Comment by johnsmith1840

Comment by johnsmith1840 3 days ago

We have no idea how to do continual learning.

Many people here are right, compute, collapse, forgetting whatever.

The only "real" way to do this would be: 1. Train a model 2. New data 3. Retrain the model in full + new data 4. Repeat 5. You still have no garuntee on the "time" aspect though.

But CL as a field basically has zero answers on how to do this in a true sense. It's crazy hard because the "solutions" are hypocritical in many ways.

We need to expand the model's representation space while keeping the previous representation space nearly the same?

Basically, you need to modify it without changing it.

Most annoying is that even the smallest of natural brains do this easily. I have a long winded theory but basically it boils down to AI likely needs to "sleep" or rest somehow.

mackenziebowes 3 days ago

The cool thing about AI that I'm seeing as an outsider/non-academic, is that it's relatively cheap to clone. Sleeping/resting could be done by a "clone" and benefits could be distributed on a rolling schedule, right?

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johnsmith1840 3 days ago

One clone takes a nap while the other works is pretty cool.
But the clone couldn't run without sleeping? So that's more of a teammate than a clone.
1 works while the other sleeps and then swap.
If this method ever worked our current alignment methods get chucked out the window those would be two completely different AI.

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- mackenziebowes 3 days ago
  
  I can't be certain, I'm not at all an AI engineer or math guy, but I think at the "wake up" point you equalize instances. Like during 'sleep' some list of functions/operations `m` are applied to model weights `n` producing a new model, `n + 1`. Wouldn't you just clone `n + 1`, send it to work, and start a new training run `m + 1` to make `n + 2`?
  
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  notpushkin 3 days ago
  
  This was my first idea as well. Keep training continuously and redeploy clones after each cycle. From a layman perspective this seems reasonable :thinking:
  
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  maleldil 2 days ago
  
  You can't realistically keep training the same model forever, or it will start forgetting things it knew before. The proper name for this is "catastrophic forgetting".
  
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khalic 3 days ago

You should look into LoRA, it’s a partial retraining method, doesn’t require nearly as much as retraining the whole model. It’s different from what this paper is suggesting. The self improvements in this paper even sets the rules for the improvements, basically creating new data out of what it has.

LoRA paper: https://arxiv.org/abs/2106.09685

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zelphirkalt 2 days ago

This only seems to be the case with the current crop of models. "Online learning" is a term for having models deployed and keeping them learning and it has been around for more basic models for a long time.

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johnsmith1840 2 days ago

Not sure how much you've gotten into CL but online learning while similar is not the same.
Online learning is more akin to RL in that it's a structured and boxed enviroment. Step outside of that box or the box changes too much and you collapse.
CL is much more similar to meta learning. The concepts are more about learning NEW content while keeping previous the same.
CL is a completely open problem with all model types. EWC is amoung the better attempts (and a favorite of mine) at solving it with big limitations.

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Inviz 2 days ago

Evolving prompts seems to fit the "modify without changing" bill, does it?

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johnsmith1840 2 days ago

Yes but it's similar to RNNs or energy models.
They try to keep a single continuous "state" that always updates.
It's more about going "farther" than something more akin to "go forever" that CL promises.
Scaling laws are true in that infinite scale would 100% lead to AGI. But at the same time the problem with it is that you can't infinitely scale the computation per task.
RL solves this problem in general but it has a deep assumption of knowing the future. Step too far out of the box and it collapses.
The smallest natural brains handle unknown future states with a fixed computation budget per timestep which is truly incredible.

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Davidzheng 3 days ago

but natural brains sleep too, which I guess is your point. But actually is it even clear in human brains whether most of neural compute is evaluation vs training? maybe the brain is like for e.g. capable of running 20T model of compute and deploying like 2B model at given time and most of compute is training in background new models--I mean like you say we have no idea except for training from scratch, but if we are working much below capacity of compute we could actually actively train from scratch repeatedly (like the xAI cluster could probably train gpt4o size in a matter of hours)

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johnsmith1840 3 days ago

AGI likely a combination of these two papers + something new likely along the lines of distillation.

1. Preventing collapse -> model gets "full" https://arxiv.org/pdf/1612.00796

2. Forgetting causes better generalization https://arxiv.org/abs/2307.01163

3. Unknow paper that connects this - allow a "forgetting" model that improves generalization over time. - I tried for a long time to make this but it's a bit difficult

Fun implication is that if true this implies AGI will need "breaks" and likely need to consume non task content of high variety much like a person does.

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khalic 3 days ago

There is no sign that LLMs are capable of general reasoning, on the contrary, so hold your horses about that. We have proven they can do basic composition (as a developer, I see proof of this every time I generate some code with an assistant) which is amazing already, but we’re still far from anything like “general intelligence”.

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- johnsmith1840 3 days ago
  
  My argument is that we already have psuedo/static reasoners. CL will turn our non reasoners into reasoners.
  CL has been an open problem from the very beginnings of AI research with basically no solution. Its pervasiveness indicates a very deep misunderstanding on our knowledge of reasoning.
  
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zelphirkalt 2 days ago

That's really reaching way to far. We have no idea, whether that will lead to anything even close to AGI and it even seems more likely, that it will just run into the next hurdle.

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- johnsmith1840 2 days ago
  
  Totally possible!
  I just like talking about it. I will say that learning outside distribution content while keeping previous knowledge in a "useful" state is a capability that would absolutely supercharge ever AI method we currently have.
  It's atleast an honest atempt at a research direction other than "scale infinitely for everything" that we currently do.
  Just think about how natural brains do something incredible.
  1. They have fixed computation budgets per time step. 2. They continously learn entirely new tasks while still maintaining previous in a useful state.
  That's a capability I would very much like in my AI.
  Scaling laws are correct but they are also the reason we are nowhere near replacing humans.
  Take a simple job maybe admin work. Every timestep depends on the previous timestep. While not a complex job and an AI could do it for awhile but over time the compuation required to "look back" its memory and connect it for the next step grows near exponentially.
  RAG is another perfect example of this problem.
  I do deeply belive AGI will be solved by a kid with a whiteboard not a supercluster. CL is my best guess at what that means.
  Maybe it's a super RL or energy type method but I've never seen it.
  
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