3 ms·

> Why would it ever be possible to derive the representation just from the pointer? Of course it cannot derive a representation from just a pointer. Neither hu

by xcv123 3y ago

> Why would it ever be possible to derive the representation just from the pointer? Of course it cannot derive a representation from just a pointer. Neither humans or machines can do that. The words are not pointers in isolation. They are connected in a network of semantic relations. The model is in the relationships between pointers. Artificial neural network.

YeGoblynQueenne 3y ago
The question then becomes where do the "semantic" relations in the network come from. Unless there is some way to extract the meaning of words with respect to the real-world concepts they refer to, from text, any relation between words that your model, models is not going to be semantic (i.e. it won't represent meaning). Like I say in another comment, this kind of explanation is "words all the way down", but it really doesn't explain where the process ends (or begins) and where the meaning comes into the words. To give you an example, here is a small semantic network that I just created by hand, that represents is-a and has-a relations: abc32 is-a c356 7yt4 is-a c356 c356 has-a 902a c356 has-a 8773 The names of entities in the network, like abc32, replace real English words that I just changed manually (or, well, vim-illy). You can use the is-a and has-a relations in the network to infer e.g. that abc32 has-a 902a, and that 7yt4 and abc32 are things of the same kind, but go ahead and try to understand what the real-world entities I renamed were. You can't- and neither can any model that is trained on text consisting of words whose meaning it understands as much as you understand the meaning of abc32 and 7yt4. Wikipedia has an article on semantic networks: https://en.wikipedia.org/wiki/Semantic_network https://en.wikipedia.org/wiki/Semantic_network I haven't read it but from a quick glance it looks informative.
- xcv123 3y ago
  > but go ahead and try to understand what the real-world entities I renamed were. Obviously not. Insufficient data. Try that substitution exercise again with terabytes of data (Common Crawl dataset) then feed it into a neural network and see if it can find any patterns.
  - deleted 3y ago
    [deleted]
  - YeGoblynQueenne 3y ago
    This is not a matter of data quantity, but data quality. Text data doesn't have the information needed to learn a world model from it and so it doesn't matter how big a model you train, it will never learn the right representation (i.e. the right patterns).
    - xcv123 3y ago
      > Text data doesn't have the information needed to learn a world model from it As I mentioned in another reply you have a philosophical disagreement and are missing the point. It provides "a" model of the world. Not necessarily the "one true model" that is your own personal life experience of the world since birth. There is enough information to build "a" world model in all the text ever written by humans about the world. That is obvious. So your argument is completely subjective and philosophical, not technical. GPT now includes imagery in its training set BTW.
      - YeGoblynQueenne 3y ago
        I don't do philosophical arguments. But it's obvious this conversation not productive so there's no reason for me to insist.
        xcv123 3y ago
        But you are essentially making a philosophical argument about what defines a "world model". It is a disagreement about a subjective interpretation of the term.
        YeGoblynQueenne 3y ago
        Be careful about jumping to conclusions just to convince yourself you're right and I'm wrong. I do not assume what you think I assume. But it's obvious you're not looking for a curious conversation and reaching any kind of common understanding.

by xcv123 3y ago

YeGoblynQueenne 3y ago

The question then becomes where do the "semantic" relations in the network come from. Unless there is some way to extract the meaning of words with respect to the real-world concepts they refer to, from text, any relation between words that your model, models is not going to be semantic (i.e. it won't represent meaning). Like I say in another comment, this kind of explanation is "words all the way down", but it really doesn't explain where the process ends (or begins) and where the meaning comes into the words. To give you an example, here is a small semantic network that I just created by hand, that represents is-a and has-a relations: abc32 is-a c356 7yt4 is-a c356 c356 has-a 902a c356 has-a 8773 The names of entities in the network, like abc32, replace real English words that I just changed manually (or, well, vim-illy). You can use the is-a and has-a relations in the network to infer e.g. that abc32 has-a 902a, and that 7yt4 and abc32 are things of the same kind, but go ahead and try to understand what the real-world entities I renamed were. You can't- and neither can any model that is trained on text consisting of words whose meaning it understands as much as you understand the meaning of abc32 and 7yt4. Wikipedia has an article on semantic networks: https://en.wikipedia.org/wiki/Semantic_network https://en.wikipedia.org/wiki/Semantic_network I haven't read it but from a quick glance it looks informative.

xcv123 3y ago
> but go ahead and try to understand what the real-world entities I renamed were. Obviously not. Insufficient data. Try that substitution exercise again with terabytes of data (Common Crawl dataset) then feed it into a neural network and see if it can find any patterns.
- deleted 3y ago
  [deleted]
- YeGoblynQueenne 3y ago
  This is not a matter of data quantity, but data quality. Text data doesn't have the information needed to learn a world model from it and so it doesn't matter how big a model you train, it will never learn the right representation (i.e. the right patterns).
  - xcv123 3y ago
    > Text data doesn't have the information needed to learn a world model from it As I mentioned in another reply you have a philosophical disagreement and are missing the point. It provides "a" model of the world. Not necessarily the "one true model" that is your own personal life experience of the world since birth. There is enough information to build "a" world model in all the text ever written by humans about the world. That is obvious. So your argument is completely subjective and philosophical, not technical. GPT now includes imagery in its training set BTW.
    - YeGoblynQueenne 3y ago
      I don't do philosophical arguments. But it's obvious this conversation not productive so there's no reason for me to insist.
      - xcv123 3y ago
        But you are essentially making a philosophical argument about what defines a "world model". It is a disagreement about a subjective interpretation of the term.
        YeGoblynQueenne 3y ago
        Be careful about jumping to conclusions just to convince yourself you're right and I'm wrong. I do not assume what you think I assume. But it's obvious you're not looking for a curious conversation and reaching any kind of common understanding.