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Perfectly reasonable, isn't it? But in a sense when YOU learned language YOU also learned a world model. For instance when your teacher explains to you the dif

Perfectly reasonable, isn't it? But in a sense when YOU learned language YOU also learned a world model. For instance when your teacher explains to you the difference between the tenses (had, have, will have) you realize that time is a thing that you need to think about. Even if you already had some sense of this, you now have it made explicit. Why should we say the LLM hasn't learned a world model when it's done what a kid has done, and everyone agrees the kid understands things? From what I see, there are some things it hasn't learned correctly. Notably with limbs, it doesn't know how fingers and elbows work, for some reason. But it does know something about what they should look like, and so we get these hilarious images. But I also don't see why it shouldn't overcome this eventually, since it's come pretty far as it is.

daveguy 3y ago
The reason why the LLM apparent world model should not be considered to be the same as a human's world model is because of the modality of learning. The world model we learn as we learn a language includes the world model embedded in language. But the human world model includes models embedded in flailing about limbs, the permanence of an object, sounds and smells associated with walking through the world. Now, all those senses and interactions obviously aren't required for a robust world model. But I would be willing to make a large wager that training on more than "valid sequences of words" definitely is required. That's why hallucinations, confident wrongness, and bizarre misunderstandings are endemic to the failings of LLMs. Don't get me wrong. LLMs are a technological breakthrough in AI for language processing. They are extremely useful in themselves. However, they are not and will not become AGI through larger models. Lessons learned from LLMs will transfer to other modes of interaction. I believe multi-modal learning and transfer learning are the most interesting fields in AI right now.
- lordnacho 3y ago
  That makes sense, but isn't this a matter of presenting it with more models? Maybe a physical model discovered via video or something like that? Then it will be similar to what babies are trained with, images and sound. Tactile and olfactory would be similar. By doing this you'd glue the words to sights, sounds, smells, etc. But it also seems like this is already someone has thought of and is being explored.
  - daveguy 3y ago
    You are correct, there is active research on this. And words and pictures are associated in models like stable diffusion. There has been some success combining GANs and LLMs, but it is far from a solved problem. And as the training data gets more complex the required training resources increase too. Currently it's more like a confusing barrier than a happy extension of LLMs.

daveguy 3y ago

The reason why the LLM apparent world model should not be considered to be the same as a human's world model is because of the modality of learning. The world model we learn as we learn a language includes the world model embedded in language. But the human world model includes models embedded in flailing about limbs, the permanence of an object, sounds and smells associated with walking through the world. Now, all those senses and interactions obviously aren't required for a robust world model. But I would be willing to make a large wager that training on more than "valid sequences of words" definitely is required. That's why hallucinations, confident wrongness, and bizarre misunderstandings are endemic to the failings of LLMs. Don't get me wrong. LLMs are a technological breakthrough in AI for language processing. They are extremely useful in themselves. However, they are not and will not become AGI through larger models. Lessons learned from LLMs will transfer to other modes of interaction. I believe multi-modal learning and transfer learning are the most interesting fields in AI right now.

lordnacho 3y ago
That makes sense, but isn't this a matter of presenting it with more models? Maybe a physical model discovered via video or something like that? Then it will be similar to what babies are trained with, images and sound. Tactile and olfactory would be similar. By doing this you'd glue the words to sights, sounds, smells, etc. But it also seems like this is already someone has thought of and is being explored.
- daveguy 3y ago
  You are correct, there is active research on this. And words and pictures are associated in models like stable diffusion. There has been some success combining GANs and LLMs, but it is far from a solved problem. And as the training data gets more complex the required training resources increase too. Currently it's more like a confusing barrier than a happy extension of LLMs.