Larks comments on Taking Ideas Seriously - Less Wrong
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Another way of saying it is that human beings can solve any problem that can be solved. Does that help?
Careful here - as I mentioned above, evidence never supports a theory, it just provides a ready stock of criticisms of rival theories. Let me give you an argument: If you hold that human beings are not universal knowledge creators, then you are saying that human knowledge creation processes are limited in some way, that there is some knowledge we cannot create. You are saying that humans can create a whole bunch of knowledge but whole realms of other knowledge are off limits to us. How does that work? Knowledge enables us to expand our abilities and that in turn enables us to create new knowledge and so on. Whatever this knowledge we can't create is, it would have to be walled off from all this other expanding knowledge in a rather special way. How do you build a knowledge creation machine that only has the capability to create some knowledge? That would seem much much more difficult than creating a fully universal machine.
I don't know what point Elliot was answering here, but I guess he is saying that humans are universal Turing Machines and illustrating that. He is saying that humans are universal in the sense that they can compute anything that can be computed. That is a different notion of universality to the one under discussion here (though there is a connection between the two types of universality). Elliot agrees that humans are universal knowledge creators and has written a lot about it (see, for example, his posts on The Fabric of Reality list).
'Conjectures and refutations' is an evolutionary process. The general methodology (or strategy, if you prefer) is: When faced with a problem try to come up with conjectural explanations to solve the problem and then criticise them until you find one (and only one) that cannot be knocked down by any known criticism. Take that as your tentative solution. I guess what you are looking for is an explanation of how human conjecture engines work? That is an unsolved problem. We do know some things, eg: no induction is involved.
Explanations are valuable: they help you understand something. Are you looking for an explanation of how we generate "explanations"? Again, unsolved problem.
It's not really different. It's something that people believe is true that in fact isn't. Hume was the first to realize that there was a "problem of induction" and philosophers have for years and years been trying to justify induction. It took Karl Popper to realize that induction isn't actually how we create knowledge at all: induction is a myth.
Yes, you are called "Less Wrong" after all! I was off-beam with that.
Actually, I am quite familiar with the Bayesian conception of probability. I just don't think probability has a role in the realm of epistemology. Evidence does not make a theory more probable, not even from a subjective point of view. What evidence does, as I have said, is provide a stock of criticisms against rival theories. Also, evidence only goes so far: what really matters is how theories stand up to criticism as explanations. Evidence plays a role in that. I am quite happy to talk about the probability of events in the world, but events are different from explanatory theories. Apples and oranges.
What about the problem of building pyramids on alpha-centuri by 2012? We can't, but aliens living there could.
More pressingly though, I don't see why this is important. Have we been basing our arguments on an assumption that there are problems we can't solve? Is there any evidence we can solve all problems without access to arbitrarily large amounts of computational power? Something like AIXI can solve pretty much anything, but not relevantly.
How about a neural network that can't learn XOR?
The manner in which explanations are knocked down seems under-specified, if you're not doing Bayesian updating.
Nope, I just don't know what in particular you mean by 'explanation'. I know what the word means in general, but not your specific conception.
Well, that's different from there being no such thing as a probability that a theory is true: your initial assertion implied that the concept wasn't well defined, whereas now you just mean it's irrelevant. Either way, you should probably produce some actual arguments against Jaynes's conception of probability.
Meta: You want to reply directly to a post, not its descendants, or the other person won't get a notification. I only saw your post via the Recent Posts list.
Also, it's no good telling people that they can't use evidence to support their position because it contradicts your theory when the other people haven't been convinced of your theory.
Criticism enables us to see flaws in explanations. What is under-specified about finding a flaw?
In your way, you need to come up with criticisms and also with probabilities associated with those criticisms. Criticisms of real world theories can be involved and complex. Isn't it enough to expose a flaw in an explanatory theory? Must one also go to the trouble of calculating probabilities - a task that is surely fraught with difficulty for any realistic idea of criticism? You're adding a huge amount of auxilliary theory and your evaluation is then also dependent on the truth of all this auxilliary theory.
My conception is the same as the general one.
You don't seem to be actually saying very much then; is LW really short on explanations, in the conventional sense? Explanation seems well evidenced by the last couple of top level posts. Similarly, do we really fail to criticise one another? A large number of the comments seem to be criticisms. If you're essentially criticising us for not having learn rationality 101 - the sort of rationality you learn as a child of 12, arguing against god - then obviously it would be a problem if we didn't bare in mind the stuff. But without providing evidence that we succumb to these faults, it's hard to see what the problem is.
Your other points, however, are substantive. If humans could solve any problem, or it was impossible to design an agent which could learn some but not all things, or confirmation didn't increase subjective plausibility, these would be important claims.