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Comment by simonw | Hacker News Reader

I'm very impressed at the quality of Guanaco 33B, the model that accompanies this paper.

You can try it out here: https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi

I tried "You are a sentient cheesecake that teaches people SQL, with cheesecake analogies to illustrate different points. Teach me to use count and group by" and got a good result from it: https://twitter.com/simonw/status/1661460336334241794/photo/...

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Altman’s push for regulatory capture makes so much sense given how fast this field is going. Open models you can run on regular hardware are still behind GPT-4 by some distance but they are closing in at a rate that leads me to believe there’s not much of a moat there.

There’s also a ton of promising work on quantization and pruning and other acceleration and compression techniques to make more powerful models run on smaller devices. So far the focus has been on just getting these things to work, not efficiency. There’s probably a lot of fruit to be picked here.

A few more years and a gaming PC may be at GPT-4 level or maybe even better.

No everyone won’t run their own models but it shows that there will end up being many commercial apps and services and they won’t all have to use OpenAI’s API. There’s going to be lots of competition. Unless of course it’s regulated away.

To be fair, Altman has been fairly outspoken about not limiting open source models. In how far that was just for streetcred, I cannot say however.

If progress really continues like this any regulation that does not limit open models would be a pointless exercise.

It’s only a matter of time before people really crack distributed training algorithms that can be run in a less organized swarm configuration. At that point open trainers could actually train near the frontier of what is possible. Most of the data is open.

Whether or not the regulation goes anywhere, OpenAI keeps doing what they're doing, and they look good to politicians for being cooperative.

> It's only a matter of time before people really crack distributed training algorithms that can be run in a less organized swarm configuration.

Why do you think this? This continually failed, and seems extremely unlikely to me. Barring surprising breakthrough, there is inherent communication complexity, and physical limit to communication bandwidth.

Yeah the network bandwidth is insane. Each A100 in an 8x A100 (80GB) pod [1] has its own 200 gigabit NIC! Including storage that's nine 200gbps interfaces for almost two terabit of total internal bandwidth.

The newer H100s each have a 400gbit NIC.

[1] https://shop.lambdalabs.com/deep-learning/servers/hyperplane...

The assumption I'm questioning is that it will always take a rig like that to train large models. I don't see anything inherent in the math or nature of these things that forbids a wider, more heterogenous, less synchronized, less orderly training path that involves basically a mob of people training and merging results. We just haven't really figured it out yet.

I actually see a little bit of this happening on Huggingface with people creating variations and "remixes" of generative models like Stable Diffusion and trying to one-up each other or make models to do esoteric things like render everything looking like anime. You're not going to get to the next frontier model with those methods but it shows that the interest exists and a flourishing ecosystem is forming. Now give that ecosystem new methods that are more powerful.

People with more money can obviously buy or rent more hardware. The question is whether that advantage will stay as meaningful as it is today forever.

We could try something like Civitai is doing already, but automated.

Each node could train the model on a separate concept and then combine the results.

The wording of this would be extremely difficult though. Are local NER models part of this? Relation extraction? What about GPTs that only decode to DSLs? If the model only outputs DNA sequences is that an area that can be more illegal or less if done for research by an individual? The breadth of different tasks and architectures can make this exceedingly challenging to regulate.

Open models means that OpenAI has access to and can learn from them. If the move is to target commercial competition, then this doesn't preclude it.

I won't take him seriously on this - if one really believes in the case for regulation, than there's no good reason to exclude open source models. Let's take the most pessimistic possibility, that they have a slower rate of progress than commercial models, and real progress depends on hardware. We still end up with the same result - every 'risky' point commercial models get to, open source will get to as well.

I wonder if there's a campaign that Americans can get behind to counter Altman's push for regulation. Such a thing might get me to engage in politics again. I don't want the current pace of innovation in running these models on one's own computer to slow down, because it could be a good thing for assistive technology, e.g. running something like the Be My Eyes Virtual Volunteer [1] on one's own computer.

[1]: https://www.bemyeyes.com/blog/introducing-be-my-eyes-virtual...

>Open models... are still behind GPT-4 by some distance but they are closing in at a rate that leads me to believe there’s not much of a moat there

Commercially, this doesn't matter even if true. As I keep reminding here: moats are rarely about raw tech. Moats are much more about integrations, brand ("Nobody got fired for buying X"), access to API/raw compute...

If OpenAI have a superior Office 365 integration they'll have a de facto moat. If OpenAI have a larger plugin ecosystem they'll have a de facto moat. If OpenAI has access to better compute (that's far from a certainty) they'll have a moat. If it's much easier to use OpenAI than install a local model they'll have a moat, etc. And that's true even if they don't improve their model at all.

What will open source offer? Privacy? You can see for how little and how easily people barter that.

Moats do fall - but for that, FOSS will have to think product. We're not there at all at the moment.

[EDIT: Oh, I didn't notice the author's name. We've had that conversation in the past here. Sorry for being repetitive.]

> Open models... are still behind GPT-4 by some distance but they are closing in at a rate that leads me to believe there’s not much of a moat there

This assumes that openai internally doesn't also "closing-in" towards something even more impressive to be released next year or whatnot.

I agree with you on that a good integrator can get successful, but we should not forget here that the underlying code running all of this is in the order of hundreds of lines (see alpaca.cpp for example) in combination with a boatload of money required for training (that is quickly going down), to simplify the argument a bit.

It's hardly comparable to the gigantic ecosystem of services and micro-services that your comparison alludes to with IBM and Microsoft. OpenAI is nowhere near such a brand recognition, and grassroots support for it within a company would quickly move to the next free chatgpt-clone that is better or cheaper or faster or more accessible just as what happened with Dalle-2.

The value of the models themselves will quickly come down to just a slight bit over the underlying hardware costs and Altman knows this.

Microsoft bought themselves a $10B time window to try to do what you're saying, so let's see :) But even for them, when they've built LLM-adaptations to their most popular products, it's fairly simple to just swap it out with something new and more shiny and cheaper that's not OpenAI, and the end customer won't notice as it's the Microsoft or Office brand that they buy into. They are not going to advertise what's inside their products with big banners "Powered by OpenAI" in the long run, I think (do they now?)

What you refer as 'the underlying code' isn't the real underlying code, the underlying code is the model. Your underlying code is just the runtime, which is commercially the least interesting part (which is why it is/will be/ open sourced). Both the model and the integration are way more interesting commercially. I'm sure the code Microsoft has to integrate their assistant to Windows is way more than hundreds of lines. How long until GNOME/KDE even start on something similar? Probably not before 2030.

Anyway, I treat OpenAI and Microsoft as two sides of the same coin given level of integration between the two. It's arguable the Microsoft has the upper hand here but OpenAI is their main LLM talent. [EDIT: I don't see MS switching backends from a backend they control, especially when performance apparently is adequate enough already and the real cost isn't licensing the code, but Azure, so open source doesn't necessarily have an advantage here.]

Open models can talk about "forbidden" topics and can be extended by users. Both of them are significant advantages.

The ability to talk about 'forbidden' topics is also a significant disadvantage. Just wait for the first moral panic targeting open source GPTs. I think that Open Source will exist (barring a legal ban), but the triumphalism is in my mind very unjustified. There's a fair chance closed source will get 99% of this market.

It's an advantage, because each user can apply different restrictions to Open Source AI models. If I'm writing a crime novel for adults - I want the assistant to be able to talk about sex and violence. But obviously - a corporate customer support chatbot should not talk about those things.

I understand why ClosedAI added those restrictions. But they are too inflexible.

I alluded to what you say in the last paragraph: the average user isn't going to play with open source models, but the existence of the open source AI ecosystem will enable more competitors.

There's not going to just be one AI company. There will be thousands and thousands, each addressing different use cases and market niches. In a world where OpenAI has a powerful technological moat, all of these companies would end up having to pay rent to OpenAI. In a world with a strong open source AI ecosystem that's not the case. They can take open source models and even train them themselves and refine them for specific use cases.

Winner take all dynamics in general are overstated. They exist in a few niches but not most. How many networking, database, file sync, cloud, gaming, banking, or hosting companies are there? There's even been markets that once looked winner take all like social media that have recently experienced a flurry of diversification.

Edit: there's one more reason I'm not sure moats are strong in AI: AI can write code and can process "messy" inputs. One of the thing that strengthens moats built around integrations and such is that the difficulty of doing the integration is part of the barrier. Integrations are frankly annoying and labor intensive to create. With AI you can just tell it to integrate in natural language and schlep messy imperfect data into it. That makes integrations significantly less labor intensive, making it easier for a competitor to pop up and add them very easily and quickly.

I agree OpenAI won't and can't have anywhere near a monopoly, excluding very unlikely legal shenanigans. The field is too approachable, and ultimately, to pinch into their market one doesn't need GPT-4 level performance. Besides, apparently OpenAI can't scale compute fast enough, so users will end up by default requiring other providers. There's a very good chance of OpenAI/Microsoft being major players though IMHO.

I can however see a possible future where open source is not going to have any significant impact on LLMs, say like Desktop Linux. Either because it gets stuck in a technical realm and doesn't make anything too approachable to ordinary users, or because it lacks the necessary integrations, or because developers get stuck arguing about the license (raw LLAMA not being good enough due to the non-commercial requirement), or because a moral panic ("4chanGPT is radicalizing people!") leads to a form of legal restrictions that makes open source efforts difficult to sustain. This doesn't have to be, so long as the hacker community can avoid falling into complacency.

On integrations, you're thinking about input, but there are still significant challenges there, the output step, API keys, rate limits, various crazy API corners, certifications... LLMs will help, but I expect integration to still be annoying.

For example, the Microsoft example where the Assistant changes the system to Dark Mode. You can't use LLM messy input to get that output on a generic level (and if you could, that would risk the LLM as an attack vector). You might be able to use a software development LLM to help write the code to do that specific thing and make it available to the product LLM, but ultimately that's a generic software productivity speedup - which also 'helps' those writing the API to make it more complicated and do more stuff we'll need to implement...

I agree that pure open source probably won't cross over into general use. Open source is great at tech but bad at product, mostly because product requires doing a lot of boring polish that programmers hate and thus generally have to be paid to do (I have ranted about this extensively here and elsewhere). Consumers also hate choice these days because they're all in a state of continuous cognitive overload. They want the one thing to do the one thing and stop bothering me.

That being said, Linux and open source have had a gigantic effect on the market. This hasn't been by shipping products directly to consumers but by enabling a ton of startups to get there faster and cheaper. OSS enables a ton of innovation and consumers benefit from that.

I'm arguing that the same thing is going to happen in AI. That's all. AI will get faster, better, and cheaper, and the basic functionality will get commoditized. That will lead to more competition and more variation and make it hard for people like OpenAI to have a monopoly.

I'm sure OpenAI will still exist. They might stay a dominant player. I just don't see a world where they "own the tech" and get to be "the only AI" and charge rent to the entire industry because nothing works without their API. That's a fantasy... unless they can legislate it into existence, which is what I think they're trying to do.

On integrations: what happens when everyone uses AI? One of the most exciting possibilities I see for this technology is to entirely toss out the persnickety concept of the API in favor of LLMs talking to LLMs. Making software interoperate in the conventional way frankly sucks. It's a terrible slog through mud. Imagine if I could just say "hey app I just wrote, meet GitHub! GitHub, please explain to my app what you can do and how to access your capabilities... Now tell GitHub to... now tell other app on my machine to... now tell Amazon to..."

We are going to look back at how we did software interoperability the way we look at programming mainframes with punched cards.

Well, I guess we agree here - the basic functionality will be common, Open AI will not have a monopoly (unless lawmakers really screw up), but will likely exist and be a major player. I am not sure about Open Source at all just yet - e.g. it's possible that a panic will lead to restrictions that will make open work rather difficult. I guess we'll see.

>what happens when everyone uses AI? One of the most exciting possibilities I see for this technology is to entirely toss out the persnickety concept of the API in favor of LLMs talking to LLMs.

Hmm. Your idea has lower throughput and higher latency, while having not entirely clear security properties. But really, companies will sacrifice nearly all that in the service of faster shipping, paying less to programmers, and easier interfacing. Just look at the history of latency in user systems from the 1980s till today. The only game breaker here is security.

Hm. We implement an internal API that the LLM must use in order to do anything local, and do the limiting/auditing there. So the external interface is language, but what the LLM can do is either use the limited internal API to do approved actions locally, or speak to other LLMs (which will have their own internal APIs) using the user's context (OpenID token, whatever). There's a slight risk in the security boundary here (e.g. make sure the LLM never impersonates - don't allow it to see the actual user context, make sure each LLM instance only has access to the current user context. Also need to make sure it doesn't tell the other LLMs too much), but I think it's handleable?

Regardless, we aren't there yet, and there are still a few issues here (e.g. I expect Microsoft/Google to always have an advantage in Office 365/GSuite integrations somehow). Until we do, integrations will still have a big effect on the market.

Thinking about this a bit more, the main issue is that the LLM is an attack vector. Hallucinations aren't a big deal if the action requires approval or can be easily undone. The only way right now I can see to make LLM interfaces 'safe' would be to give it an internal API and pass the user context so we can set up permissions that way. There are a very few cases where even that might not be enough (basically whenever you pass 'on-behalf-of' transactions _without_ direct approval from the origin via a temporary token - e.g. many backend payment APIs allow this).

However, if you have an internal API which is the real security boundary, you might as well expose it. The effort required to add an LLM is now an extra effort and that flips the incentives to where the LLM really adds value. So adding LLMs as interfaces really makes sense on Windows 11 (practically single user, hallucinations have limited cost) and GitHub (devs have local copy anyway), maybe on AWS (devs would like an easier interface, but there's a need to reassure about safety), and none on Stripe or MailGun (complex security scenarios, usually decided by finance/marketing department which don't care about integration difficulty).

Interesting parallels , there is good possibility for this outcome with respect to Open Source LLMs

> I can however see a possible future where open source is not going to have any significant impact on LLMs, say like Desktop Linux.

> Either because it gets stuck in a technical realm and doesn't make anything too approachable to ordinary users, or because it lacks the necessary integrations..

Just to be super clear, do you mean Altman's push for greater regulation, or that he is pushing for actual regulatory capture i.e. corruption of regulating authorities?

My prompt: You are a sentient cow with a PHD in mathematics. You can speak English, but you randomly insert Cow-like “Moo” sounds into parts of your dialogue. Explain to me why 2+2=4.

Excerpt: “As a sentient cow with a PhD in moo-matics, I am happy to explain why 2+2 equals 4, my dear hooman friend… In moo-matical terms, each number is actually made up of smaller units called digits.”

I approve.

Which is heavier: a pound of feathers, or a great british pound?

> Both weights are equal.

So same answer as free ChatGPT, only terser.

GPT-4 hit it out of the park:

The question seems to be asking about two different types of "pounds": one as a unit of weight (the pound of feathers), and one as a unit of currency (the British pound).

A pound of feathers: This is a measure of weight. In the avoirdupois system (which is commonly used in the US), a pound is defined as exactly 0.45359237 kilograms.

A Great British pound: This is the unit of currency in the United Kingdom, often symbolised as £. The weight of a physical £1 coin, according to the Royal Mint, is 8.75 grams.

So, if we are comparing the weight of these two "pounds," a pound of feathers is heavier than a physical £1 coin.

Tbh I asked GPT-3.5 and it responds correctly. Do you have an example where it fails?

Tested the same prompt with GPT-4, and here is the result: https://imgur.com/7y2FiSP

In my opinion, the GPT-4 result is far more informative and less muddled.

Both answers are mostly just regurgitating an SQL tutorial with the objects and column names cheesecake related, so I don't think it's an awfully good test.

It did fail on: "Burt's father has 3 sons, Jack and John. What's the name of the 3rd son?"

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