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Comment by lordnacho | Hacker News Reader

What seems a bit miraculous to me is, how did the researchers who put us on this path come to suspect that you could just throw more data and more parameters at the problem? If the emergent behavior doesn't appear for moderate sized models, how do you convince management to let you build a huge model?

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This is perhaps why it took us this long to get to LLMs, the underlying math and ideas were (mostly) there, and even if the Transformer as an architecture wasn't ready yet, it wouldn't surprise me if throwing sufficient data/compute at a worse architecture wouldn't also produce comparable emergent behavior

There needed to be someone willing to try going big at an organization with sufficient idle compute/data just sitting there, not a surprise it first happened at Google.

But we got here step by step, as other interesting use cases came up by using somewhat less compute. Image recognition, early forms of image generation, AlphaGo, AlphaZero for chess. All earlier forms of deep neural networks that are much more reasonable than training a top of the line LLM today, but seemed expensive at the time. And ultimately a lot of this also comes from the hardware advancements and the math advancements. If you took classes neural networks in the 1990s, you'd notice that they mostly talked about 1 or 2 hidden layers, and not all that much focus on the math to train large networks, precisely because of how daunting the compute costs were for anything that wasn't a toy. But then came video card hardware, and improvements to use it to do gradient descent, making going past silly 3 layer networks somewhat reasonable.

Every bet makes perfect sense after you consider how promising the previous one looked, and how much cheaper the compute was getting. Imagine being tasked to train an LLM in 1995: All the architectural knowledge we have today and a state-level mandate would not have gotten all that far. Just the amount of fast memory that we put to bear wouldn't have been viable until relatively recently.

> and how much cheaper the compute was getting.

I remember back in the 90s how scientists/data analysts were saying that we'd need exaflop scale systems to tackle certain problems. I remember thinking how foreign that number was when small systems were running maybe tens of megaFLOPS. Now we have systems starting to zettaflops (FP8 so not exact comparison).

You might appreciate this article: https://www.quantamagazine.org/when-chatgpt-broke-an-entire-...

They didn't. Not LLM people specifically. Google a long time ago figured out that you get far better results on a very wide range of problems just by going bigger. (Which then must have become frustrating for some people because most of the effort seems to have gone to scaling? See for example as-opposed-to-symbolic.)

While GPT-2 didn't show emergent abilities, it did show improved accuracy on various tasks with respect to GPT-1. At that point, it was clear that scaling made sense.

In other words, no one expected GPT-3 to suddenly start solving tasks without training as it did, but it was expected to be useful as an incremental improvement to what GPT-2 did. At the time, GPT-2 was seeing practical use, mainly in text generation from some initial words - at that point the big scare was about massive generation of fake news - and also as a model that one could fine-tune for specific tasks. It made sense to train a larger model that would do all that better. The rest is history.

I don't think model sizes increased suddenly, there might not be emergent properties for certain tasks at smaller scales but there was improvement at slower rate for sure. Competition to improve that metric albeit at lower pace led to slow increase in model sizes and by chance led to emergence the way its defined in paper?

There’s that Sinclair quote:

It Is Difficult to Get a Man to Understand Something When His Salary Depends Upon His Not Understanding It

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