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Models Are Getting Dumber on Purpose

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Re: Models Are Getting Dumber on Purpose

#181

Post by flaburgan »

jimmaswell wrote: post #58
kennywinker wrote: post #4

Ideally what I'd like to see is pluggable knowledge bases. So if I'm e.g. coding a SwiftUI app for navigation, I'd take 9B of basic coding and reasoning, add 10B of swift/swiftUI, add 5B of GIS/geography knowledge and another 5B of frontend app design knowledge. My model doesn't need to know a single line of python. Then when I want to research electronics components, I grab a 15B model of agentic research techniques…

An LLM works better the more disparate world knowledge it has, even if it's not immediately obvious why it would be relevant. The model finds a structure to the problem you give it in a largely language-agnostic way that benefits from training on every language (these things are direct descendants of Google Translate), and even non-programming knowledge - the structure of your task might resemble an ancient Chinese p…

>There are "experts" which do divide parts of the model that are found to activate together for specific tasks, so they can be processed in parallel to join the result at the end, but it's nowhere near the granularity of a SwiftUI expert and a python expert. The difference in those things is so trivial from an abstract point of view that it would make no sense. They would be 99% the same.

I think you're missing the point of the article though, which suggests not to mix reasoning capacities with actual knowledge. Sure working with Swift and Python is basically the same, it's programming, with the same concepts etc. Much closer than the ability to drive a car. But the point is, the methods, libs etc are all different and things are changing each time a new version of the language is released. Like you don't need to relearn how to drive a car if you go to the UK but you have to known the wheel is on the right. Knowledge shouldn't be stored in the weights.

Re: Models Are Getting Dumber on Purpose

#182

Post by c7b »

kennywinker wrote: post #129
c7b wrote: post #127

Earlier quoted context omitted.

At a more technical level, what do you suggest? Training a small LLM on Python code exclusively? And then one on general CS/algorithms, which you'll also need? I don't think the current transformer architectures would compose as you suggest.

With what I know about how LLMs work now, I guess I am suggesting more specific variants. Qwen3.8 has a 2.4T version and a 27B version. I understand that to mean that they are the same architecture, just one version has a massive training set and the other has a very small subset. So, it seems very possible that variants of 27B could be generated that tune it for specific things by selecting different training data f…

> I understand that to mean that they are the same architecture, just one version has a massive training set and the other has a very small subset.

No. It means that the one model has 2.4 trillion parameters while the other has only 27 billion. I don't know the details about their architecture or training, but presumably they used the same or similar training sets for both and a conceptually similar architecture, scaled down. I'd guess they also have some techniques to re-use some of the work done for the big model for the smaller versions (if anyone knows more about this I'd be interested). The architectures cannot be identical by definition because then the parameter count would be the same. Subsetting the data to such narrow fields as you describe could risk losing some edge, there are a lot of emergent capabilities in those models and I don't think that emergence is fully understood yet. There are subject-specific models, but for far broader subject areas than you suggested, like coding or math or prose.

I'm sure composability is possible in principle, I'm just sceptical that it'll be a good long-term solution, for my originally stated reason. It's basically just The Bitter Lesson again, we may gain some short-lived edge by putting more domain knowledge into the algorithm, but ultimately (these days often: surprisingly quickly) it'll be outgunned by something that just leverages raw computation better.

Re: Models Are Getting Dumber on Purpose

#183

Post by cyanydeez »

kennywinker wrote: post #104
cyanydeez wrote: post #31

Earlier quoted context omitted.

the technicals of search did not get worse. the economics of the search provider did. If government wasn't stuck in the 50s mindset, search provider would have been nationalized just like a utility.

I think the techinicals of search did get worse - the ai sloppification of the internet made the needle harder to find in the haystack.

that's not the techicals, thats the content. That could be overcome via robust "Library of Congress" like filtering and evaluating.

We don't really have that much more good information now than 30 years ago. We had an explosion of garbage that google saw as an oppotunity to sell us on, rather than filter out.

Google saw it as their job to keep us on search longer, so the SEO garbage filling the search feed benefited them.

Re: Models Are Getting Dumber on Purpose

#184

Post by SCHiM »

jiggawatts wrote: post #160
SCHiM wrote: post #137

Earlier quoted context omitted.

>I get it. You don't feel ownership over someone else's AI. You don't feel involved, you don't feel like you have agency. You don't _have_ ownership of someone else's ai, and that comes with real risks. Security risks, privacy risks, business risk. They might rug pull you, they might charge you more, or like atrophic, silently corrupt the answers, or code... The labs are happy to jump on any emergent capability the s…

It doesn't have to be "externally hosted, proprietary AI model"! The argument is against "self-assembled small AI pieces" versus frontier monolithic models. A) You can always self-host something like Kimi, DeepSeek, or GLM. B) Just because you use a specific proprietary AI for programming doesn't actually bind you to that provider in any meaningful way. The authored code remains even if you stop paying them! Of cours…

Anthropic*

I think I agree with the bitter lesson, but I wish I weren't :)

> A) You can always self-host something like Kimi, DeepSeek, or GLM.

I mean, one could rent-a-box for, like, 10$$ per hour? Agentic loop development gets really expensive at scale with larger models, like, if you want to A/B test two tool schemas to see which works better, and you run 100 benchmarks...

But why are you so convinced the bitter lesson is true, and it's not just a temporary lead? Proper agent loops and RLVR are like, 3 years old at this point? At some point, right, the compute can't scale it out further? And at _that_ point the lead position might go back to: highest compute + smartest designed smarts.

Against my somewhat better judgement I'm currently "assembling small AI pieces" :(, for lack of access to unrestricted models for offensive security work and, ehh, funds. It's _okay_ so far, I'm running private benchmarks and look at the trajectories. To be perfectly honest, qwen is _really_ doing well, finishing quite complex chains without a lot of smarts in the prompt. Just "Go pwn {server}, use these {tools}".

But, there are some smarts embedded in those tools. Helpful errors, retries, benchmarked/handy representation. Strict validation of what the model tries to do, etc.

> The authored code remains even if you stop paying them!

This is true, but my point is that they will outcompete you if you happen to stumble on something that actually makes good money using LLMs and becomes popular. Obviously, if you don't then they won't.

> Of course, if you use AI as an active component in some sort of service, then the EULA, rug-pulls, etc... suddenly start to matter. That's a different story.

That's the plan hehe

Re: Models Are Getting Dumber on Purpose

#185

Post by 3836293648 »

lowsong wrote: post #72
gagan2020 wrote: post #20

Intelligence vs Knowledge LLMs work is being intelligent not having knowledge of everything is ok. But, they have to be intelligent enough (with some degree of knowledge) that where to find the information (search tools or any other tools for that matter)

LLMs are not intelligent. Please don't anthropomorphize.

Intelligence is still not well defined, and by more solid attempt LLMs absolutely are.

Intelligence, Knowledge and Sentience are three very separate concepts.

Re: Models Are Getting Dumber on Purpose

#186

Post by xtiansimon »

kennywinker wrote: post #4

Ideally what I'd like to see is pluggable knowledge bases. So if I'm e.g. coding a SwiftUI app for navigation, I'd take 9B of basic coding and reasoning, add 10B of swift/swiftUI, add 5B of GIS/geography knowledge and another 5B of frontend app design knowledge. My model doesn't need to know a single line of python. Then when I want to research electronics components, I grab a 15B model of agentic research techniques…

I'd [subscribe to] 9B of basic coding and reasoning, add [subscription for] 10B of swift/swiftUI, add [monthly lease of] 5B of GIS/geography knowledge and another [monthly obligation] 5B of frontend app design knowledge. LOL

Right now we have the Star Trek computer. A polymath of knowledge. But I’m sure you’re right. It’s the trend of all knowing to move from the bazaars to the temples, and back again.

Re: Models Are Getting Dumber on Purpose

#187

Post by kentonv »

5-0 wrote: post #173
kentonv wrote: post #99

Earlier quoted context omitted.

Tangent: This is often true of humans as well. We often make a decision based on a gut feeling, and then backfill a logical reason supporting our feeling, without even realizing we're doing it -- rationalization.

Yes, humans in general. Scientists, perhaps less so. Is it unreasonable to hold LLMs to a higher standard?

> Scientists, perhaps less so.

I disagree. In fact, I find people who think of themselves as highly rational seem to be particularly prone to rationalization. Because being rational is integral to their whole identity, they are much more eager to accept their own rationalizations as sound and resist admitting that they are guided by instinct.

Re: Models Are Getting Dumber on Purpose

#188

Post by jhanschoo »

znnajdla wrote: post #59
kennywinker wrote: post #4

Ideally what I'd like to see is pluggable knowledge bases. So if I'm e.g. coding a SwiftUI app for navigation, I'd take 9B of basic coding and reasoning, add 10B of swift/swiftUI, add 5B of GIS/geography knowledge and another 5B of frontend app design knowledge. My model doesn't need to know a single line of python. Then when I want to research electronics components, I grab a 15B model of agentic research techniques…

This is a fundamental misunderstanding of how LLMs work. You can’t really specialize a model. You specialize the harness. A well-trained general purpose LLM doesn’t need examples in its training data, it can write good code in a new language you invented yesterday with just a spec definition. And it will perform better than a small model trained on lots of examples of your invented language. The reason is because of…

> The reason is because of the “universal geometry of embeddings”, i.e all human languages have the same underlying pattern structure, so any model that is very good in any language is good in all languages.

I think this claim is acceptable in context, but taken alone, this needs to be qualified. Some aspects of language are universal, like abstract information structure and other pragmatics, but no model is going to speak rural Khmer dialects well anytime soon because not enough of it has been digitized, fundamentally speaking, hence qualifying what it means to "be good at all languages".

Re: Models Are Getting Dumber on Purpose

#189

Post by malfist »

kentonv wrote: post #149
malfist wrote: post #93

Earlier quoted context omitted.

Seriously, anybody with a passing knowledge of LLMs knows thats not how they function. You can't encode logic in them because that's not how they work. It's a statistical model with useful emergent properties. It doesn't think, it doesn't reason, it isn't aware of facts or the rules of logic.

> It's a statistical model with useful emergent properties. It doesn't think, it doesn't reason, it isn't aware of facts or the rules of logic. What makes you so sure your own brain doesn't work the same way?

It takes me a long time to do matrix multiplication

Re: Models Are Getting Dumber on Purpose

#190

Post by arational »

Gecko4072 wrote: post #3

With all the focus on coding and agentic use, I wonder if the rest of the world will notice or care? Most AI use is not for coding or harnesses contrary to what this website thinks. Maybe this is how American companies stay winning. For example I prefer Kimi K2.6 1T parameter to Flash V4 0731 230B parameter, even if it is less intelligent.

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