This followup (Edit: alkjash’s followup post, not my followup comment) addresses my stated motivation for suggesting that the babble-generator is based on pattern-matching rather than a mere entropy. I had said that there are too many possible ideas for a entropy to generate reasonable ones. For babble to be produced by a random walk along the idea graph is more plausible. It’s not obvious that you couldn’t produce sufficiently high-quality babble with a random-walk along a well-constructed idea-graph.
Now, while I absolutely think the idea graph exists, and I agree that producing babble involves a walk along that graph, I am still committed to the belief that that walk is not random, but is guided by pattern matching. My first reason for holding this belief is introspection: I noticed that ideas are produced by “guess-and-check” (or babble-and-prune) by introspection, and I also noticed that the guessing process is based on pattern matching. That’s fairly weak evidence. My stronger reason for belieiving that babble is produced by pattern matching is that it’s safer to assume that a neurological process is based on pattern matching than random behavior. Neurons are naturally suited to forming pattern-matching machines (please forgive my lay-understanding of cognitive science), and while I don’t see why they couldn’t also form an entropy generator, I don’t suspect that a random walk down the idea graph would be more adaptive than a more “intelligent” pattern matching algorithm.
I also infer that the babble-generator is a pattern matcher from the predictions that makes. If the babble-generator is a random-walk down the idea-graph, then the only way to improve your babble should be to improve your idea graph. If the babble-generator is a pattern-matcher-diected-walk down the idea-graph then you should be able to improve your babble both by training the pattern-matcher well and by improving your idea-graph. Let’s say reading nonfiction impoves your idea graph more effectively than it trains a hypthetical pattern-matcher, and that writing novels trains your pattern-matcher more effectively than it improves your idea-graph. Then if the random-walk hypothesis is true, we should see the same kinds of improvements to babble when we read nonfiction and write novels, but if the pattern-matcher hypothesis is true we should expect different kinds of improvements.
***
I think for the most part we’re talking about the same thing, I’m just suggesting this additional detail of pattern-matching, which has normative consequences (as I sketched out in my previous comment). However I’m not quite sure that we’re talking about the same graphs. You say:
What is the Babble graph? It’s the graph within which your words and concepts are connected. Some of these connections are by rhyme and visual similarity, others are semantic or personal.
I certainly don’t think think that this graph is a graph of words, even though I agree that there can be connections representing syntactic relationships like rhyme. I don’t think that the babble algorithm is “start at some node of the graph, output the word associated with that node, then select a connected node and repeat.” There is an idea-graph, and it’s used in the production of babble, but not like that. I’m not sure if you were claiming that it does, but in case you were, I disagree. I would try to elaborate what role I do think the idea-graph plays in babble generation, but this comment is already getting very long.
I’m curious about the details of your model of this “babble-graph,” You mention that it can create new connections, which suggest to me that the “graph” is actually a static representation of an active process of connection-drawing. I could be convinced that the pattern-matching I’m talking about is actually a separate process which is responsible for forming these connections. But I’m fuzzy on what exactly you mean so I’m not sure that’s even coherent.
I think I was intentionally vague about the things you are emphasizing because I don’t have a higher-resolution picture of what’s going on. I mentioned that “random” means something like “random, biased by the weak, local filter,” but your picture of pattern-matching seems like a better description of the kind of bias that’s actually going on.
Similarly, it’s probably true that there are different levels of Babble going on, at some points you are pattern-matching with literal words, at other points you are using phrases or concepts or entire cached arguments, and I roughly defined the Babble graph to contain all of these things.
I’m inclined to think that the babble you’ve been describing is actually just thoughts, and not linguistic at all. You create thoughts by babble-and-prune and then a separate process converts the thoughts into words. I haven’t thought much about how that process works (and at first inspection I think it’s probably also structured as babble-and-prune), but I think it makes sense to think about it as separate.
If the processes of forming thoughts and phrasing them linguistically were happening at the same level, I’d expect it to be more intuitive to make syntax reflect semantics, like you see in Shakespeare where the phonetic qualities of a character’s speech reflect their personality. Instead, writing like that seems to require System 2 intervention.
But I must admit I’m biased. If I were designing a mind, I’d want to have thought generation uncoupled from sentence generation, but it doesn’t have to actually work that way.
Edit: If generating linguistic-babble happens on a separate level from generating thought-babble, then that has consequences for how to train thought-babble. Your suggestions of playing scrabble and writing haikus would train the wrong babble (nothing wrong with training linguistic-babble, that’s how you become a good writer, but I’m more interested in thought-babble). I think if you wanted to train thought-babble, you’d want to do something like freewriting or brainstorming — rapidly producing a set of related ideas without judgment.
yes, the process that converts thoughts to words is separate
however, caveat: the words are ALSO used for initialization of your concept network/tree, so these two might continue matching closely by default if you don’t do any individual work on improving them
I can’t give you a RCT for proof but I’ve had this idea for at least 7 months now (blog post) so I had lots of time to verify it
yes, training the concept network/tree directly looks completely different from training the verbal network/tree (though on some meta level the process of doing it is the same)
see this as an example of explicit non-verbal training (notes from improving my rationality-related abstract concept network) - the notes are of course in English, but it should be clear enough that this is not the point: e.g. I’m making up many of the words and phrases as I go because it doesn’t matter for the concept network/tree if my verbal language is standard or not
Are you referring to the second half of my comment? Because perhaps I wasn’t clear enough. I’m confused what alkjash means, because some of their references to the babble graph seemed perfectly consistent with my understanding but I got the impression that overall we might not be talking about the same thing.if we are talking about the same thing then that whole section of my comment is irrelevant.
This is a followup to my comment on the previous post.
This followup (Edit: alkjash’s followup post, not my followup comment) addresses my stated motivation for suggesting that the babble-generator is based on pattern-matching rather than a mere entropy. I had said that there are too many possible ideas for a entropy to generate reasonable ones. For babble to be produced by a random walk along the idea graph is more plausible. It’s not obvious that you couldn’t produce sufficiently high-quality babble with a random-walk along a well-constructed idea-graph.
Now, while I absolutely think the idea graph exists, and I agree that producing babble involves a walk along that graph, I am still committed to the belief that that walk is not random, but is guided by pattern matching. My first reason for holding this belief is introspection: I noticed that ideas are produced by “guess-and-check” (or babble-and-prune) by introspection, and I also noticed that the guessing process is based on pattern matching. That’s fairly weak evidence. My stronger reason for belieiving that babble is produced by pattern matching is that it’s safer to assume that a neurological process is based on pattern matching than random behavior. Neurons are naturally suited to forming pattern-matching machines (please forgive my lay-understanding of cognitive science), and while I don’t see why they couldn’t also form an entropy generator, I don’t suspect that a random walk down the idea graph would be more adaptive than a more “intelligent” pattern matching algorithm.
I also infer that the babble-generator is a pattern matcher from the predictions that makes. If the babble-generator is a random-walk down the idea-graph, then the only way to improve your babble should be to improve your idea graph. If the babble-generator is a pattern-matcher-diected-walk down the idea-graph then you should be able to improve your babble both by training the pattern-matcher well and by improving your idea-graph. Let’s say reading nonfiction impoves your idea graph more effectively than it trains a hypthetical pattern-matcher, and that writing novels trains your pattern-matcher more effectively than it improves your idea-graph. Then if the random-walk hypothesis is true, we should see the same kinds of improvements to babble when we read nonfiction and write novels, but if the pattern-matcher hypothesis is true we should expect different kinds of improvements.
***
I think for the most part we’re talking about the same thing, I’m just suggesting this additional detail of pattern-matching, which has normative consequences (as I sketched out in my previous comment). However I’m not quite sure that we’re talking about the same graphs. You say:
I certainly don’t think think that this graph is a graph of words, even though I agree that there can be connections representing syntactic relationships like rhyme. I don’t think that the babble algorithm is “start at some node of the graph, output the word associated with that node, then select a connected node and repeat.” There is an idea-graph, and it’s used in the production of babble, but not like that. I’m not sure if you were claiming that it does, but in case you were, I disagree. I would try to elaborate what role I do think the idea-graph plays in babble generation, but this comment is already getting very long.
I’m curious about the details of your model of this “babble-graph,” You mention that it can create new connections, which suggest to me that the “graph” is actually a static representation of an active process of connection-drawing. I could be convinced that the pattern-matching I’m talking about is actually a separate process which is responsible for forming these connections. But I’m fuzzy on what exactly you mean so I’m not sure that’s even coherent.
Great posts, I wouldn’t mind a part 3!
I think I was intentionally vague about the things you are emphasizing because I don’t have a higher-resolution picture of what’s going on. I mentioned that “random” means something like “random, biased by the weak, local filter,” but your picture of pattern-matching seems like a better description of the kind of bias that’s actually going on.
Similarly, it’s probably true that there are different levels of Babble going on, at some points you are pattern-matching with literal words, at other points you are using phrases or concepts or entire cached arguments, and I roughly defined the Babble graph to contain all of these things.
I’m inclined to think that the babble you’ve been describing is actually just thoughts, and not linguistic at all. You create thoughts by babble-and-prune and then a separate process converts the thoughts into words. I haven’t thought much about how that process works (and at first inspection I think it’s probably also structured as babble-and-prune), but I think it makes sense to think about it as separate.
If the processes of forming thoughts and phrasing them linguistically were happening at the same level, I’d expect it to be more intuitive to make syntax reflect semantics, like you see in Shakespeare where the phonetic qualities of a character’s speech reflect their personality. Instead, writing like that seems to require System 2 intervention.
But I must admit I’m biased. If I were designing a mind, I’d want to have thought generation uncoupled from sentence generation, but it doesn’t have to actually work that way.
Edit: If generating linguistic-babble happens on a separate level from generating thought-babble, then that has consequences for how to train thought-babble. Your suggestions of playing scrabble and writing haikus would train the wrong babble (nothing wrong with training linguistic-babble, that’s how you become a good writer, but I’m more interested in thought-babble). I think if you wanted to train thought-babble, you’d want to do something like freewriting or brainstorming — rapidly producing a set of related ideas without judgment.
Haha, you seem to be on track:
yes, the process that converts thoughts to words is separate
however, caveat: the words are ALSO used for initialization of your concept network/tree, so these two might continue matching closely by default if you don’t do any individual work on improving them
I can’t give you a RCT for proof but I’ve had this idea for at least 7 months now (blog post) so I had lots of time to verify it
yes, training the concept network/tree directly looks completely different from training the verbal network/tree (though on some meta level the process of doing it is the same)
see this as an example of explicit non-verbal training (notes from improving my rationality-related abstract concept network) - the notes are of course in English, but it should be clear enough that this is not the point: e.g. I’m making up many of the words and phrases as I go because it doesn’t matter for the concept network/tree if my verbal language is standard or not
Aren’t you by any chance attacking a strawman?
There is no “privileged” level of models in the brain. Single English words, in particular, are not privileged in any way.
When your models/neural connections are updated, so are your priors for babble.
This is all dead obvious.
Are you referring to the second half of my comment? Because perhaps I wasn’t clear enough. I’m confused what alkjash means, because some of their references to the babble graph seemed perfectly consistent with my understanding but I got the impression that overall we might not be talking about the same thing.if we are talking about the same thing then that whole section of my comment is irrelevant.
See other comments on this post, I think this is sufficiently resolved by now.