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0 pointsdimitri-vs1y ago0 comments

Have we really watered down the definition of AGI that much?

LLMs aren't really capable of "learning" anything outside their training data. Which I feel is a very basic and fundamental capability of humans.

Every new request thread is a blank slate utilizing whatever context you provide for the specific task and after the tread is done (or context limit runs out) it's like it never happened. Sure you can use databases, do web queries, etc. but these are inflexible bandaid solutions, far from what's needed for AGI.

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theptip1y ago

> LLMs aren't really capable of "learning" anything outside their training data.

ChatGPT has had for some time the feature of storing memories about its conversations with users. And you can use function calling to make this more generic.

I think drawing the boundary at “model + scaffolding” is more interesting.

dimitri-vsOP1y ago

Calling the sentence or two it arbitrarily saves when you statd your preferences and profile info "memories" is a stretch.

True equivalent to human memories would require something like a multimodal trillion token context window.

RAG is just not going to cut it, and if anything will exacerbated problems with hallucinations.

theptip1y ago

Well, now you’ve moved the goalposts from “learn anything” to “learn at human level”. Sure, they don’t have that yet.

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TrapLord_Rhodo1y ago

Thats the whole point of llama index? I can connect my LLM to any node or context i want. Syncing it to a real time data flow like an API and it can learn...? How is that different than a human?

Once optimus is up an working by the 100k+, the spatial problems will be solved. We just don't have enough spatial awareness data, or for a way for the LLM to learn about the physical world.

bubblyworld1y ago

That's true for vanilla LLMs, but also keep in mind that there are no details about o3's architecture at the moment. Clearly they are doing something different given the huge performance jump on a lot of benchmarks, and it may well involve in-context learning.

catmanjan1y ago

Given every other iteration has basically just been the same thing but bigger, why should we think this?

bubblyworld1y ago

My point was to caution against being too confident about the underlying architecture, not to argue for any particular alternative.

Your statement is false - things changed a lot between gpt4 and o1 under the hood, but notably not a larger model size. In fact the model size of o1 is smaller than gpt4 by several orders of magnitude! Improvements are being made in other ways.

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0 pointsdimitri-vs1y ago0 comments

Have we really watered down the definition of AGI that much?

LLMs aren't really capable of "learning" anything outside their training data. Which I feel is a very basic and fundamental capability of humans.

0 comments

theptip1y ago

> LLMs aren't really capable of "learning" anything outside their training data.

ChatGPT has had for some time the feature of storing memories about its conversations with users. And you can use function calling to make this more generic.

I think drawing the boundary at “model + scaffolding” is more interesting.

dimitri-vsOP1y ago

Calling the sentence or two it arbitrarily saves when you statd your preferences and profile info "memories" is a stretch.

True equivalent to human memories would require something like a multimodal trillion token context window.

RAG is just not going to cut it, and if anything will exacerbated problems with hallucinations.

theptip1y ago

Well, now you’ve moved the goalposts from “learn anything” to “learn at human level”. Sure, they don’t have that yet.

1 more reply

TrapLord_Rhodo1y ago

Thats the whole point of llama index? I can connect my LLM to any node or context i want. Syncing it to a real time data flow like an API and it can learn...? How is that different than a human?

Once optimus is up an working by the 100k+, the spatial problems will be solved. We just don't have enough spatial awareness data, or for a way for the LLM to learn about the physical world.

bubblyworld1y ago

catmanjan1y ago

Given every other iteration has basically just been the same thing but bigger, why should we think this?

bubblyworld1y ago

My point was to caution against being too confident about the underlying architecture, not to argue for any particular alternative.

j / k navigate · click thread line to collapse