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I always wondered if the specific dimensionality of the layers and tensors has a specific effect on the model. It's hard to explain, but higher dimensional spaces have weird topological properties, not all behave the same way and some things are perfectly doable in one set of dimensions while for others it just plain doesn't work (e.g. applying surgery on to turn a shape into another).
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?
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'm not a techie, so perhaps someone can help me understand this: AFAIK, no theoretical computer scientist predicted emergence in AI models. Doesn't that suggest that the field of theoretical computer science (or theoretical AI, if you will) is suspect? It's like Lord Kelvin saying that heavier-than-air flying machines are impossible a decade before the Wright brothers' first flight.
I'm not a techie, so perhaps someone can help me understand this: AFAIK, no theoretical computer scientist predicted emergence in AI models. Doesn't that suggest that the field of theoretical computer science (or theoretical AI, if you will) is suspect? It's like Lord Kelvin saying that heavier-than-air flying machines are impossible a decade before the Wright brothers' first flight.
Has emergent behavior ever been predicted prior to it being observed in other theoretical fields?
it doesn't seem that surprising to me.
Earlier quoted context omitted.
Emergence in the context of LLMs is really just us learning that "hey, you don't actually need intelligence to do , turns out it can be done using a good enough next token predictor. We're basically learning what intelligence isn't as we see some of the things these models can do. I always understood this to be the initial framing, e.g. in the Language Models are Few Shot Learners paper but then it got flipped around…
The challenge there would be showing that humans have this thing called intelligence. You yourself are just outputting ephemeral actions that rise out of your subconscious. We have no idea what that system feeding our output looks like (except it's some kind of organic neural net) and hence there isn't really a basis for discriminating what is and isn't intelligent besides "if it solves problems, it has some degree o…
If you want to understand how birds fly, the fact that planes also fly is near useless. While a few common aerodynamic principles apply, both types of flight are so different from each other that you do not learn very much about one from the other.
On the other hand, if your goal is just "humans moving through the air for extended distances", it doesn't matter at all that airplanes do not fly the way birds do.
And then, on the generated third hand, if you need the kind of tight quarters maneuverability that birds can do in forests and other tangled spaces, then the way our current airplanes fly is of little to no use at all, and you're going to need a very different sort of technology than the one used in current aircraft.
And on the accidentally generated fourth hand, if your goal is "moving very large mass over very long distance", the the mechanisms of bird flight are likely to be of little utility.
The fact that two different systems can be described in a similar way (e.g. "flying") doesn't by itself tell you that they are working in remotely the same way or capable of the same sorts of things.
> “The real question is how can we predict when a new LLM will achieve some new capability X. For example, X = “Write a short story that resonates with the social mood of the present time and is a runaway hit”
Framing a capability as something that is objectively measurable (“able to perform math on the 12th grade level”, “able to write a coherent, novel text without spelling/grammar mistakes”) makes sense within the context of what the author is trying to demonstrate.
But the social proof aspect (“is a runaway hit”) feels orthogonal to it? Things can be runaway hits for social factors independently of the capability they actually represent.
I remain skeptical of emergent properties in LLMs in the way that people have used that term. There was a belief 3-4 years ago that if you just make the models big enough, they magically acquire intelligence. But since then, we’ve seen that the models are actually still pretty limited by the training data: like other ML models, they interpolate well between the data they’ve been trained on, but they don’t generalize…
I remain skeptical of emergent properties in LLMs in the way that people have used that term. There was a belief 3-4 years ago that if you just make the models big enough, they magically acquire intelligence. But since then, we’ve seen that the models are actually still pretty limited by the training data: like other ML models, they interpolate well between the data they’ve been trained on, but they don’t generalize…