What you’re saying is basically: sometimes we know some aspects of a thing, but don’t know other aspects of it. There’s a thing in a suitcase. Well, I know where it is (in the suitcase), and a bit about how big it is (smaller than a bulldozer), and whether it’s tangible versus abstract (tangible). Then there are other things about it that I don’t know, like its color and shape. OK, cool. That’s not unusual—absolutely everything is like that. Even things I’m looking straight at are like that. I don’t know their weight, internal composition, etc.
I don’t need a “theory” to explain how a “hypothetical” learning algorithm can build a generative model that can represent this kind of information in its latent variables, and draw appropriate inferences. It’s not a hypothetical! Any generative model built by a predictive learning algorithm will actually do this—it will pick up on local patterns and extrapolate them, even in the absence of omniscient knowledge of every aspect of the thing / situation. It will draw inferences from the limited information it does have. Trained LLMs do this, and an adult cortex does it too.
I think you’re going wrong by taking “aboutness” to be a bedrock principle of how you’re thinking about things. These predictive learning algorithms and trained models actually exist. If, when you run these algorithms, you wind up with all kinds of edge cases where it’s unclear what is “about” what, (and you do), then that’s a sign that you should not be treating “aboutness” as a bedrock principle in the first place. “Aboutness” is like any other word / category—there are cases where it’s clearly a useful notion, and cases where it’s clearly not, and lots of edge cases in between. The sensible way to deal with edge cases is to use more words to elaborate what’s going on. (“Is chess a sport?” “Well, it’s like a sport in such-and-such respects but it also has so-and-so properties which are not very sport-like.” That’s a good response! No need for philosophizing.)
That’s how I’m using “veridicality” (≈ aboutness) in this series. I defined the term in Post 1 and am using it regularly, because I think there are lots of central cases where it’s clearly useful. There are also plenty of edge cases, and when I hit an edge case, I just use more words to elaborate exactly what’s going on. [Copying from Post 1:] For example, suppose intuitive concept X faithfully captures the behavior of algorithm Y, but X is intuitively conceptualized as a spirit floating in the room, rather than as an algorithm within the Platonic, ethereal realm of algorithms. Well then, I would just say something like: “X has good veridical correspondence to the behavior of algorithm Y, but the spirit- and location-related aspects of X do not veridically correspond to anything at all.” (This is basically a real example—it’s how some “awakened” (Post 6) people talk about what I call conscious awareness in this post.) I think you want “aboutness” to be something more fundamental than that, and I think that you’re wrong to want that.
I don’t need a “theory” to explain how a “hypothetical” learning algorithm can build a generative model that can represent this kind of information in its latent variables, and draw appropriate inferences.
Sure, but we would still need a separate explanation if we want to understand how representation/reference works in a model (or in the brain) itself. If we are interested in that, of course. It could be interesting from the standpoint of philosophy of mind, philosophy of language, linguistics, cognitive psychology, and of course machine learning interpretability.
If, when you run these algorithms, you wind up with all kinds of edge cases where it’s unclear what is “about” what, (and you do), then that’s a sign that you should not be treating “aboutness” as a bedrock principle in the first place.
I don’t think we did run into any edge cases of representation so far where something partially represents or is partially represented, like chess is partially sport-like. Representation/reference/aboutness doesn’t seem a very vague concept. Apparently the difficulty of finding an adequate definition isn’t due to vagueness.
That being said, it’s clearly not necessary for your theory to cover this topic if you don’t find it very interesting and/or you have other objectives.
What you’re saying is basically: sometimes we know some aspects of a thing, but don’t know other aspects of it. There’s a thing in a suitcase. Well, I know where it is (in the suitcase), and a bit about how big it is (smaller than a bulldozer), and whether it’s tangible versus abstract (tangible). Then there are other things about it that I don’t know, like its color and shape. OK, cool. That’s not unusual—absolutely everything is like that. Even things I’m looking straight at are like that. I don’t know their weight, internal composition, etc.
I don’t need a “theory” to explain how a “hypothetical” learning algorithm can build a generative model that can represent this kind of information in its latent variables, and draw appropriate inferences. It’s not a hypothetical! Any generative model built by a predictive learning algorithm will actually do this—it will pick up on local patterns and extrapolate them, even in the absence of omniscient knowledge of every aspect of the thing / situation. It will draw inferences from the limited information it does have. Trained LLMs do this, and an adult cortex does it too.
I think you’re going wrong by taking “aboutness” to be a bedrock principle of how you’re thinking about things. These predictive learning algorithms and trained models actually exist. If, when you run these algorithms, you wind up with all kinds of edge cases where it’s unclear what is “about” what, (and you do), then that’s a sign that you should not be treating “aboutness” as a bedrock principle in the first place. “Aboutness” is like any other word / category—there are cases where it’s clearly a useful notion, and cases where it’s clearly not, and lots of edge cases in between. The sensible way to deal with edge cases is to use more words to elaborate what’s going on. (“Is chess a sport?” “Well, it’s like a sport in such-and-such respects but it also has so-and-so properties which are not very sport-like.” That’s a good response! No need for philosophizing.)
That’s how I’m using “veridicality” (≈ aboutness) in this series. I defined the term in Post 1 and am using it regularly, because I think there are lots of central cases where it’s clearly useful. There are also plenty of edge cases, and when I hit an edge case, I just use more words to elaborate exactly what’s going on. [Copying from Post 1:] For example, suppose intuitive concept X faithfully captures the behavior of algorithm Y, but X is intuitively conceptualized as a spirit floating in the room, rather than as an algorithm within the Platonic, ethereal realm of algorithms. Well then, I would just say something like: “X has good veridical correspondence to the behavior of algorithm Y, but the spirit- and location-related aspects of X do not veridically correspond to anything at all.” (This is basically a real example—it’s how some “awakened” (Post 6) people talk about what I call conscious awareness in this post.) I think you want “aboutness” to be something more fundamental than that, and I think that you’re wrong to want that.
Sure, but we would still need a separate explanation if we want to understand how representation/reference works in a model (or in the brain) itself. If we are interested in that, of course. It could be interesting from the standpoint of philosophy of mind, philosophy of language, linguistics, cognitive psychology, and of course machine learning interpretability.
I don’t think we did run into any edge cases of representation so far where something partially represents or is partially represented, like chess is partially sport-like. Representation/reference/aboutness doesn’t seem a very vague concept. Apparently the difficulty of finding an adequate definition isn’t due to vagueness.
That being said, it’s clearly not necessary for your theory to cover this topic if you don’t find it very interesting and/or you have other objectives.