Also open sourced a bunch of infra to go with it.
Anyone who claims open source and open weights models are "decel" needs to get their head checked
https://github.com/MoonshotAI/MoonEP
Anyone who claims open source and open weights models are "decel" needs to get their head checked
https://github.com/MoonshotAI/MoonEP
Decel:
- Potentially reduces investor appetite for funding big labs.
- More risk of powerful AI getting in bad hands -> more regulation.
Accel:
- More competition so big labs can't rest on laurels.
- More research in open, so all labs can accrete advancements faster.
I feel like open-source = acceleration has a much more clear argument. (and how bad would deceleration be in any case?)
The problem with the decel/accel rhetoric is that it lacks nuance.
I think you meant less research and experiments in big labs because they don't get all the AI money.
Training is expensive, but they also have more than 10 000 of employees combined and they cost a lot of money.
But ultimately these were ideas floating around in the air, if one group hadn't done the experiment, someone else would have.
Regardless I think it's impossible to believe that most LLM research was done by closed labs that don't publish, especially Anthropic (who missed out on and copied two of the largest pieces of important research of the last several years), and that none was done by open labs like DeepSeek, and that the open labs are just copycats. It's quite clear that isn't the case.
Kimi K3 is plausibly a lot less dangerous than a totally jailbroken ChatGPT/Gemini/Claude Sonnet (let alone Opus or Fable!) and it's quite deeply weird how no one seems to be calling for those models to be banned or restrained by further regulation. Why the double standard against the less concerning (but more efficient!) open weight models?
Is there a world where open source models end up at the frontier, or do you think there are structural/first-principles reasons why this won't happen?
Put another way, if you want to slow things down, put it behind a paywall, tag ideas ans “intellectual property” (meaning you’re the only one who can use it) and get the lawyers involved (injecting our slow legal system).
None of the above is a judgement call on whether development should be accelerated.
Much better would be to just use good ol' "please":
> I know it's a hard ask on this site, but please start parsing content and not tone
I, for example, dislike reading comments complaining the submission (or another comment) is LLM generated. Focus on the content, not the style.
I'm not going to have my way, and nor shall you.
It's why people can advocate for ethnic cleansing here, and that's fine as long as they word it correctly, but if someone calls them an asshole about it, they're flagged.
Really common in rationalist circles from my experience as well because they believe that true statements aren't always normative, and that, since their arguments are true because they're rational, their statements aren't necessarily normative. Begging the question, of course, but I see that in situations like Scott Alexander's defense of "human biodiversity" theories (the whole HBD moniker is itself an example of everything I'm talking about condensed into two words).
Depends on which subreddit and which flavor of groupthink. The behavior you say is upvoted is one I often see downvoted to oblivion on Reddit.
> but if someone calls them an asshole about it, they're flagged.
That's because name calling is against HN guidelines.
Just an aside since it's not clear: I think the asshole is the one using labels like "decel" (or even "MAGA" unless the person self describes). It's irrelevant if I agree with the rest of the comment.
It's OK to call people out for name calling while still agreeing with the rest. It's problematic to require one acknowledge the quality of the rest of the comment when calling them out on their name calling.
Put in a less twisted manner: If it's OK to address his comment sans the "decel", it should also be OK to address his use of "decel" without discussing the rest of the comment.
Really doubt that but feel free to give an example.
>If it's OK to address his comment sans the "decel", it should also be OK to address his use of "decel" without discussing the rest of the comment
I think the only acceptable response is to answer his argument if you can read it. If you can't handle a heated commenter and insist on making his anger the discussion, then you'd have been better off not answering it. So just recognize this and ignore.
When indulging in a discussion with others, one has to realize that the universe of what people consider acceptable responses isn't limited to yours.
Yes, it's clear that's what you think. It's also the whole point of discussion here. Merely repeating it isn't supporting your perspective.
> If you can't handle a heated commenter and insist on making his anger the discussion, then you'd have been better off not answering it. So just recognize this and ignore.
He is the one who brought the anger into the discussion, and thus it became part of the discussion. Not addressing it is a symptom of not handling it.
Do understand: Until about a decade ago, I thought like you. It caused me all kinds of headaches in the real world, and so I decided to study what an effective conversation. I took a multi-day course, as well as read several books on it.
All, without exception, point out that failure to address the emotional content is a bad idea and one of the reasons conversations become ineffective.
I am not sure how you could walk away from this comment thread and still think that's the case. Minus this digression which does argue about central concepts, the discussion about the original point is pretty close to the bottom of Graham’s Hierarchy of Disagreement.
I guess maybe a better question would be why you think pivoting a conversation about how open weight models are changing the industry into a conversation about whether a commenter was rude is a positive outcome? I would understand either ignoring him or answering his content, but I'm trying to understand how getting into back and forth about tone (back in my day we call that a flame war) is a good use of one's time.
>Do understand: Until about a decade ago, I thought like you. It caused me all kinds of headaches in the real world
I've found it to be quite the opposite! Not rankling at someone's emotionally charged language in real life has been incredibly useful, both for interactions with friends and strangers, but also with family. The people I know who are miserable about visiting family at Thanksgiving feel that way because they are unable to stop themselves from getting into conflicts because their relatives made some emotionally charged statement. There are also plenty of situations where people come in hot and angry, and you can turn it around by simply letting their tone slide and trying to help them. Happened all the time when I tutored compsci students. They'd come in angry and surly because they'd been beating their head against a problem or concept, and a little patience and forbearance went a long way towards improving their attitude.
the only reason other labs can catch up is because the frontier labs can be distilled, and they siphon a % of the labs' revenue to reinvest into the next iteration
full accel would mean nationalizing the big 2 labs and locking in manhattan project style until RSI
(Edit: some great counterpoints in the replies. my view has definitely been changed!)
There's no chance K3 is a distill of Fable, it came out way too soon after the limited fable release to be feasbile.
If you look at all of the top ML conferences, chinese labs contribute way more to advances in ML than "Open"AI and Anthropic: https://www.reddit.com/r/TheMachineGod/comments/1pi4q7f/pape...
This K3 release just helped every other lab on the planet stay in the race by making it possible for them to build on top of it, placing them at the frontier starting line instead of having to spend billions of their own dollars and risking it all to attempt to catch up.
The open source contributions I linked to above will move the whole field forward and reduce the costs of training and inference for everyone.
Open science compounds on it self, every new advancement pushes the field forwards and opens up new grounds for future improvements.
It is impossible for a single closed lab to consistently stay ahead of the rest of the field, especially in a huge growing research area like machine learning. The only only advantage the big labs have is money, but the naive scaling game is not sustainable long term when you have to pay 10-100x more then the fast followers and we start getting more and more open models or use case specific models that can handle 90% of high volume use cases.
Research is a high variance, low expected value activity, meaning that the few large concentrated labs have to be conservative with their bets and double down on proven things when scaling up. The rest of the field is like a diversified portfolio, with thousands of players making smaller riskier bets that only require a few of them to succeed (like K3 did here, and DeepSeek a year ago)
EDIT: also if you look at most of the work from OpenAI, it's mostly taking existing promising open research work and scaling it up. (except for things like CLIP and etc from Alec Radford)
And years down the line, lots of other research labs used my code and cited my paper.
We had a bunch of things that we never published that ended up being major research findings years later at top conferences.
Further, you can just read the papers released alongside most open models. Plenty of hugely influential research results published that drive the frontier forward. It's not like these models are just existing architectures downloaded from Huggingface and trained on frontier lab APIs.
Frontier models would have to do something extraordinary or unique, or unreplicatable, because clearly there is no moat, and US companies are sitting on huge nvidia valuations and get surprised when competitors beat them.