In the case of LLMs like ChatGPT, while we understand the architecture, and how it works at that level (attention via key matching, etc), what is missing is how the architecture is actually being utilized by the trained model. For example, it turns out that consecutive pairs of attention heads sometimes learn to coordinate and can look words (tokens) up in the context and copy them to the output - this isn't something you could really have predicted just by looking at the architecture. The companies like Anthropic developing these have discovered a few such insights into how they are actually working, but not too many!
Yes, we don't really understand where emergent capabilities are coming from, at least not to extent of being able to predict them ahead of time ("if we feed it this amount of data, of this type, it'll learn to do X"). New emergent capabilities arise, from time to time, as models are scaled up, but no one can predict exactly what their next-gen model is going to be capable of.
While finite precision, finite width transformers aren't TC, I don't see why the same property of the game of life, where one cannot predict the end state from the starting state wouldn't hold.
As we know transformers are at least as powerful as TC^0 which contains AC^0, which is as powerful as first order logic, it is undecidable and thus may be similar to HALT, were we will never be able to accurately predict when emergence happens so approximation may be the best we do unless there are constraints through something like the parallelism tradeoff that allows for it.
If you consider PCP[O(log n),O(1)] = NP, or that only O(log n) bits are required for NP, the results of this paper seems more plausible.
https://arxiv.org/abs/2304.15004
I have yet to see any peer review that makes that continuous view invalid.
As you pointed out, we understand the underlying systems, but I think we should be surprised if someone does find a good approximation reduction.
But in my experience that also indicates an extreme limit in what can be modeled.
Then again all FFNs are effectively DAGs and I.I.D. does force a gaussian distribution of inputs.
But unless you are learning something that is Markovian and Ergotic undecidablity seems like a high probability.
The problem with trying to anticipate the capabilities of a new model/training-set is that we don't even know what the new computer itself will be capable of, or how it will now interpret the program.
The way I'd tend to view it is that an existing trained model has some set of capabilities which reflect what can be done by combining the set of data-patterns/data-manipulations ("thought patterns" ?) that it has learnt. If we scale up the model and add more training data (perhaps some of a different type than has been used before), then there are two unknowns:
1) What new data-patterns/data-manipulations will it be able to learn ?
2) What new capabilities will become possible by using these new patterns/manipulations in combination with what it had before ?
Maybe it's a bit like having a construction set of various parts, and considering what new types of things could be built with if it if we added some new parts (e.g. a beam, or gear, or wheel), except we are trying to predict this without even knowing what those new parts will be.
Soft attention, applying a probabilistic curve across multiple neurons is why I think it is related.
The Problem with Probabilistic DAG Automata for Semantic Graphs
The “emergent” properties are going to be impacted by randomness of your starting point as well as ordering of your training.
You can see this out of videos where people train a NN to do something multiple times and each time, the NN picks up on something slightly different. Slight variances in what is fed as inputs during training can cause actually high variation in what is picked up on.
I’m getting decently annoyed with HNs constant pretending that this is all just “magic”.
Nobody is saying this is magic - it's just something that is (with our current level of knowledge) impossible to predict will happen. If you scale a model up, and/or give it more training data, then it'll usually get better at what it could already do, but it may also develop some new (emergent) capabilities that no-one had anticipated.
And that’s still beside the point that the properties that emerge can greatly differ just by changing the ordering of your training.
Again, we see this on NNs training to play games. The strategies that emerge are completely unexpected, and when you train a NN multiple times, often differ greatly, or slightly.
Here's an excerpt from the article:
"The researchers found that a formula used in statistical analysis provides a streamlined mathematical description of how neural networks, such as GPT-2, a precursor to ChatGPT, learn relevant patterns in data, known as features. This formula also explains how neural networks use these relevant patterns to make predictions."
The trite answer to "HOW do NN's learn?" is obviously gradient descent - error minimization, with the features being learnt being those that best support error minimization by the higher layers, effectively learning some basis set of features that can be composed into more complex higher level patterns.
The more interesting question perhaps is WHAT (not HOW) do NN's learn, and there doesn't seem to be any single answer to that - it depends on the network architecture. What a CNN learns is not the same as what an LLM such GPT-2 (which they claim to address) learns.
What an LLM learns is tied to the question of how does a trained LLM actually work, and this is very much a research question - the field of mechanistic interpretability (induction head circuits, and so forth). I guess you could combine this with the question of HOW does an LLM learn if you are looking for a higher level transformer-specific answer, and not just the generic error minimization answer: how does a transformer learn those circuits?
Other types of NN may be better understood, but anyone claiming to fully know how an LLM works is deluding themselves. Companies like Anthropic don't themselves fully know, and in fact have mechanistic interpretability as a potential roadblock to further scaling since they have committed to scaling safely, and want to understand the inner workings of the model in order both to control it and provide guarantees that a larger model has not learnt to do anything dangerous.