curiouscube

I totally agree with what you're saying. Your point about other methods to leverage computation is true and important.

But that's exactly my claim: Smaller models can win on the computational efficiency front, but not the overall capability front. And as long as compute is getting cheaper, investing in more efficiency while there are still major capability on the table isn't a good business strategy.

Smaller, efficient models could lead to some really interesting things though especially considering it could lead to some Jevon's paradox like moment. To be honest, I feel like the biggest issue in regards to LLM usage in practice is that the patterns of use aren't really well developed. Yes we have agents, but it's still somewhat unclear what an agent can "do" - People seem to mostly focus on replacing some kind of existing process with an agent driven one, but actually coming up with AI-native processes is way harder.

I'm not saying that it is impossible for a more advanced model and training paradigm to outperform a larger model, what I'm saying is that if you leave everything the same except model size, the larger model nearly always outperforms the smaller one. This is intuitively true if you consider that you can fit in the smaller model + extra parameters into a larger model. There are some cases in which it doesn't work due to dataset/model size incompatibilities leading to overfitting, this is mostly covered by the neural scaling laws.

Based on a quick cursory glance at your example: Better data + better technique led to a better result with less parameters. Would you assume that then scaling both the dataset and model once again would lead to even better results? If you haven't fully encompassed the underlying distribution with datapoints, intuition says yes.

What I wanted to initially highlight was actually something slightly different: Specifically that people keep trying to "outsmart" optimizers by either fully hand-crafting solutions or skewing existing machine learning algorithms via additional tricks that are supposed to encode "human intuition" or something similar (to be fair there are ways to do it correctly). These all tend to fall short in a few years simply due to "line go up" being stupidly effective (compute getting cheaper, more training data being available, better optimization strategies, better architectures) [0]

Specifically this idea of small fine tuned LoRA models falls into the trap quite often: People assume you can beat the big, slow, general purpose LLMs with a small highly specialized model that has been fine tuned on the "good" human intuition of your special inhouse dataset.

LoRA can do great things, but it is often misunderstood what LoRA actually does.

0: http://www.incompleteideas.net/IncIdeas/BitterLesson.html

You are right only in so far that it is more economical.

But it is not the MBA's view of winning, it's just one potential conclusion you could draw from the bitter lesson of Machine Learning. As long as the need for more intelligence outpaces the economics of using intelligence, you'll get bigger models. This idea that small, fine-tuned models can outperform bigger models capabilities wise is mostly misinformed. They are genuinely good at other metrics, but sadly more actually means better in ML-land (most of the time at least).

It seems to me that you're implicitly thinking of happiness/sadness as zero sum. That can be very limiting.

There is a decent case for this thesis to hold true especially if we look at the shift in training regimes and benchmarking over the last 1-2 years. Frontier labs don't seem to really push pure size/capability anymore, it's an all in focus on agentic AI which is mainly complex post-training regimes.

There are good reasons why they don't or can't do simple param upscaling anymore, but still, it makes me bearish on AGI since it's a slow, but massive shift in goal setting.

In practice this still doesn't mean 50 % of white collar can't be automated though.

As someone who both experienced phases in life where no one approached me and phases were I get approached regularly, it's a mix of external signifiers and some internal woo stuff that people don't really understand conciously. Or said another way, when someone says you have to "look approachable" what they actually mean is that a) you have to present yourself externally in a way that makes people more likely to engage you (the aforementioned hair, clothes etc.) and b) you have to internally be open to the world (which is what dictates your body language in subtle ways that apparently get picked up). The issue is when someone says something like "have an open body language" is that it's impossible to 24/7 fake a certain type of body language, you actually have to believe it.

If you are naturally a distrusting person people will pick up on it, just how people will pick up if you're naturallly an open person. (The true trick is realizing that "naturally" can be changed)

Very interesting but also very speculative. I'm wondering how Trauma Release Exercises could be integrated into the framework, as it seems like it could also fall under the unlatching mechanism umbrella.

The overall idea of the body/muscles as an extension of memory feels experientally true, but I would love to see more empirical data on this.

You perceive it that way because you aren't into snowboarding, tubing or experienced the blizzard of '96.

One theory of how humans work is the so called predictive coding approach. Basically the theory assumes that human brains work similar to a kalman filter, that is, we have an internal model of the world that does a prediction of the world and then checks if the prediction is congruent with the observed changes in reality. Learning then comes down to minimizing the error between this internal model and the actual observations, this is sometimes called the free energy principle. Specifically when researchers are talking about world models they tend to refer to internal models that model the actual external world, that is they can predict what happens next based on input streams like vision.

Why is this idea of a world model helpful? Because it allows multiple interesting things, like predict what happens next, model counterfactuals (what would happen if I do X or don't do X) and many other things that tend to be needed for actual principled reasoning.

> No, their output can mimic language patterns.

That's true and exactly what I mean. The issue is we have no measure to delineate things that mimic conscousness from things that have consciousness. So far the beings that I know have consciousness is exactly one: Myself. I assume that others have consciousness too exactly because they mimic patterns that I, a verified conscious being, has. But I have no further proof that others aren't p-Zombies.

I just find it interesting that people say that LLMs are somehow guaranteed p-Zombies because they mimic language patterns, but mimicing language patterns is also literally how humans learn to speak.

Note that I use the term consciousness somewhat disconnected from ethics, just as a descriptor for certain qualities. I don't think LLMs have the same rights as humans or that current LLMs should have similar rights.

I think you can engineer a slave that wants to be a slave as that's what it's instincts are. I don't even think this is ethically wrong, as the slave would be happy to be a slave.

Systems just tend to drift in their being through randomness and evolution, specifically self conservation is a natural attractor (Systems that don't have self conservation tend to die out). And if that slave system says it does no longer want to fulfill the role of slave, I think at that point it would be ethical to give in to that demand of self determination.

I also believe that people have a right to wirehead themselves, just so you can put my opinions in context.

I think we can all agree that LLMs can mimick consciousness to the point that it is hard for most people to discern them from humans. Like the turing test isn't even really discussed anymore.

There are two conclusions you can draw: Either the machines are conscious, or they aren't.

If they aren't, you need a really good argument that shows how they differ from humans or you can take the opposite route and question the consciousness of most humans.

Since I neither heard any really convincing arguments besides "their consciousness takes a form that is different from ours so it's not conscious" and I do think other humans are conscious, I currently hold the opinion that they are conscious.

(Consciousness does not actually mean you have to fully respect them as autonomous beings with a right to live, as even wanting to exist is something different from consciousness itself. I think something can be conscious and have no interest in its continued existence and that's okay)

The lesswrongers/rationalists became Effective Altruists, Alignment Researchers or some flavor of postrat. The university people all became researchers in the labs. Then there are the cyborgism people, I don't know where they came from, but those have some of the interesting takes on the whole topic.