Comment by famouswaffles
10 months ago
>I don't look at a stump and ascertain its chairness with a confidence of 85%
But i think you did. Not consciously, but i think your brain definitely did.
https://www.nature.com/articles/415429a https://pubmed.ncbi.nlm.nih.gov/8891655/
These papers don't appear to say that: the first one describes the behavior as statistically optimal, which is exactly what you'd expect for a sound set of defeasible relations.
Or intuitively: my ability to determine whether a bird flies or not is definitely going to be statistically optimal, but my underlying cognitive process is not itself inherently statistical: I could be looking at a penguin and remembering that birds fly by default except when they're penguins, and only then if the penguin isn't wearing a jetpack. That's a non-statistical set of relations, but its external observation is modeled statistically.
>which is exactly what you'd expect for a sound set of defeasible relations.
This is a leap. While a complex system of rules might coincidentally produce behavior that looks statistically optimal in some scenarios, the paper (Ernst & Banks) argues that the mechanism itself operates according to statistical principles (MLE), not just that the outcome happens to look that way.
Moreover, it's highly unlikely, bordering on impossible, to reduce the situations the brain deals with even on a daily basis into a set of defeasible statements.
Example: Recognizing a "Dog"
Defeasible Attempt: is_dog(X) :- has_four_legs(X), has_tail(X), barks(X), not is_cat(X), not is_fox(X), not is_robot_dog(X).
is_dog(X) :- has_four_legs(X), wags_tail(X), is_friendly_to_humans(X), not is_wolf(X).
How do you define barks(X) (what about whimpers, growls? What about a dog that doesn't bark?)? How do you handle breeds that look very different (Chihuahua vs. Great Dane)? How do you handle seeing only part of the animal? How do you represent the overall visual gestalt? The number of rules and exceptions quickly becomes vast and brittle.
Ultimately, the proof as they say, is in the pudding. By the way, the CyC we are all talking about here is non-monotonic. https://www.cyc.com/wp-content/uploads/2019/07/First-Orderiz...
If you've tried something for decades and it's not working, and it doesn't even look like it's working and experiments with the brain suggest probabilistic inference and probabilistic inference machines work much better than the alternatives ever did, you have to face the music.
> How do you define barks(X) (what about whimpers, growls? What about a dog that doesn't bark?)? How do you handle breeds that look very different (Chihuahua vs. Great Dane)? How do you handle seeing only part of the animal? How do you represent the overall visual gestalt? The number of rules and exceptions quickly becomes vast and brittle.
This is the dimensionality mentioned in the adjacent post, and it's true of a probabilistic approach as well: an LLM trained on descriptions of dogs is going to hallucinate when an otherwise sensible query about dogs doesn't match its training. As others have said more elegantly than I will, this points to a pretty different cognitive model than humans have; human beings can (and do) give up on a task.
(I feel like I've had to say this a few times in threads now: none of this is to imply that Cyc was a success or would have worked.)
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