6 ms·

Those are the in / outputs, but the model works on the embeddings which is a high dimensional representation of meaning.

by Tarq0n 4y ago

nerdponx 4y ago
Meaning only insofar as can be observed from the context of the word. I don't think it's reasonable to call that "meaning" in and of itself.
- SanderNL 4y ago
  It’s a good philosophical exercise to imagine what’s left of the meaning of a word if you strip all context from it. What is a word if it doesn’t reference anything, if it has no context? Isn’t this like a pointer without referencing an address? I’ll give you that words can reference tokens of other modalities like hearing and such, but that is still the same problem.
  - nerdponx 4y ago
    I'm thinking in the other direction. Of course context reveals (or as in your philosophical exercise, defines) some of the meaning of the word. But how much of the meaning is context-dependent depends on the word and the context itself where it's being used. For example, consider a word that means two different things in two different contexts that would otherwise be very far apart in whatever latent "meaning space" they might exist in. A naive model might end up averaging across both contexts, resulting in a representation of that word that represents neither meaning of the word, being located vaguely in between both regions of space. Alternatively, this could pull "groups" of words that otherwise are not related into adjacent regions of space, creating the appearance of semantic similarity when there shouldn't be any. In this example, the physical surrounding word context is actually misleading, and addition information is needed in order to make the learned embeddings more realistic. In any case, it should be obvious to people trained in statistical practice that word models do not encode "meaning" per se, but rather an estimate of a model of meaning, which is not a complete model, but it does a pretty good job most of the time. We as the experts should be careful to stick to language along these lines, so as not to promulgate fanciful ideas about AI. I am not even going to bother with the "it's not really intelligent!" angle here. For all I care, the model is fully sentient. But the fact is that the token embeddings do not encode "meaning", it encodes "an estimate of model of meaning". They are our best effort attempt at encoding meaning parsimoniously, but that is not the same thing as meaning itself. Map is not the territory, all models are wrong, etc.
    - SanderNL 4y ago
      > Map is not the territory I think this one will get fuzzy real fast when you talk about cognition itself. Otherwise, thanks for the thoughtful response. Food for thought.
dvt 4y ago
> high dimensional representation of meaning This is word salad. I don't even know what you mean by "meaning," but an embedding is simply an array filled with a bunch of floating point numbers[1] and, importantly, these embeddings are trained, so "doctor" will have an embedding similar to "hospital" because it's seen in close proximity in the training data. [1] https://www.tensorflow.org/text/guide/word_embeddings https://www.tensorflow.org/text/guide/word_embeddings
- Dylan16807 4y ago
  How is it word salad? The length of the number array is referred to as dimensions for math reasons. That's half the statement right there. > importantly, these embeddings are trained, so "doctor" will have an embedding similar to "hospital" because it's seen in close proximity in the training data It is trained to put together words with similar... meaning. So the embedding is a representation of... meaning.
  - dvt 4y ago
    > It is trained to put together words with similar... meaning. So the embedding is a representation of... meaning. This might be waxing philosophical, but my position is that the meaning of a word is intrinsic, not extrinsic. The meaning of the word "ball" has nothing to do with the fact that it's next to the word "net" on a page (which is what LLMs do). But rather, "ball" is the round rubbery thing I kick around in my back yard.
    - airstrike 4y ago
      Except "ball" can also mean a party, so you do need "net" or other words for context
      - vidarh 4y ago
        More fundamentally, until someone has told you that "ball" can mean "a round thing" or pointed to a ball and said that "ball" can mean that, "ball" does not have that meaning to you. The meaning is purely extrinsic. There's nothing about a ball that makes it named that, as shown by the fact that a ball is called other things in other languages.
beepbooptheory 4y ago
I feel like if meaning itself was simply contained or even encoded in the words we write, the world would be a very different place to us. How could we ever find ourselves, as we do, arguing about the meaning of a text? Even then driest prose can sometimes lead to different interpretations. Is there "meaning" somehow inside the novel on my desk? Can you extract it and manipulate it? If I had no body, environment, temporality, what even could the book mean to me? Even more, if nobody could teach me the meanings of the words themselves in the book, what could it ever be to me but a collection of certain patterns?
- gabereiser 4y ago
  but it is exactly human-kinds ability to put meaning to words that make us unique. That give us language and communication, artistry and music. Words mean nothing without meaning. It's not encoded, it's inferred. There's nouns, adjectives, verbs, etc and some of them are tied to the same word. That's very important. The chain of words derives it's meaning in context so having a system that operates on meaning is, dare I say, meaningful. It's the whole point.
- knome 4y ago
  >if meaning itself was simply contained or even encoded in the words we write I feel you might enjoy Chapter VI of Douglas Hofstadter's "Godel, Escher, Back", being "The Location of Meaning".

Those are the in / outputs, but the model works on the embeddings which is a high dimensional representation of meaning.

by Tarq0n 4y ago

Those are the in / outputs, but the model works on the embeddings which is a high dimensional representation of meaning.

nerdponx 4y ago

Meaning only insofar as can be observed from the context of the word. I don't think it's reasonable to call that "meaning" in and of itself.

SanderNL 4y ago
It’s a good philosophical exercise to imagine what’s left of the meaning of a word if you strip all context from it. What is a word if it doesn’t reference anything, if it has no context? Isn’t this like a pointer without referencing an address? I’ll give you that words can reference tokens of other modalities like hearing and such, but that is still the same problem.
- nerdponx 4y ago
  I'm thinking in the other direction. Of course context reveals (or as in your philosophical exercise, defines) some of the meaning of the word. But how much of the meaning is context-dependent depends on the word and the context itself where it's being used. For example, consider a word that means two different things in two different contexts that would otherwise be very far apart in whatever latent "meaning space" they might exist in. A naive model might end up averaging across both contexts, resulting in a representation of that word that represents neither meaning of the word, being located vaguely in between both regions of space. Alternatively, this could pull "groups" of words that otherwise are not related into adjacent regions of space, creating the appearance of semantic similarity when there shouldn't be any. In this example, the physical surrounding word context is actually misleading, and addition information is needed in order to make the learned embeddings more realistic. In any case, it should be obvious to people trained in statistical practice that word models do not encode "meaning" per se, but rather an estimate of a model of meaning, which is not a complete model, but it does a pretty good job most of the time. We as the experts should be careful to stick to language along these lines, so as not to promulgate fanciful ideas about AI. I am not even going to bother with the "it's not really intelligent!" angle here. For all I care, the model is fully sentient. But the fact is that the token embeddings do not encode "meaning", it encodes "an estimate of model of meaning". They are our best effort attempt at encoding meaning parsimoniously, but that is not the same thing as meaning itself. Map is not the territory, all models are wrong, etc.
  - SanderNL 4y ago
    > Map is not the territory I think this one will get fuzzy real fast when you talk about cognition itself. Otherwise, thanks for the thoughtful response. Food for thought.

dvt 4y ago

> high dimensional representation of meaning This is word salad. I don't even know what you mean by "meaning," but an embedding is simply an array filled with a bunch of floating point numbers[1] and, importantly, these embeddings are trained, so "doctor" will have an embedding similar to "hospital" because it's seen in close proximity in the training data. [1] https://www.tensorflow.org/text/guide/word_embeddings https://www.tensorflow.org/text/guide/word_embeddings

Dylan16807 4y ago
How is it word salad? The length of the number array is referred to as dimensions for math reasons. That's half the statement right there. > importantly, these embeddings are trained, so "doctor" will have an embedding similar to "hospital" because it's seen in close proximity in the training data It is trained to put together words with similar... meaning. So the embedding is a representation of... meaning.
- dvt 4y ago
  > It is trained to put together words with similar... meaning. So the embedding is a representation of... meaning. This might be waxing philosophical, but my position is that the meaning of a word is intrinsic, not extrinsic. The meaning of the word "ball" has nothing to do with the fact that it's next to the word "net" on a page (which is what LLMs do). But rather, "ball" is the round rubbery thing I kick around in my back yard.
  - airstrike 4y ago
    Except "ball" can also mean a party, so you do need "net" or other words for context
    - vidarh 4y ago
      More fundamentally, until someone has told you that "ball" can mean "a round thing" or pointed to a ball and said that "ball" can mean that, "ball" does not have that meaning to you. The meaning is purely extrinsic. There's nothing about a ball that makes it named that, as shown by the fact that a ball is called other things in other languages.

beepbooptheory 4y ago

I feel like if meaning itself was simply contained or even encoded in the words we write, the world would be a very different place to us. How could we ever find ourselves, as we do, arguing about the meaning of a text? Even then driest prose can sometimes lead to different interpretations. Is there "meaning" somehow inside the novel on my desk? Can you extract it and manipulate it? If I had no body, environment, temporality, what even could the book mean to me? Even more, if nobody could teach me the meanings of the words themselves in the book, what could it ever be to me but a collection of certain patterns?

gabereiser 4y ago
but it is exactly human-kinds ability to put meaning to words that make us unique. That give us language and communication, artistry and music. Words mean nothing without meaning. It's not encoded, it's inferred. There's nouns, adjectives, verbs, etc and some of them are tied to the same word. That's very important. The chain of words derives it's meaning in context so having a system that operates on meaning is, dare I say, meaningful. It's the whole point.
knome 4y ago
>if meaning itself was simply contained or even encoded in the words we write I feel you might enjoy Chapter VI of Douglas Hofstadter's "Godel, Escher, Back", being "The Location of Meaning".