I think this, meaning some better and more sophisticated version of this, is a possible route to developing AI that can learn without changing its weights. It uses GPT-4 as a component of a system. You can tell it something, it will commit it to memory, and on later interactions with a different context it will recall what you previously told it and use that to form a new response.

I've never said what I have is worth a hundred million dollars. My contribution here is a few Python scripts and some prompts. I'm sure other people working on this have the same or better ideas.

In other words, you wrote some Python scripts and you want me to give you a thousand dollars for them? You really do think I'm a sucker.

??? I originally said that GPT-4 could be a component of a system that learns without updating model weights. You said that was unrealistic. I said I already had a prototype and offered to bet you that I do actually have this prototype. My reason for offering the bet is that I know you know that what I wrote is actually realistic, thus, you will refuse the bet.

I believed refusing the bet would force you to acknowledge that you were wrong, what I wrote above is actually realistic. You know it's realistic which is why you won't bet (and you shouldn't bet, you will instantly lose). I don't want you to give me a thousand dollars, I want you to acknowledge what I said was realistic.

Yeah, I get it. You think you're so smart and you gotcha'd me. So you're just being a jerk.

> You have discovered the next big thing in AI and you want to get away with a thousand dollar bet?

You mean the thing that's been blogged about for months, has countless of companies offering it in a turn-key form, or as a component of their products? The thing that OpenAI has dedicated models for, with dirt-cheap pricing? The thing OpenAI has had tutorials for in their documentation for some half a year now? The thing you can test for yourself in an evening if you know a little bit of Python?

Come on.

Please go back to the comment I posted at the top of this thread:

>> Take a robot and put an untrained neural net in its brain, then send it out into the world and wait to see what it learned. Do you think it will learn anything? It won't- because it will observe most events a single time. And neural nets don't learn that way. They must be trained, painstakingly, at great length, cost and effort, only "in the lab", on vast amounts of data and compute. And once they're trained, they're trained. They cannot learn anymore. They cannot change their model. Unless they are retrained. From scratch. Painstakingly, at great cost, on vast amounts of data and compute. Again. And again. And again.

Is that really the limitation that the thing everyone's doing is addressing? Or is this whole sub-thread just a big, irrelevant sidetrack from what I said, brought on by extreme misunderstanding of everything related to the subject? I put my money on the latter.

For the record, what I'm talking about is called "catastrophic forgetting" but I hate this term because it is one more instance of anthropomorphic bullshit of the type that abounds today in internet discourse.

I understand you're assuming that NNs won't be useful for this purpose, until they can be continuously on-line trained - that is, learning while working, like animal and human brains. Everyone else on this subthread is arguing that this is not necessary. Instead, you can use pre-trained NNs as fixed components, use a different system (like a vector database, in my example) to provide memory, and the whole thing will already be capable of learning.

Sure, it won't be able to fine-tune its instincts, at least not at first[0], but this doesn't matter - it makes no sense to compare robots and squirrels across their entire life cycle, as where every animal needs to learn most things from experience, an AI-powered robot rolls out of factory with all that experience preloaded, and experience itself is something developed separately, at scale, synthesizing much more any individual animal or human could ever learn in their lifetime.

In short: the limitation you're pointing out is a limitation of LARPing biological life. It's not a limitation for beating its performance across the board.

> For the record, what I'm talking about is called "catastrophic forgetting"

As I understand it, this is exactly the thing the robot-controlling AI will be immune to, if you build it around current pre-trained NN models and external memory. No NN fine-tuning on the fly -> no possibility for "catastrophic forgetting".

EDIT: Re launching a robot with an untrained NN - such a thing doesn't make practical sense unless you're also making the robot autonomously self-replicating. If you're going to build the hardware for the robot in a factory, you may just as well preload it with firmware; there's hardly a case where one would be possible and the other not.

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[0] - Two avenues for future development: 1) building the robot AI around models that can be fine-tuned on the fly, or 2) training an NN that can emulate an NN inside; the internal NN would then become a function of variable inputs to fixed NN. I think option 1) is actually promising - note that those NNs don't have to be continuously fine-tuneable - you can e.g. put two NNs for each component, use one during "wake time", and fine-tune it during "sleep time" based on recorded memory, while other serves as backup. Or fine-tune one while using the other, then swap pointers. There's solid prior art here with systems designed for space missions - the design I proposed is just a more complex version of how software updates are implemented on Martian rovers, and other hardware that can't be reflashed in the lab if you brick it remotely.