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We make correlations based on experience of reality. They make correlations based on experience of text. Text is only meaningful when it is correlated with re

We make correlations based on experience of reality. They make correlations based on experience of text. Text is only meaningful when it is correlated with reality. An LLM will see that "Dog" and "Woof" occur near each other in text a lot, but it doesn't know what a dog is or what a woof is. You do know that, because you have experienced an actual dog and heard it go woof.

mewpmewp2 2y ago
But there's also plenty of things I haven't seen in real life and have only read about in books or text. Does it mean that I wouldn't know what they are as well? In addition you can feed LLMs image, audio input as well, and in theory you could feed any sensory data that we get. In fact it can describe what is on an image, to quite a lot of detail. Is knowing what a dog is being able to recognize it on an image, and is knowing what a woof is being able to recognize it in an audio? Ultimately, after all, all of it can be represented as input tokens, binary data or otherwise. We also receive constant input from the World, which could be represented in any various different forms and processing layers can transform it into different helpful symbolic things to problem solve based on that. LLMs in order to be able to logically consider what a dog might do given certain situations, would have needed to build some form of neural representation of the idea to be able to produce the output it can, that goes beyond parroting the training data in my view. I can give it an input in some interesting configurations and permutations and it's able to logically use the concepts to figure out what might happen.
- AndrewDucker 2y ago
  "Does it mean that I wouldn't know what they are as well?" Are they explained in terms of things that you have seen in real life? Or in ways that build on that? Then you can certainly gain that by building on those models. And yes, if you can feed an attention-based neural network (not an LLM, those are specifically Language-based) sense data (and preferably action data too, for how it interacts with the world) then that would give it a picture of the world. And once it can recognise a dog then you can say "And we call this a dog!". There's research in to this already - see this piece https://www.economist.com/science-and-technology/2024/06/05/robots-are-suddenly-getting-cleverer-whats-changed https://www.economist.com/science-and-technology/2024/06/05/... (you may need to use archive.is to read the archive of that page). ==== These “vision-language-action models” (VLAMs) take in text and images, plus data relating to the robot’s presence in the physical world, including the readings on internal sensors, the degree of rotation of different joints and the positions of actuators (such as grippers, or the fingers of a robot’s hands). The resulting models can then answer questions about a scene, such as “can you see an apple?” But they can also predict how a robot arm needs to move to pick that apple up, as well as how this will affect what the world looks like." Grounding the model’s perception in the real world in this way greatly reduces hallucinations (the tendency for AI models to make things up and get things wrong). ====
  - mewpmewp2 2y ago
    But GPT-4o for example as a multimodal model is already able to take images as an input, so does it have enough to know what a dog is in your opinion? Even though it's able to process images it's still being called an LLM, but multimodal LLM.
mewpmewp2 2y ago
For instance, if it doesn't understand or know what animals are, how is it able to answer a prompt like this: https://chatgpt.com/share/68815b5f-7570-443a-bcdf-e6ef4d233ba4 https://chatgpt.com/share/68815b5f-7570-443a-bcdf-e6ef4d233b... And connect the dots in such a way to develop this interesting scenario and at the same time consider so many different interactions between the room and the animals.
- AndrewDucker 2y ago
  Because it knows which text is associated with which other text, in quite complex and subtle ways. It's exceptionally good at analysing text without needing to understand anything more than which words are seen in proximity to which other words, and how their placement in relation to each other affects other bits of text. It is amazing how well this technique works. But at no point has it linked the word "cat" to real actual cats, because it has literally got no access to any real actual cats. It has had no access to any information beyond the word "cat" and its relationship to other words.
  - mewpmewp2 2y ago
    You are calling it text associated with other text. But I think it has to have internalized it in a much more complex way to be able to produce output like that to any arbitrary configuration of different things. This internalization and ability to apply that internalization to produce scenarios like that tells me it must have enough understanding, even if it hasn't been able to "see" a cat, it has an understanding of its characteristics and description, and how the same thing can interact with other things. To me that's understanding of the object. Alternatively, if we are talking about a blind and deaf person, and that blind person hasn't ever seen any cats, would they be unable to understand what a cat ultimately is? If all their input is in braille and tactile sign language?
    - AndrewDucker 2y ago
      "it has an understanding of its characteristics and description" It has no access to the characteristics. It literally only has access to text. It knows that some text is associated with the word "cat", but it doesn't know what "fluffy" means, because it has never seen fluff. There is literally an infinite regress of meaning, where you never get out of language to actual meaning, unless you have some kind of experience of "fluff" (or something else which can be related to "fluff"). A is a bit like B which is a bit like C, which is like D, which is a mix of B and E, which is quite F-like. This never gets you to meaning, because the meaning is the sensation, or the experience. And it doesn't have that. (Blind people would be able to understand the physicallity of a cat, because they can still experience them in a variety of ways. Whether they can understand that the cat is white is down to the kind of blindness, but if they've never seen colour then probably not.)

mewpmewp2 2y ago

But there's also plenty of things I haven't seen in real life and have only read about in books or text. Does it mean that I wouldn't know what they are as well? In addition you can feed LLMs image, audio input as well, and in theory you could feed any sensory data that we get. In fact it can describe what is on an image, to quite a lot of detail. Is knowing what a dog is being able to recognize it on an image, and is knowing what a woof is being able to recognize it in an audio? Ultimately, after all, all of it can be represented as input tokens, binary data or otherwise. We also receive constant input from the World, which could be represented in any various different forms and processing layers can transform it into different helpful symbolic things to problem solve based on that. LLMs in order to be able to logically consider what a dog might do given certain situations, would have needed to build some form of neural representation of the idea to be able to produce the output it can, that goes beyond parroting the training data in my view. I can give it an input in some interesting configurations and permutations and it's able to logically use the concepts to figure out what might happen.

AndrewDucker 2y ago
"Does it mean that I wouldn't know what they are as well?" Are they explained in terms of things that you have seen in real life? Or in ways that build on that? Then you can certainly gain that by building on those models. And yes, if you can feed an attention-based neural network (not an LLM, those are specifically Language-based) sense data (and preferably action data too, for how it interacts with the world) then that would give it a picture of the world. And once it can recognise a dog then you can say "And we call this a dog!". There's research in to this already - see this piece https://www.economist.com/science-and-technology/2024/06/05/robots-are-suddenly-getting-cleverer-whats-changed https://www.economist.com/science-and-technology/2024/06/05/... (you may need to use archive.is to read the archive of that page). ==== These “vision-language-action models” (VLAMs) take in text and images, plus data relating to the robot’s presence in the physical world, including the readings on internal sensors, the degree of rotation of different joints and the positions of actuators (such as grippers, or the fingers of a robot’s hands). The resulting models can then answer questions about a scene, such as “can you see an apple?” But they can also predict how a robot arm needs to move to pick that apple up, as well as how this will affect what the world looks like." Grounding the model’s perception in the real world in this way greatly reduces hallucinations (the tendency for AI models to make things up and get things wrong). ====
- mewpmewp2 2y ago
  But GPT-4o for example as a multimodal model is already able to take images as an input, so does it have enough to know what a dog is in your opinion? Even though it's able to process images it's still being called an LLM, but multimodal LLM.

mewpmewp2 2y ago

For instance, if it doesn't understand or know what animals are, how is it able to answer a prompt like this: https://chatgpt.com/share/68815b5f-7570-443a-bcdf-e6ef4d233ba4 https://chatgpt.com/share/68815b5f-7570-443a-bcdf-e6ef4d233b... And connect the dots in such a way to develop this interesting scenario and at the same time consider so many different interactions between the room and the animals.

AndrewDucker 2y ago
Because it knows which text is associated with which other text, in quite complex and subtle ways. It's exceptionally good at analysing text without needing to understand anything more than which words are seen in proximity to which other words, and how their placement in relation to each other affects other bits of text. It is amazing how well this technique works. But at no point has it linked the word "cat" to real actual cats, because it has literally got no access to any real actual cats. It has had no access to any information beyond the word "cat" and its relationship to other words.
- mewpmewp2 2y ago
  You are calling it text associated with other text. But I think it has to have internalized it in a much more complex way to be able to produce output like that to any arbitrary configuration of different things. This internalization and ability to apply that internalization to produce scenarios like that tells me it must have enough understanding, even if it hasn't been able to "see" a cat, it has an understanding of its characteristics and description, and how the same thing can interact with other things. To me that's understanding of the object. Alternatively, if we are talking about a blind and deaf person, and that blind person hasn't ever seen any cats, would they be unable to understand what a cat ultimately is? If all their input is in braille and tactile sign language?
  - AndrewDucker 2y ago
    "it has an understanding of its characteristics and description" It has no access to the characteristics. It literally only has access to text. It knows that some text is associated with the word "cat", but it doesn't know what "fluffy" means, because it has never seen fluff. There is literally an infinite regress of meaning, where you never get out of language to actual meaning, unless you have some kind of experience of "fluff" (or something else which can be related to "fluff"). A is a bit like B which is a bit like C, which is like D, which is a mix of B and E, which is quite F-like. This never gets you to meaning, because the meaning is the sensation, or the experience. And it doesn't have that. (Blind people would be able to understand the physicallity of a cat, because they can still experience them in a variety of ways. Whether they can understand that the cat is white is down to the kind of blindness, but if they've never seen colour then probably not.)