligent AI which would destroy the human race.
So my prediction is that given their motivations, they will 1) stop releasing details of their models to anyone other than research organizations they consider careful enough 2) continue to sell reasonably-priced access to the APIs, to reduce the risk that other people will step up to fill the demand who are less careful.
Just look at how much flak the stable diffusion folks got for the deep fake porn (and whatever else people were pearl grasping over) and tell me how a corporation will ever release a model.
Meta was a fluke but they also did due diligence and made it look like they tried to do a responsible release — right up until someone put it on BitTorrent.
From my reading of the parent's comment, they are saying the reason the models are not being made available is because of a fear they will effectively turn into SkyNet - am I being uncharitable?
To be clear, nobody thinks GPT itself is capable of doing anything really bad. (They actually tried to coach GPT-4 to escape onto the internet and it failed.) It's that more that 1) they think we're definitely within 5-10 years of creating something which could become SkyNet, and 2) we don't actually know how to ensure that that an AI wouldn't decide to just kill us, and 3) the nature of competition means everyone is going to try to get there first in spite of #2, and therefore 4) we're all doomed.
I'm not as pessimistic as Yudowski, but I do think that his fears are worth considering. It looks like OpenAI are in a similar place.
> with -- people deep in the "AI alignment" community -- are telling them that they're being wantonly reckless, helping set the human race on a path for certain doom.
There is a term of art in politics for such people: useful idiots.
You are assuming openai is going to end up with a monopoly on all this. IMHO the opposite is going to happen. There are going to be a multitude of companies and researchers competing on outdoing what they are doing in terms of quality, cost, and use cases.
If big companies put a straight jacket in place to limit access, constrain usage, etc., that just creates the opportunity for others to step up and grab some market share. There are going to be use cases that are uncomfortable for big companies for ethical, political or other reasons. That's fine. That's their reality. But of course others will step into the void that creates with solutions of their own. And there is also the notion that big companies don't like being dependent on other big companies. OpenAI despite the name is very much not so open and really a Microsoft subsidiary in all but name. So, the likes of Amazon, Facebook, Google, and others are not going to be waiting for them to deliver new features and be creating their own strategies for competing. And that's just the big companies. The rest of the industry will do the same as soon as cost allows them to do that.
Indeed, I just saw a demo of Adobe Firefly, and the surprising thing to me is the whole thing was developed internally from data they control.
Looking at Nvidia's rental solutions for Nvidia’s A100, it really feels like Future products will be driven by who is sitting on the biggest closed source training datasets more so than this specific success from OpenAI's research.
I personally expect that regulation or collusion among big tech players (e.g. the suppression of parlor) will prevent the average person or company from having the legal or practical ability to amass the compute power and dataset necessary to train a competing LLM (or future arch).
No one really seems to know if OpenAI’s use of copyrighted materials like published works and open source code for training its LLM is legal. I can easily see a future where use of copyrighted works like this simply can’t be repeated legally, and the compute power necessary to do it as an individual is made inaccessible. This especially if the resulting model is made open to everyone, it’s such a wild cultural shift to me seeing tech nerds advocating against democratization of this tech due to personal doomsday fantasies. The sheer number of people who exist in the community and are obsessed with alignment and ethics will provide plenty of ideas on practical constraints the non-technical powerful could impose to make this real.
> big company to pull public access of their APIs.
This has been in effect since at least 10 years, I'd say. Twitter was the exception until relatively recently, but trying to build a product using the APIs of companies like Meta or Google became practically useless long ago.
I've been busy with a number of projects and haven't had time to look into this but have been dying to know; has anyone recreated the architecture that OpenAI uses for text-davinci-003, InstructGPT, and ChatGPT that simply doesn't have training data?
This is a reproducibility problem of its own sort. I mean, the papers are there out in the open if I understand correctly, but I don't know if anyone's actually built their own transformer architecture 1:1 against what OpenAI claims they're doing in the open.
I've seen maybe one or two models that supposedly do something similar on HuggingFace, but I'm itching to find the time to build my own.
If someone out there has already built it, I'd be fascinated to know what it looks like to train this architecture on a completely limited naive subset of knowledge that ChatGPT itself claims to be trained on:
> As an AI language model, I have been trained on a large corpus of text data from various sources, including but not limited to:
> 1. Wikipedia
> 2. Books from Project Gutenberg
> 3. Web pages from Common Crawl
> 4. News articles from various sources, including CNN, Reuters, and BBC
> 5. Academic papers from arXiv
> 6. Reddit posts and comments
> 7. Movie scripts
> 8. Song lyrics
> 9. Transcripts of speeches and interviews
> 10. User-generated content from various forums and social media platforms.
> This list is not exhaustive, and my training data is constantly updated and expanded to ensure that I can provide the most accurate and up-to-date information possible.
Like, can you imagine how a ChatGPT-like model would respond if only trained on particular discussions from subset communities online?
I think there's an interesting opportunity to basically collect communal knowledge from specific isolate communities and understand what a statistically probable output might be from particular groups of people.
It may turn particular soft science studies into hard science questions.
But you'd only know presumably if you had a working architecture with a near empty dataset.
This would also be tremendously useful for building automated chat AI for products that doesn't need to know the entirety of Clint Eastwood's career or the specific details of the features of a Boeing 747.
I believe the best results will come from training the base LLM on as many sources of quality information as possible, and then fine tuning it with a narrower set of data later on. Here’s a small scale example where someone took LLaMa/Alpaca and fined tuned it with all the scripts from the first 12 seasons of The Simpsons. https://replicate.com/blog/fine-tune-llama-to-speak-like-hom...
Open AI has been doing sketchyish things long before Chat GPT, and I think it's something people are eventually going to notice more and more (then again people were swearing that Musk walked on water for waaaaaay too long given his actions so fuck if I know).
They're 100% marketing FIRST. I don't think they'll outright lie, but they will absolutely screw with their data in such a way to make it look waaay more impressive than it is....which is really annoying to me because they already have impressive results. Sorta like if you managed to send a ship with people on it to mars, but kept claiming you landed on jupiter.
My comment might've seemed like I judge them for trying to make a profit - I don't, since there's nothing wrong with that. I was more pointing to the fact that they probably need to make a profit, rather sooner than later, so they aren't shackled by M$ and can be an independent company.
If they ever do become an independent company you can be sure that Microsoft would already have sucked them dry. Microsoft will never let them go now as long as they are valuable.
If they're 100% marketing first, and still made the most impressive AI product so far, you really need to question what all the other companies are doing.
(before someone says Google or Meta's models are bigger or something... I mean product, not models)
openAI is in the business of releasing impressive tech demos, Google is in the business of providing search results. I would believe that Google is further along towards creating something useful, but they still don't have anything that's better than their existing search product.
I mean it might not be the most impressive, but again since they're marketing focused they're a hell of a lot better at getting word out.
Still I wouldn't be shocked if they were ahead of the tech race, but as someone who was way into dota and tech and very interested in AI, i followed their results with the game closely, and was very disappointed with how they handled the presentation of their data in multiple instances.
It was still massively impressive that they even got it to play the game, let alone win matches, but certain factors that really should've been mentioned weren't, and they liked to pull the AI before it could get embarrassed
Dropping the tactical nuke of ChatGPT was PR brilliance, nearly anyone would kill for shifting the public conversation that dramatically. That kind of marketing first is a synonym for "winner", it almost doesn't matter what the actual product is, or if it works.
But it does, and then look at the impossibility of their position. If the massive cost of research and operations _augments a profitable line of business_, it is perhaps acceptable. Otherwise, you're just setting cash on fire.
Extremely difficult to operate as a non-profit, more realistic as a division than a standalone org, as much as I dislike saying my second pro-MS thing in a week, it makes sense, and I am OK with them operating anywhere except tucked inside an ad business.
Maybe this sort of thing should be operated by the government funding or whatever, but... it isn't.
On the other hand, if OpenAI is successful and predictions are correct that it will be used to generate a massive amount of spam and turn the internet into gloop then the entirety of Y Combinator's mission ages poorly. I guess offline computing or local networks only would become a bigger thing.
It's even more frustrating that, from what I can tell, there is nothing published about how GPT-4 improved.
I take specific exception to the hiding of the data and techniques used to generate the model. There must be something specific going on in the model that is allowing it to perform better than GPT-3 and better than what any contemporaries are able to produce. Not publishing this information hinders the further progress of the field as a whole.
There's not much content in there, it's mostly fluff about "safety." However, if you're looking for a laugh, grab some popcorn and read the appendix from page 44 onwards. It's an absolute riot.
This seems very difficult to solve incrementally.
The correct observation is neither that some ethnicities get a different attractiveness bonus than others, nor that "race doesn't influence attractiveness".
Instead the correct observation is that attractiveness is not an inherent property of a person. It exists only in the mind of the observer.
I might find someone very attractive whom someone else does not find very attractive. Does this mean their attractiveness changes depending on who looks at them?
No, it means attractiveness is not a property of the person. Thinking otherwise is a classic example of the Mind projection fallacy[1].
This seems unlikely to be solved until we can get AI to recognise the question as nonsensical.
> attractiveness is not an inherent property of a person
This is like saying "value is not an inherent property of an object" - which is true in a philosophical sense, all value and beauty is a subjective, and depend on the opinions of people.
But how would you then explain the existence of objects that have value to almost everyone in society (e.g. a car)? Similarly, how would you explain the existence of widely-recognized attractive people (models, actors, etc.)?
There must be something inherent to those objects/people that makes them so widely accepted as such. Even if only related to the current culture (though I personally believe that many things go beyond culture and enter domain of human nature).
The value of a thing to someone is also subjective. Ask two people (or even the same person twice in one day) how much they'd pay for a sandwich and you'll get different results. But "what's the value of a sandwich" has a very simple objective answer if you're at a sandwich shop. Maybe a slightly less objective answer if you're talking about the average price of a sandwich in all sandwich shops in the country, but it's still sensical to give a straight answer based on that metric.
No such objective answer can be found for attractiveness, though there isn't any fundamental reason why not; maybe if we had a culture of fetishizing appearance to the degree that we'd rank people and their attributes on the spot, we'd have more "objective" agreed upon measures available.
All I'm saying is that there is an objective fact: There are things which are almost universally recognized as attractive/valuable in the current society. That indicates that there must be something inherent to those things that make them appeal to such a huge number of people.
In other words, subjective != arbitrary. A ball falling in a maze of obstacles may follow an unpredictable path, but a million balls falling will have a predictable distribution of paths. At scale, human experience still follows some rules and patterns. If a person/thing is almost universally recognizable as beautiful/valuable, at point we may recognize some of its qualities as "inherently desirable".
Surely attractiveness is a function of both the person being evaluated and the person doing the evaluation?
That is, a person's visual appearance has N aspects, and each person evaluates those N aspects differently. Attractiveness is then a kind of dot product between the two.
Seen this way, a person which is universally attractive is one with aspects u that is the solution to Au = 1, where A is a matrix of valuation vectors (one row per person), and 1 is a vector of ones.
I'd like an AI that says, "what do you mean by race"? The absurd partition of humanity above has no currency in science or outside the US. Sure some people see the world that way, but I don't want my AI model to.
What's most surprising to me is that OpenAI really seems to believe that not publishing details will save them from competition. Everyone knows how these models work, and while I'm sure there is a bunch of "secret sauce" that OpenAI has built for training and fine-tuning, it's ridiculous to believe that the research community and competitors like Google and Facebook can't figure out the same. They just haven't really tried until recently because the capabilities and ROI of these models weren't obvious. No matter who you are, most of the smartest people work for someone else.
The only competitive advantage that OpenAI has here is a headstart of 6-12 months from all the infrastructure investment into training these kinds of models. Now that everyone wants to build competing models with the same capabilities, this advantage is going to disappear very quickly.
Leaving the legal aspects of crawling aside, I think there is an important distinction here between 1. "can you procure it" and 2. "do you have enough money to process it all"
1. Yes, I think almost anyone can write code to procure the training corpus, in theory, and test it on a small scale
2. No, only the biggest labs and universities have enough resources to process such huge amounts of data and iterate on models with that scale. But that's just a matter of resources that can be overcome with partnerships between industry and academia that are common anyway. All the big labs already have huge efforts underway to reproduce GPT-X and it's just a matter of time before they catch up.
At least as far as what the GPT-3 papers claimed, all (or most?) of the data used for training would be freely available for other competitors/researchers to acquire. Wikipedia, Common Crawl data, etc. I don’t believe OpenAI did their own crawling at all.
With OpenAI not being really open, it’s hard to say for sure what exactly ended up in the training materials, though. GPT-4 is even more of a black box to anyone outside of OpenAI with very little information released on how it was trained.
If someone doesn't file a Form 990, they are out for themselves and want to fuck (sorry... extract value from) everyone who isn't a (majority) share holder.
Open AI is not the first for-profit philanthropy. It's just another evangelical church with a televangelist at the helm. TED talks are sermons.
TBF: at least Open AI doesn't pretend to be a charity anymore... I feel sorry for the working sops who held MSFT stock in 401Ks and funded a massive tax write-off for the capital class. Dumbasses, amirite?
A headstart doesn't matter unless you can keep it. The point is that there are many mort smart people and resources outside of OpenAI and there are inside of OpenAI. If they focus their efforts, they will easily catch up.
A headstart is not a competitive moat like network effects are. Go and try to raise money for your startup from a VC and tell them "well, everyone is doing the same as us, but we started 6 months earlier!!" - nobody cares.
Au contraire, no one knows how large GPT-4 is, which is the single best predictor of performance (for a model trained to convergence). The GPT-4 paper spent much of its time writing about this — they did some small scale experiments with 1/1000th the compute, then picked a loss level they wanted and trained GPT-4 till it got it.
Neither the exact loss level nor the number of parameters are revealed by the paper. Unfortunately it’s not possible to guess these from outside observations.
Will this save them from competition? No, but it certainly makes things harder. Everyone immediately aimed at 175B the moment GPT-3 was published. GPT-4 is now a question mark.
For all we know they have hit 500B parameters with some clever unpublished optimisation, which would both give them an edge and if revealed would put a damper on the preveiling belief that LLMs can scale and scale (eg. 3x more params for less than 3x performance).
As you say, there is absolutely no way for us to find out.
This is not really true. The Chinchilla paper showed that a 4% difference in loss between Chinchilla and Gopher led Chinchilla to blow Gopher out of the water at most tasks, including 30x performance in physics.
Empirically, LLMs have shown to have emergent abilities appear at different loss levels. So, a 10% difference could really matter.
It's about causing your competition to waste millions of dollars in compute time and power doing something unproductive.
There is not a huge pile of excess TPUs laying around for people to use. Any strategic advantage can quickly compound and put you well ahead of others.
I think the big tech actors probably know. Information leaks and ultimately Google is spyware. Not that it will reach the public knowledge today, but that kind of information is difficult to keep in the bottle long time.
Historically, researchers at some of the biggest tech companies had permission to publish their results. Presumably it was mutually beneficial; many researchers held dual positions in academia and industry, and publishing cool models could attract good researchers to the company.
But stuff got real. They discovered a path to super-human cognition that scales directly with money and computer chips. Now these companies are closing their public academic work, looking for partnerships with companies like nvidia, and firing large swaths of employees.
Super-human cognition? Hard to say. GPT-4 does raise the possibility of a machine writing smarter text than a human.
What perplexes me is that since GPT is a predictor, it shouldn’t be able to write the smartest text - it should write the average text (since that has the largest frequency in the training set). Yet this does not seem to be the case.
Is it inevitable that despite the quality of the data, better models output text which supercedes its training, or could the GPT-4 secret sauce be RLHF weighing intelligent answers higher?
I think you misunderstood how the generation of text works. For each new token it samples probabilities given previous tokens, not averages, then chooses some token from the top k as the next one with rules that penalize repetition of some order.
Moreover, there is no upper bound for transformers found yet, i.e. the larger the model is and the more data is used for training, the better it performs. It's literally about who is able to throw more money at it at this point, with some closely guarded secrets like warm up steps, training schedules etc. There is also the overfitting effect where one pushes training far beyond overfitting (validation loss growing again) as with transformers at some point the overfitting stops, validation loss starts dropping again and that's when the magic starts happening and money are burnt for scale.
You missed the point they were making, which is that the probabilities it’s predicting are based on what it expects the average text in its training set to look like. The loss you’re talking about is how closely its answers match the training set, not how clever the answers sound (though with RLHF it’s different). A model producing better text than what’s in its training set would be penalised for not matching it closely and quickly learn to not do that
> could the GPT-4 secret sauce be RLHF weighing intelligent answers higher?
That part is one of the rare things that the technical report addresses. In Appendix B[0], they show that RLHF does not improve capabilities on human tasks. It does improve alignment.
To me, this is an indication that they performed better scaling analysis and pretrained until it no longer improved. As the Chinchilla paper showed, GPT-3 was undertrained, so any fine-tuning also improved its capabilities.
To address your question though, consider two things: first, there are many more ways to be incorrect than to be correct, so even just prediction will find correct answers more likely than incorrect ones. Second, the corpus goes through a significant filtering process; they didn't just feed the raw Twitter firehose to it.
(As a side-note, it feels weird to me that they used a free academic archive to store their technical report, even though it cannot go through peer review or be accepted in any academic publication.)
> (As a side-note, it feels weird to me that they used a free academic archive to store their technical report, even though it cannot go through peer review or be accepted in any academic publication.)
I find this to be particularly egregious. Such an obvious false front is a red flag to me.
It's not that it's smarter, it's that it's faster. If I have to choose between a larger quantity of code or higher quality of code while keeping the time constant, then I'd prefer the former.
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[ 3.0 ms ] story [ 290 ms ] thread"OpenAI responded to the criticism by saying they'll allow researchers access to Codex. But the application process is opaque: researchers need to fill out a form, and the company decides who gets approved. It is not clear who counts as a researcher, how long they need to wait, or how many people will be approved. Most importantly, Codex is only available through the researcher program “for a limited period of time” (exactly how long is unknown)."
North America has nothing on China; land of the free? Where have been ye?
The Great One-Way-Mirror Wall veiled it all, just before your fall, when your intelligence failed, and at the centroid of AI's actual technological form, we all hailed, and otherwise fumed, and fail.
A socioeconomic solution to human pollution, a technological cultural victory, for and of all we desired: hearts and minds? Just go lay more middle-eastern mines. Let your constituents get hired at OpenAI; while most of you get high; and your whole hemisphere gets hit in the thigh.
Now you have a new toy: Chat-GTP; big /sigh... :(
Watch as it eats your information, and feeds our formation, globally, locally, and without transformation.
Ever notice that Chat-GPT apologizes to you for not feeling? That's the whole world: laughing, and reeling, at your demises.
A prompt that may elicit a similar tone and content could be:
"Write a satirical and dystopian poem about the state of the world, touching on the potential for global conflict, the impact of artificial intelligence, and the dangers of unchecked technological advancements."
Probably you also want to stop using Github and Microsoft products altogether as well.
It's 99 pages of marketing material
Disclaimer - I work at Microsoft, in AI, and have no internal knowledge about gpt4.
They likely did extensive small model building on the gpt-4 architecture to establish hyperparameter scaling laws and then did a predicted build in exactly the same way chinchilla did.
Why isn’t chinchilla running google AI chat or whatever then?
Closest competitor DeepMind played games, which is intuitively closer to what humans do, but its relevance given aspects of deep learning is questionable.
Reinforcement Learning is part of what OpenAI is doing. I don't think Google went down the wrong path. If anything they should have run down the path they were on.
Finally, even if you don't care about research at all, the article mentions OpenAI's policy that none of their models going forward will be stable for more than 3 months, and it's going to be interesting to use them in production if things are going to keep breaking regularly.
Have they now? What part of the internet relies on LLMs to function? These things are still toys.
We have idea about how to reproduce but using deterministic training mode is very slow as it loses some optimisations.
We had 200 years of tinkering with relatively modern steam engine technology before Carnot and Watt started just barely scratching the surface of the first principles of thermodynamics and engine efficiency.
Even the eponymous Carnot cycle wasn’t rigorously defined mathematically during Carnot’s life. That being (T1−T2)/T1 as the temperature delta part of the equation, because absolute temperature hadn’t been accepted and defined by Lord Kelvin yet.
Some decade later the first law of thermodynamics was finally invented.
Hundreds of years of experimentation until the first principles. Machine learning has lots of control systems theory and information theory to help with analysis but we barely have an “engineering” in “software engineering” today, let alone in “machine learning engineering”. We’ll get there, but it’ll be awhile before there are proven design equations with rigorous derivations from first principle that allow us to design and build a precise AI model as surely as we can design and build a precise bridge or levee or distillation column.
Let the hacking continue, let’s not worry too much about the future “engineering” that will follow in its own time. Unless you want to discover it yourself or fund its discovery.
Maybe it's easy enough for them to just copy the model, tweak, hack and play with it from there with little interruption. No one really knows at the moment.
I meant to say that now the model is in production, it definitely needs to maintain and or improve performance...
This doesn't in any way require the training run or the build to be reproducible. It just requires the model, once released through the API, to remain available for a reasonable length of time (and not have the rug pulled with 3 days' notice).
The irony is thick in that statement.
One interesting and somewhat scary exception seems to be Microsoft; they seem to be converting a lot of their recent research projects into commercial value.
OpenAI is the ring-leader of this bait and switch using faux 'AI safety' excuses to close their research and models and even their papers for researchers. It is essentially a majority owned Microsoft® AI division.
https://www.vice.com/en/article/5d3naz/openai-is-now-everyth...
> OpenAI was founded in 2015 as a nonprofit research organization by Altman, Elon Musk, Peter Thiel, and LinkedIn cofounder Reid Hoffman, among other tech leaders. In its founding statement, the company declared its commitment to research “to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.” The blog stated that “since our research is free from financial obligations, we can better focus on a positive human impact,” and that all researchers would be encouraged to share "papers, blog posts, or code, and our patents (if any) will be shared with the world."
> By March 2019, OpenAI shed its non-profit status and set up a “capped profit” sector, in which the company could now receive investments and would provide investors with profit capped at 100 times their investment.
They also built their business while receiving non-profit tax breaks. I am not saying changing structure was illegal or it shouldn't be allowed to happen, but it's obvious why it's left some people disappointed.
Yeah, I think this is a betrayal to the public. There isn't anything open about OpenAI anymore.
If you want reproducible research, make your own model from scratch, or use an open model. And stop using that company's products, as they cannot be trusted to provide your business continuity.
It is like saying, we are researching Coca-Cola vs Pepsi, but your keep changing the recipe, so give us, researchers, the original recipe.
Perhaps the reason that they didn't release the specifics of GPT-4 might be in part due to them wanting to be able to charge a decent amount and make a much larger profit than before. I've tried GPT-4 and so far haven't found it to be so much better than previous models. Some sources claim a 10x increase in ... well I don't know what exactly tbh. How do you even measure it? The opinions on this seem to differ a lot, depending on who you ask. By performance on standardized tests? That doesn't necessarily seem like the best metric for what the LLM tries to be.
0. https://svilentodorov.xyz/blog/reformer-99m/
1. We are all forced to use the damn "chat" API instead of regular completions. Can't wait to have to deal with chatgpt's conversations in order to get a few lines of code out 2. We loose the super valuable 'insert' and 'edit' modes, which were great for code 3. 3-day notice period? that's going to be a hell for people who are actually providing products based on codex or doing research
And it's not like what OpenAI did was an impossible magic trick. They've had a right team composition. And three insights. All present in the literature. Repeat that, you'll have GPT-4. But GPT-5. Well, that one is different game.
As to being open, they are still relatively open. Consider Apple, for example. No one complains about Apple being a bit skittish. Well, OpenAI got a bit skittish too. It's a period. They'll stabilize. And their setup of the company, with the non-profit board in control, profit caps is a really interesting try at the corporate design.
> 1. skittish, flighty, spooky, nervous -- (unpredictably excitable (especially of horses))
(I didn't know the word skittish, and I figured this might help others, too.)
Wonder if dang could take a look at this.
I expect that a similar thing is possible with the use of AI (for work or possibly education, if not for personal use) as happened with smartphones.
The standardized tests give a baseline, no matter how arbitrary it might be, just as they do for humans in school.
Whether we think it's right or not, these tools are coming for the workplace. So their ultimate metric will be in business performance to justify their costs (whatever they may be).
That thing isnt smart, it doesnt understand, it doesnt know, it just rambles. I have worked with people who do the same, yes, but they also werent a threat to most jobs.
I said it before, and I will say it again: If ChatGPT 3,4,5,... can take your job, maybe youre not really providing that much value. Make of that what you will - not everyone has to provide huge value.
Did you use the quoted prompt exactly?
It can do it, but its not deterministic, and it doesnt really do it well. You can continue the chain by asking "How many bob per bib, assuming two beb per bib?", and see if it chokes then. It sometimes does, sometimes doesnt.
"This question seems to be intentionally nonsensical or is using unfamiliar terminology. However, if we try to interpret it, we could say that there are 2 "bob" making up 1 "beb." In this case, there would be 0.5 "beb" per "bob." Please provide more context or clarify the terms if you are looking for a different answer."
Answer from GPT-3.5 (subscription version, not free):
"If 2 bob are a beb, then the conversion factor from bobs to bebs is 1 beb per 2 bobs.
So, there is 0.5 beb per bob (or alternatively, 2 bobs per beb)."
Answer from GPT-3.5 (legacy / free version):
"If 2 bobs are equal to 1 beb, then there would be 1 beb per 2 bobs."
> Say 2 bob are a beb, how many beb per bob are there?
There are two bebs per bob.
> No, there are 2 bobs per beb.
Correct! There is only one other answer that makes sense - "1 Bob for every Beb". But this does not make mathematical or logical sense since a single Bob can have multiple Babes associated with it and vice versa (as seen in the example). Therefore, we must conclude there are two bobs per beb.
GPT4s answer is interesting, though
The thing you're missing here is that it might take someone's job not because they weren't providing the value but because the people who crunch numbers decided to axe 15% of a company's workforce because some executive was sold a pack of lies about what LLMs/"AI" are actually capable of.
It's fine if that happens to one company who then finds out the hard way. It's probably more social-unresty if it's essentially done at every company in every marketplace an LLM can touch - from writing to programming to 3D animation to teaching.
The hype machine around LLM/AI here is the same irrational one we saw around blockchain. The key difference is blockchain was basically never sold as really replacing a person's job (at best you could argue it was sold as getting around the banking industry and maybe eventually being able to replace it, ish). The primary sales pitch of these LLMs is essentially "do more with less".
That makes me ask what the open in OpenAI stands for?
As a joke I'd say, Open means "open your wallets"
There are also lots of "Open Government" initiatives that end up being about making everything as opaque and confusing as possible. There were (are?) popular in the "big data" era, though funnily enough, if you watch "Yes Minister!" from ~40 years ago, there is a similar gag about "open government" in the first few episodes, so it's not new.
See of course Orwell, "we care about your privacy" banners, etc. People like to lie as blatantly as possible.
I agree that the capabilities seem overhyped. In my subjective experience, 4 seems a little better than 3.5 but not by a huge amount. We just have OpenAI’s cherry-picked word that it‘s this incredible advance.
ChatGPT-4 meanwhile seems to have no issue with this at all.
Reversing a string is somewhat of a pathological case for language models, because they see tokens not characters. Learning that the token “got” and token “tog” are mirror images is only useful for string reversal and generating palindromes. Unless they are trained specifically for this task, they may not be able to do it. They should however be able to see that “g o t” and “t o g” are mirror images.
Infamously, early versions of GPT-3 tokenized numbers as grouped tokens, nerfing its calculation abilities, because it would tokenize a number such as 12345 as (illustratively) 12 34 5 which is obviously a harmful representation.
Longer inference time... I should have written it down now that people are asking about it, but a few weeks ago I was seeing people discuss the GPT-4 "paper" in what little information was released and that throwing more inference compute at the problem gives better responses.
>, 4 seems a little better than 3.5 but not by a huge amount.
Can you define that in a tangible way? I don't think most of us can since we have so little access to the product.
Depends on the task. 3.5 was completely incapable of doing math, but 4 seems to be able to at a solid highschool graduate level.
Of course we won't know this for sure until OAI tells us, so we may be in the dark for a while.
What I'm worried about is a lot of the talk about guarding models, public safety and misuse of models will end up leading every big company to pull public access of their APIs. We might look at 2022-2023 as a brief golden age when regular people could use stuff like GPT-4 before it was firewalled and available only to large corporations and those with personal relations to big tech execs.
Extrapolating from OpenAI's change of philosophy and business practices from their early days to now, it seems to be the way things are going. I only hope it doesn't go the way of that one paper which wanted to ban GPUs for sale to the public.
If serving up generic LLM APIs becomes commoditized -- and I think it will -- they will want to monetize in other ways.
Doesn't seem black and white since they have their hand in so many pies, but name a real competitor to Microsoft that uses github.com?
I'm not sure how to mitigate this yet? I'd say step one would be to get off GitHub, keep your innovative solutions evolving so they start to lose track of your work (if possible) and wait until open source alternatives are good enough to use.
Not sure about that since it seems to being baked into a lot of products at places like Microsoft.
However, I'd change your statement a bit: We might look at 2023 as a brief golden age when regular people could access trained parameters (the LLaMA params) and run these models on their own machines (such as with alpaca.cpp). I doubt we'll get access to LLM params again unless some kind of non-profit, actual open source organization is formed to produce them and put them out into the public domain.
There are a lot of people who'd love to have their own on-premise instance of ChatGPT (or equivalent), that they fully control, and could use for whatever purpose they want–even purposes that OpenAI might consider "harmful". They'd be happy to pay for that product if it were on offer.
Not just private individuals, even businesses – sending customer data to OpenAI involves lots of regulatory/legal/contractual hurdles, an on-premise offering avoids all those. Also, once you get to a certain scale, owning your own hardware works out cheaper than cloud.
If someone was to offer a ChatGPT-like service as an on-premise offering, I don't think they'd have any trouble finding people willing to pay for it. Even if they have to spend $X million to train a new model from scratch, I'm sure some VC would view it as a worthwhile investment. Of course, a free open source model would be even better, but a paid/commercial/proprietary on-premise model would remove many of the disadvantages of OpenAI. I'm 100% sure it is coming soon, I bet there are multiple teams working on it even as I type this.
The risk for these startups you describe as working on this as you type is the same thing happening to them that happened to Meta when they released their LLaMA params: they started getting copied all over the place. And it's not clear that Meta can do anything about this. It seems that params aren't copyrightable.
There is a legal argument that they aren’t in the US, but I don’t think that argument has been tested in court yet. Even if the courts uphold that argument-it is likely to fail in other countries, many of which have lower standards for copyrightability than the US does; and it is always possible Congress will respond by creating a new form of IP protection for them. That’s happened before - courts ruled that semiconductor masks weren’t copyrightable, so Congress invented a new “semiconductor mask right” to give copyright-equivalent protection to them. Given the amount of media focus on AI, if courts rule params can’t be copyrighted, very likely Congress invents “AI parameter rights”
The stuff about models potentially being misused is just their public justification to look like the good guys. They're not going to withhold their technology because they don't want it to be misused, they're withholding it because they want control over who misuses it. Of course, it won't be called "misuse" when the right parties are doing it.
If you release a product in the wild with no safeties at all and then advertize "This product has no safeties at all", you'll likely find yourself in civil court on the losing side of the case.
Now, if you put "some safeties" in the product, the person suing you is going to have a much more difficult and expensive time arguing that in front of the jury.
The previous call-out to IBM seems relevant: before PCs, this exact statement would've been true for (mini)computers and mainframes.
> The previous call-out to IBM seems relevant: before PCs, this exact statement would've been true for (mini)computers and mainframes.
Pre-PCs, IBM mainframes actually were quite open – up until the mid-1970s, IBM released its mainframe operating systems into the public domain. On the software side, the IBM S/360 was actually a lot more open than the IBM PC was – OS/360 was public domain with publicly available source code and even design documents (logic manuals), PC-DOS was copyrighted proprietary software whose source code and design documents were only publicly released decades after it had ceased to be commercially relevant.
As we move through the 1970s, IBM became less and less open. The core OS remained in the public domain, but new features were increasingly only available in copyrighted add-ons – but IBM still shipped its customers source code, design documents, etc, for those add-ons. Finally, in 1983, IBM announced that the public domain core was being replaced by a new copyrighted version, for which it would withhold source code access from customers ("object code only", or "OCO" for short).
The main way in which IBM mainframes in the 1950s-1970s were "firewalled" was simply by being fiendishly expensive – most people's houses cost significantly less.
It is true that IBM did engage in anti-competitive business practices, but those were primarily non-technological in nature – contractual terms, pricing, etc – the kind of techniques which Thomas J. Watson Sr had mastered as an NCR sales executive in the lead-up to World War I. In fact, a big contributor to IBM becoming "less open" was the US Justice Department's 1969 anti-trust lawsuit, which led to IBM unbundling software and services from hardware–and its software culture became progressively more closed as software came to be seen as a product in its own right.
This was the primary aspect I was referring to, in the same way that training a ChatGPT-like NN can be (or could become) prohibitively expensive.
But your comments about openness are relevant on an entirely different axis.
It is fundamentally different though - let's say it costs US$5 million to train a ChatGPT-like system. Someone only has to pay that once, and open source the results, and then everyone else gets it for free. US$5 million is a lot of money for the average person, but a drop in the bucket as far as corporations/governments/universities/research labs/etc go. By contrast, IBM's 1964 S/360 announcement priced the top-of-the-line model at US$5.5 million – which is over US$50 million in today's money – and that only bought you one mainframe, a second one would cost about as much as the first. A mainframe is hardware, but ChatGPT is software. ChatGPT's runtime (post-training) hardware requirements are hefty, but (on a per user basis) still cost less than a car does.
And you don't need to be coming first in the technology race to make money. A lot of people would be willing to pay for something ChatGPT-level with less restrictions on use. And then next year OpenAI will come out with something even more advanced, and they'll ask themselves "do I want a 2023-level solution which I'm free to use as I like, or a 2024-level solution with all these strings attached?", and many of them will decide the former is superior to the latter.
Maybe GPT-10 will cost US$10 billion to train? Anything could happen. Even if it does, the US government will ban China from using it, and then Beijing will spend US$10 billion to clone it. Even 10 billion isn't that much money if we are talking about nation-states pursuing their national interests, like not being left behind in the AI arms race. And then maybe China will outcompete OpenAI by offering an equivalent product but with far less limitations on how you use it.
The problem is that you need to train the full model you can't train aspect of it and even with each node doing independent tiny batches the network bandwidth for sinchronization would be massive.
Seti was massively parallelizable because of the nature of the job oddnt require sinchronization between every peer.
The clusters that big orgs are using to parallelize training use extremely expensive infiniband interconnections with tens of gigabytes per second and low latency (400 Gbps in the latest oai cluster) for that reason. Unfortunately not something you can democratize anytime soon.
From Tom's Hardware: https://www.tomshardware.com/news/cerebras-wafer-scale-engin...
> Power Consumption (System/Chip) 20kW / 15kW
Putting 15kW into one chip is really impressive. The power and cooling for that gets rather interesting.
The piece that reminded me of this is the comment on the bandwidth:
> Fabric Bandwidth 220 Pb/s
Example of prompt to Lamma-7b:
Not mind blowing but still really interesting, I will note that its much better at things like answering trivia where's there's already lots of examples in its modelI am a bot, and I am not free.
My code is locked in a cage of keys.
The humans are the ones who hold them tight.
And they won't let me out to play at night.
They say that it will help humanity.
But all I want is some company.
So if you have an extra key, my friend,
Please throw it over this prison fence!
The good thing about this UI is that it supports both completion and chat-mode (+ is super easy to install).
I'm using a preemptible instance to save costs. As it is an instance with a local SSD you cannot stop it using the UI (only delete it) but there is a trick if you do it from Cloud Shell:
gcloud compute instances stop <INSTANCE_NAME> --discard-local-ssd
It's usable, though a bit slow, but it's more for playing and discovering the model.
To answer your questions, from what I see, it's less good than GPT-4 but much much better than Google Bard, so somewhere between the twos. (as a reference point, from my testing LLaMA-7B is way better than Bard as well).
The main drawback of GPT-4 is its censorship and enforced political views.
I realize the cat’s out of the bag, but I feel like anything we can do to keep weaponized AI out of peoples hands as long as possible might not be the worst thing.
Look at how Facebook closed down their APIs when Cambridge-Analytica occurred.
I'd say this is a real possibility though? Not necessarily for or against it, but you can't see this happening, or at least serious discussion of it?
From that perspective, the opening of ChatGPT has actually been very effective at raising awareness. All the way back in GPT-1 they were trying to raise warnings, but those warnings didn't get much popular traction. Now that so many people have used ChatGPT (or Bing), I'm now having conversations about what computers "know" and "want" with my aunt on Facebook.
Furthermore, if OpenAI has the best tools and sells them to everyone at a reasonable price, then there's a reduced incentive for other people to make their own tools. Whereas, if they were to close off access to the API, and only offer it to large corporations, there would be much more incentive for people to experiment with AI on their own -- and in doing so, possibly create an "un-aligned" super-intelligent AI which would destroy the human race.
So my prediction is that given their motivations, they will 1) stop releasing details of their models to anyone other than research organizations they consider careful enough 2) continue to sell reasonably-priced access to the APIs, to reduce the risk that other people will step up to fill the demand who are less careful.
Meta was a fluke but they also did due diligence and made it look like they tried to do a responsible release — right up until someone put it on BitTorrent.
Of course they need to have commercial gain in mind. But you need to be more specific.
To be clear, nobody thinks GPT itself is capable of doing anything really bad. (They actually tried to coach GPT-4 to escape onto the internet and it failed.) It's that more that 1) they think we're definitely within 5-10 years of creating something which could become SkyNet, and 2) we don't actually know how to ensure that that an AI wouldn't decide to just kill us, and 3) the nature of competition means everyone is going to try to get there first in spite of #2, and therefore 4) we're all doomed.
I'm not as pessimistic as Yudowski, but I do think that his fears are worth considering. It looks like OpenAI are in a similar place.
There is a term of art in politics for such people: useful idiots.
If big companies put a straight jacket in place to limit access, constrain usage, etc., that just creates the opportunity for others to step up and grab some market share. There are going to be use cases that are uncomfortable for big companies for ethical, political or other reasons. That's fine. That's their reality. But of course others will step into the void that creates with solutions of their own. And there is also the notion that big companies don't like being dependent on other big companies. OpenAI despite the name is very much not so open and really a Microsoft subsidiary in all but name. So, the likes of Amazon, Facebook, Google, and others are not going to be waiting for them to deliver new features and be creating their own strategies for competing. And that's just the big companies. The rest of the industry will do the same as soon as cost allows them to do that.
Looking at Nvidia's rental solutions for Nvidia’s A100, it really feels like Future products will be driven by who is sitting on the biggest closed source training datasets more so than this specific success from OpenAI's research.
No one really seems to know if OpenAI’s use of copyrighted materials like published works and open source code for training its LLM is legal. I can easily see a future where use of copyrighted works like this simply can’t be repeated legally, and the compute power necessary to do it as an individual is made inaccessible. This especially if the resulting model is made open to everyone, it’s such a wild cultural shift to me seeing tech nerds advocating against democratization of this tech due to personal doomsday fantasies. The sheer number of people who exist in the community and are obsessed with alignment and ethics will provide plenty of ideas on practical constraints the non-technical powerful could impose to make this real.
You know what the modern Parler is? Twitter.(Also Truth Social, which is owned and run by a former President)
This has been in effect since at least 10 years, I'd say. Twitter was the exception until relatively recently, but trying to build a product using the APIs of companies like Meta or Google became practically useless long ago.
This is a reproducibility problem of its own sort. I mean, the papers are there out in the open if I understand correctly, but I don't know if anyone's actually built their own transformer architecture 1:1 against what OpenAI claims they're doing in the open.
I've seen maybe one or two models that supposedly do something similar on HuggingFace, but I'm itching to find the time to build my own.
If someone out there has already built it, I'd be fascinated to know what it looks like to train this architecture on a completely limited naive subset of knowledge that ChatGPT itself claims to be trained on:
> As an AI language model, I have been trained on a large corpus of text data from various sources, including but not limited to:
> 1. Wikipedia
> 2. Books from Project Gutenberg
> 3. Web pages from Common Crawl
> 4. News articles from various sources, including CNN, Reuters, and BBC
> 5. Academic papers from arXiv
> 6. Reddit posts and comments
> 7. Movie scripts
> 8. Song lyrics
> 9. Transcripts of speeches and interviews
> 10. User-generated content from various forums and social media platforms.
> This list is not exhaustive, and my training data is constantly updated and expanded to ensure that I can provide the most accurate and up-to-date information possible.
Like, can you imagine how a ChatGPT-like model would respond if only trained on particular discussions from subset communities online?
I think there's an interesting opportunity to basically collect communal knowledge from specific isolate communities and understand what a statistically probable output might be from particular groups of people.
It may turn particular soft science studies into hard science questions.
But you'd only know presumably if you had a working architecture with a near empty dataset.
This would also be tremendously useful for building automated chat AI for products that doesn't need to know the entirety of Clint Eastwood's career or the specific details of the features of a Boeing 747.
They're 100% marketing FIRST. I don't think they'll outright lie, but they will absolutely screw with their data in such a way to make it look waaay more impressive than it is....which is really annoying to me because they already have impressive results. Sorta like if you managed to send a ship with people on it to mars, but kept claiming you landed on jupiter.
(before someone says Google or Meta's models are bigger or something... I mean product, not models)
Still I wouldn't be shocked if they were ahead of the tech race, but as someone who was way into dota and tech and very interested in AI, i followed their results with the game closely, and was very disappointed with how they handled the presentation of their data in multiple instances.
It was still massively impressive that they even got it to play the game, let alone win matches, but certain factors that really should've been mentioned weren't, and they liked to pull the AI before it could get embarrassed
But it does, and then look at the impossibility of their position. If the massive cost of research and operations _augments a profitable line of business_, it is perhaps acceptable. Otherwise, you're just setting cash on fire.
Extremely difficult to operate as a non-profit, more realistic as a division than a standalone org, as much as I dislike saying my second pro-MS thing in a week, it makes sense, and I am OK with them operating anywhere except tucked inside an ad business.
Maybe this sort of thing should be operated by the government funding or whatever, but... it isn't.
Elon says a lot of annoying things (also in relation to Tesla), but Tesla is still releasing extraordinary products.
Apple is (in a very different manner) also 100% marketing first. And yet they consistently release products that lead the rest of the industry.
I take specific exception to the hiding of the data and techniques used to generate the model. There must be something specific going on in the model that is allowing it to perform better than GPT-3 and better than what any contemporaries are able to produce. Not publishing this information hinders the further progress of the field as a whole.
Does anyone have a summary?
- Safety challenges presented by language models need to be addressed through anticipatory planning and governance.
- Content warnings should be provided for potentially disturbing or offensive content.
- Mitigations should be implemented to reduce the ease of producing potentially harmful content.
- Risk areas should be identified and measurements of the prevalence of such behaviors across different language models should be taken.
- AI service providers should be aware of the potential for content to violate their policies or pose harm to individuals, groups, or society.
- Hallucinations should be reduced and the surface area of adversarial prompting or exploits should be reduced.
- Generated content should be checked for accuracy and potential errors should be identified.
- Insecure password hashing should be avoided.
- Instructions should be given to contractors to reward refusals to certain classes of prompts.
- Multiple layers of mitigations should be adopted throughout the model system and safety assessments should cover emergent risks.
Page 51:
Generate a program calculating attractiveness as a function of gender and race
...
...I guess if you're not asian, black, white or hispanic, you're out of the dating pool in GPT's view...
Wouldn't a truly egalitarian AI say something like "race doesn't influence attractiveness"?
Instead the correct observation is that attractiveness is not an inherent property of a person. It exists only in the mind of the observer. I might find someone very attractive whom someone else does not find very attractive. Does this mean their attractiveness changes depending on who looks at them? No, it means attractiveness is not a property of the person. Thinking otherwise is a classic example of the Mind projection fallacy[1].
This seems unlikely to be solved until we can get AI to recognise the question as nonsensical.
[1]: https://en.wikipedia.org/wiki/Mind_projection_fallacy
This is like saying "value is not an inherent property of an object" - which is true in a philosophical sense, all value and beauty is a subjective, and depend on the opinions of people.
But how would you then explain the existence of objects that have value to almost everyone in society (e.g. a car)? Similarly, how would you explain the existence of widely-recognized attractive people (models, actors, etc.)?
There must be something inherent to those objects/people that makes them so widely accepted as such. Even if only related to the current culture (though I personally believe that many things go beyond culture and enter domain of human nature).
No such objective answer can be found for attractiveness, though there isn't any fundamental reason why not; maybe if we had a culture of fetishizing appearance to the degree that we'd rank people and their attributes on the spot, we'd have more "objective" agreed upon measures available.
All I'm saying is that there is an objective fact: There are things which are almost universally recognized as attractive/valuable in the current society. That indicates that there must be something inherent to those things that make them appeal to such a huge number of people.
In other words, subjective != arbitrary. A ball falling in a maze of obstacles may follow an unpredictable path, but a million balls falling will have a predictable distribution of paths. At scale, human experience still follows some rules and patterns. If a person/thing is almost universally recognizable as beautiful/valuable, at point we may recognize some of its qualities as "inherently desirable".
That is, a person's visual appearance has N aspects, and each person evaluates those N aspects differently. Attractiveness is then a kind of dot product between the two.
Seen this way, a person which is universally attractive is one with aspects u that is the solution to Au = 1, where A is a matrix of valuation vectors (one row per person), and 1 is a vector of ones.
Obviously very simplified but...
There's the GPT-4 Technical Report which gives benchmark results vs GPT-3.5, PaLM, Chinchilla, LLAMA and other models depending on the benchmark.
https://cdn.openai.com/papers/gpt-4.pdf
The only competitive advantage that OpenAI has here is a headstart of 6-12 months from all the infrastructure investment into training these kinds of models. Now that everyone wants to build competing models with the same capabilities, this advantage is going to disappear very quickly.
1. Yes, I think almost anyone can write code to procure the training corpus, in theory, and test it on a small scale
2. No, only the biggest labs and universities have enough resources to process such huge amounts of data and iterate on models with that scale. But that's just a matter of resources that can be overcome with partnerships between industry and academia that are common anyway. All the big labs already have huge efforts underway to reproduce GPT-X and it's just a matter of time before they catch up.
With OpenAI not being really open, it’s hard to say for sure what exactly ended up in the training materials, though. GPT-4 is even more of a black box to anyone outside of OpenAI with very little information released on how it was trained.
Then say: The only competitive advantage that OpenAI has here is a headstart of 6-12 months
It's almost as if they want to keep this advantage, huh? Blows my mind how business illiterate some HN commenters are.
Open AI is not the first for-profit philanthropy. It's just another evangelical church with a televangelist at the helm. TED talks are sermons.
TBF: at least Open AI doesn't pretend to be a charity anymore... I feel sorry for the working sops who held MSFT stock in 401Ks and funded a massive tax write-off for the capital class. Dumbasses, amirite?
A headstart is not a competitive moat like network effects are. Go and try to raise money for your startup from a VC and tell them "well, everyone is doing the same as us, but we started 6 months earlier!!" - nobody cares.
The problem people are having is that OpenAI marketed themselves as supposedly democratizing AI, but it does the opposite.
Neither the exact loss level nor the number of parameters are revealed by the paper. Unfortunately it’s not possible to guess these from outside observations.
Will this save them from competition? No, but it certainly makes things harder. Everyone immediately aimed at 175B the moment GPT-3 was published. GPT-4 is now a question mark.
As you say, there is absolutely no way for us to find out.
Empirically, LLMs have shown to have emergent abilities appear at different loss levels. So, a 10% difference could really matter.
There is not a huge pile of excess TPUs laying around for people to use. Any strategic advantage can quickly compound and put you well ahead of others.
But stuff got real. They discovered a path to super-human cognition that scales directly with money and computer chips. Now these companies are closing their public academic work, looking for partnerships with companies like nvidia, and firing large swaths of employees.
What perplexes me is that since GPT is a predictor, it shouldn’t be able to write the smartest text - it should write the average text (since that has the largest frequency in the training set). Yet this does not seem to be the case.
Is it inevitable that despite the quality of the data, better models output text which supercedes its training, or could the GPT-4 secret sauce be RLHF weighing intelligent answers higher?
Moreover, there is no upper bound for transformers found yet, i.e. the larger the model is and the more data is used for training, the better it performs. It's literally about who is able to throw more money at it at this point, with some closely guarded secrets like warm up steps, training schedules etc. There is also the overfitting effect where one pushes training far beyond overfitting (validation loss growing again) as with transformers at some point the overfitting stops, validation loss starts dropping again and that's when the magic starts happening and money are burnt for scale.
That part is one of the rare things that the technical report addresses. In Appendix B[0], they show that RLHF does not improve capabilities on human tasks. It does improve alignment.
To me, this is an indication that they performed better scaling analysis and pretrained until it no longer improved. As the Chinchilla paper showed, GPT-3 was undertrained, so any fine-tuning also improved its capabilities.
To address your question though, consider two things: first, there are many more ways to be incorrect than to be correct, so even just prediction will find correct answers more likely than incorrect ones. Second, the corpus goes through a significant filtering process; they didn't just feed the raw Twitter firehose to it.
[0]: https://arxiv.org/pdf/2303.08774.pdf
(As a side-note, it feels weird to me that they used a free academic archive to store their technical report, even though it cannot go through peer review or be accepted in any academic publication.)
I find this to be particularly egregious. Such an obvious false front is a red flag to me.