4 ms·

While it's true that language models are fundamentally based on statistical patterns in language, characterizing them as mere "probabilistic syllable generators

by ttul 2y ago

While it's true that language models are fundamentally based on statistical patterns in language, characterizing them as mere "probabilistic syllable generators" significantly understates their capabilities and functional intelligence. These models can engage in multistep logical reasoning, solve complex problems, and generate novel ideas - going far beyond simply predicting the next syllable. They can follow intricate chains of thought and arrive at non-obvious conclusions. And OpenAI has now showed us that fine-tuning a model specifically to plan step by step dramatically improves its ability to solve problems that were previously the domain of human experts. Although there is no definitive evidence that state-of-the-art language models have a comprehensive "world model" in the way humans do, several studies and observations suggest that large language models (LLMs) may possess some elements or precursors of a world model. For example, Tegmark and Gurnee [1] found that LLMs learn linear representations of space and time across multiple scales. These representations appear to be robust to prompting variations and unified across different entity types. This suggests that modern LLMs may learn rich spatiotemporal representations of the real world, which could be considered basic ingredients of a world model. And even if we look at much smaller models like Stable Diffusion XL, it's clear that they encode a rich understanding of optics [2] within just a few billion parameters (3.5 billion to be precise). Generative video models like OpenAI's Sora clearly have a world model as they are able to simulate gravity, collisions between objects, and other concepts necessary to render a coherent scene. As for AGI, the consensus on Metaculus is that it will arrive in 2023. But consider that before GPT-4 arrived, the consensus was that full AGI was not coming until 2041 [3]. The consensus for the arrival date of "weakly general" AGI is 2027 [4] (i.e AGI that doesn't have a robotic physical world component). The best tool for achieving AGI is the transformer and its derivatives; its scaling keeps going with no end in sight. Citations: [1] https://paperswithcode.com/paper/language-models-represent-space-and-time https://paperswithcode.com/paper/language-models-represent-s... [2] https://www.reddit.com/r/StableDiffusion/comments/15he3f4/elven_rings_sdxl_10_offset_lora/ https://www.reddit.com/r/StableDiffusion/comments/15he3f4/el... [3] https://www.metaculus.com/questions/5121/date-of-artificial-general-intelligence/ https://www.metaculus.com/questions/5121/date-of-artificial-... [4] https://www.metaculus.com/questions/3479/date-weakly-general-ai-is-publicly-known/ https://www.metaculus.com/questions/3479/date-weakly-general...

iLoveOncall 2y ago
> Generative video models like OpenAI's Sora clearly have a world model as they are able to simulate gravity, collisions between objects, and other concepts necessary to render a coherent scene. I won't expand on the rest, but this is simply nonsensical. The fact that Sora generates output that matches its training data doesn't show that it has a concept of gravity, collision between object, or anything else. It has a "world model" the same way a photocopier has a "document model".
- svara 2y ago
  My suspicion is that you're leaving some important parts in your logic unstated. Such as belief in a magical property within humans of "understanding", which you don't define. The ability of video models to generate novel video consistent with physical reality shows that they have extracted important invariants - physical law - out of the data. It's probably better not to muddle the discussion with ill defined terms such as "intelligence" or "understanding". I have my own beef with the AGI is nigh crowd, but this criticism amounts to word play.
  - phatfish 2y ago
    It feels like if these image and video generation models were really resolving some fundamental laws from the training data they should at least be able to re-create an image at a different angle.
  - some1else 2y ago
    "Allegory of the cave" comes to mind, when trying to describe the understanding that's missing from diffusion models. I think a super-model with such qualifications would require a number of ControlNets in a non-visual domains to be able to encode understanding of the underlying physics. Diffusion models can render permutations of whatever they've seen fairly well without that, though.
    - svara 2y ago
      I'm very familiar with the allegory of the cave, but I'm not sure I understand where you're going with the analogy here. Are you saying that it is not possible to learn about dynamics in a higher dimensional space from a lower dimensional projection? This is clearly not true in general. E.g., video models learn that even though they're only ever seeing and outputting 2d data, objects have different sides in a fashio that is consistent with our 3d reality. The distinctions you (and others in this thread) are making is purely one of degree - how much generalization has been achieved, and how well - versus one of category.

by ttul 2y ago

iLoveOncall 2y ago

> Generative video models like OpenAI's Sora clearly have a world model as they are able to simulate gravity, collisions between objects, and other concepts necessary to render a coherent scene. I won't expand on the rest, but this is simply nonsensical. The fact that Sora generates output that matches its training data doesn't show that it has a concept of gravity, collision between object, or anything else. It has a "world model" the same way a photocopier has a "document model".

svara 2y ago
My suspicion is that you're leaving some important parts in your logic unstated. Such as belief in a magical property within humans of "understanding", which you don't define. The ability of video models to generate novel video consistent with physical reality shows that they have extracted important invariants - physical law - out of the data. It's probably better not to muddle the discussion with ill defined terms such as "intelligence" or "understanding". I have my own beef with the AGI is nigh crowd, but this criticism amounts to word play.
- phatfish 2y ago
  It feels like if these image and video generation models were really resolving some fundamental laws from the training data they should at least be able to re-create an image at a different angle.
- some1else 2y ago
  "Allegory of the cave" comes to mind, when trying to describe the understanding that's missing from diffusion models. I think a super-model with such qualifications would require a number of ControlNets in a non-visual domains to be able to encode understanding of the underlying physics. Diffusion models can render permutations of whatever they've seen fairly well without that, though.
  - svara 2y ago
    I'm very familiar with the allegory of the cave, but I'm not sure I understand where you're going with the analogy here. Are you saying that it is not possible to learn about dynamics in a higher dimensional space from a lower dimensional projection? This is clearly not true in general. E.g., video models learn that even though they're only ever seeing and outputting 2d data, objects have different sides in a fashio that is consistent with our 3d reality. The distinctions you (and others in this thread) are making is purely one of degree - how much generalization has been achieved, and how well - versus one of category.