71–80 of 111 posts
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"Would love to see an architecture that learned" Would be a far more accurate statement. Training != Learning.
Do you have an example of an algorithm that learns, rather than is trained/trains itself? I don’t really see the boundary between the two concepts.
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Do you have an example of an algorithm that learns, rather than is trained/trains itself? I don’t really see the boundary between the two concepts.
If we make some massive physics breakthrough tommrow is an LLM going to be able to fully integrate that into its current data set? Or will we need to produce a host of documents and (re)train a new one in order for the concept to be deeply integrated. This distinction is subtle but lost on many who think that our current path will get us to AGI... That isn't to say we haven't created a meaningful tool but the sooner…
Earlier quoted context omitted.
If we make some massive physics breakthrough tommrow is an LLM going to be able to fully integrate that into its current data set? Or will we need to produce a host of documents and (re)train a new one in order for the concept to be deeply integrated. This distinction is subtle but lost on many who think that our current path will get us to AGI... That isn't to say we haven't created a meaningful tool but the sooner…
What is the subtle distinction? I'm "many" and it's not clear at all here. If we had some massive physics breakthrough, the LLM needs to be tought about it, but so do people. Teaching people about it would involve producing a host of documents in some format but that's also true of teaching people. Training and learning here seem to be opposite ends of the same verb no matter the medium, but I'm open to being enlight…
Earlier quoted context omitted.
Do you have an example of an algorithm that learns, rather than is trained/trains itself? I don’t really see the boundary between the two concepts.
If we make some massive physics breakthrough tommrow is an LLM going to be able to fully integrate that into its current data set? Or will we need to produce a host of documents and (re)train a new one in order for the concept to be deeply integrated. This distinction is subtle but lost on many who think that our current path will get us to AGI... That isn't to say we haven't created a meaningful tool but the sooner…
The reason that the models don't learn continuously is because it's currently prohibitively expensive. Imagine OpenAI retraining a model each time one of its 800m users sends a message. That'd make it aware instantly of every new development in the world or your life without any context engineering. There's a research gap here too but that'll be fixed with time and money.
But it's not a fundamental limitation of transformers as you make it out to be. To me it's just that things take time. The exact same architecture will be continuously learning in 2-3 years, and all the "This is the wrong path" people will need to shift goalposts. Note that I didn't argue for AGI, just that this isn't a fundamental limitiation.
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"an architecture that learned more like humans" i.e. enduring countless generations of evolutionary selection and cross breeding, then fine-tuning a bit? although it could be interesting, i don't think training on progressively complex strings entirely recapitulates this.
That’s a very interesting take. I hadn’t really considered evolution I guess if you really wanted to start from scratch, you could figure out how to evolve the whole system from a single cell or something like that. In some ways neural networks have kind of evolved in that way, assisted by humans. They started with a single perceptron, and have gone all the way to deep learning and convolutional networks I also remem…
Because they produce output probabilistically, when multiplication is deterministic. Why is this so hard for everyone?
Would love to see an architecture that learned more like humans. Start with just imitating one letter, then a few more, than some syllables, then full words, then sentences, etc. Progressively adding on top of previous knowledge Also, it’s interesting that one of the big goals/measures of models is their capacity to “generalize”, but the training methods optimize for loss/accuracy, and only after training test for ge…
Would love to see an architecture that learned more like humans. Start with just imitating one letter, then a few more, than some syllables, then full words, then sentences, etc. Progressively adding on top of previous knowledge Also, it’s interesting that one of the big goals/measures of models is their capacity to “generalize”, but the training methods optimize for loss/accuracy, and only after training test for ge…
Would like to see a car that moved like a horse.
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It doesn't work like mapping CPU caches/registers into an LLM context. Transformers have no mutable registers, they attend over past tokens and can't update prior state. RAG isn't RAM. Even with huge context, you still can't step CPU style instructions without an external, read/write memory/tooling. And temperature 0 makes outputs deterministic, not magically correct.
> And temperature 0 makes outputs deterministic, not magically correct. For reasons I don't claim to really understand, I don't think it even makes them deterministic. Floating point something something? I'm not sure temperature even has a static technical definition or implementation everywhere at this point. I've been ignoring temperature and using nucleus sampling anywhere that's exposed and it seems to work bette…
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Yes, I also wonder about this! Progress from children books to scientific papers etc. Could it learn e.g. language structure faster in a pre-training stage? Also somehow one needs to define a proxy to generalization to compute a loss and do backpropagation.
This field of study is known as "Curriculum Learning" for your Googling pleasure (or I guess ChatGPT Deep Research now).