Hi. I'm Gareth McCaughan. I've been a consistent reader and occasional commenter since the Overcoming Bias days. My LW username is "gjm" (not "Gjm" despite the wiki software's preference for that capitalization). Elsewehere I generally go by one of "g", "gjm", or "gjm11". The URL listed here is for my website and blog, neither of which has been substantially updated in about the last four years. I live near Cambridge (UK) and work for a small technology company in Cambridge. My business cards say "mathematician" but in practice my work is a mixture of simulation, data analysis, algorithm design, software development, problem-solving, and whatever random engineering no one else is doing. I am married and have a daughter born in mid-2006. The best way to contact me is by email: firstname dot lastname at pobox dot com. I am happy to be emailed out of the blue by interesting people. If you are an LW regular you are probably an interesting person in the relevant sense even if you think you aren't.
If you're wondering why some of my old posts and comments are at surprisingly negative scores, it's because for some time I was the favourite target of old-LW's resident neoreactionary troll, sockpuppeteer and mass-downvoter.
My personal theory is that not all talk is signalling, but almost all talk about signalling is. (It signals "I am smart, sophisticated, not easily fooled, and willing to face uncomfortable realities; I see below the carefully groomed surface of things to the ugliness beneath.")
In the particularly prominent case of Literal Robin Hanson, it seems possibly significant that the uncomfortable realities he uncovers are generally much more uncomfortable for one of the two major political factions in the US than for the other, and that the "other" one is the one responsible for a lot of his funding over the years (though I think he may no longer be affiliated with the Mercatus Center now?).
(Only possibly significant, and I do actually mean that. Obviously things that are politically convenient for the person saying them can also be true.)
A probability measure is a measure (on a -algebra on a set ) such that .
A measure on a -algebra is a function with properties like "if then " etc.; the idea is that the elements of are the subsets of that are well-enough behaved to be "measurable" and then if is such a subset then says how big is.
A -algebra on a set is a set of subsets of that (1) includes all-of-, (2) whenever it includes a set also includes its complement , and (3) whenever it includes all of countably many sets also includes their union.
And now probability theory is the study of probability measures. (So the measure-theoretic definition of "probability" would be "anything satisfying the formal properties of a probability measure", just as the mathematician's definition of "vector" is "anything lying in a vector space".)
"Bayesian" probability theory doesn't disagree with any of that; it just says that one useful application for (mostly the more elementary bits of) the theory of probability measures is to reasoning under uncertainty, where it's useful to quantify an agent's beliefs as a probability measure. Here is the set of ways the world could be; is something like the set of sets of ways the world could be that can be described by propositions the agent understands, or the smallest -algebra containing all of those; , more commonly denoted or or or something of the sort, gives you for any such set of ways the world could be a number quantifying how likely the agent thinks it is that the actual state of the world is in that set.
You can work with probability measures even if you think that it's inappropriate to use them to quantify the beliefs of would-be rational agents. I guess that's PP's position?
I assume it means an image used in the training process by which the robots learned to recognize things.
You don't need anyone's forgiveness. But it turns out that quantifying degrees of belief is useful sometimes, and that representing them as numbers from 0 to 1 that behave like probabilities is a good way to do that. (There are theorems that kinda-sorta say it's the only way to do that, if you want various nice-sounding things to be true, but how much you care about those nice-sounding things is up to you.) So you may be missing out on some useful thinking tools.
In the space of four frames, today's SMBC comic touches on unfriendly AI, fun theory, and overfitting in machine learning.
No one is erasing other people's comments here unless they're outright abusive.
As for the "irreducible complexity" argument, you may notice that it has convinced approximately zero percent of actual biologists. You may find "they're all brainwashed atheists so completely under Satan's thumb that they can't form rational opinions" a more convincing explanation for that than "the argument is actually not very strong", but I don't agree.
(I agree with the biologists; I think Behe's argument is bad. But I don't think arguing about that argument is particularly on topic here.)
Also, no one is claiming that Francis Bacon is anyone's moral foundation. I think you may not have been reading what Scott wrote very carefully.
It's true that lawters aren't required to take every client who comes along, but I think generally the legal profession strongly encourages them to be willing to take unattractive cases. For instance, the ABA Model Code of Professional Responsibility has various things to say, of which I've excerpted the bits that seem to me most important (on both sides of the question):
A lawyer is under no obligation to act as adviser or advocate for every person who may wish to become his client; but in furtherance of the objective of the bar to make legal services fully available, a lawyer should not lightly decline proffered employment. The fulfillment of this objective requires acceptance by a lawyer of his share of tendered employment which may be unattractive both to him and the bar generally.
[...]
When a lawyer is appointed by a court or requested by a bar association to undertake representation of a person unable to obtain counsel, whether for financial or other reasons, he should not seek to be excused from undertaking the representation except for compelling reasons. Compelling reasons do not include such factors as the repugnance of the subject matter of the proceeding, the identity or position of a person involved in the case, the belief of the lawyer that the defendant in a criminal proceeding is guilty, or the belief of the lawyer regarding the merits of the civil case.
So they don't quite say that lawyers should never refuse to represent clients just because they think they're guilty. But they do say that lawyers should be willing to take "unattractive" cases, and that if a court assigns a lawyer to represent someone who can't afford to pay for his own lawyer then that lawyer shouldn't refuse just because they think the client is guilty.
So my earlier statement goes too far, but I think it's more right than wrong: in general lawyers aren't supposed to refuse to defend you just because they think you're probably guilty. Even though they are allowed to refuse to defend you.
What do you mean by "knowing that both ways are legit"? Only one way is legit: when someone comes to you needing defence and willing to pay your fees, you defend them.
(I think the actual system is a little different: a lawyer isn't expected to defend their client if they're sure the client is guilty; in that case they would ask them to find another lawyer, or something. But that isn't because those clients don't deserve defending, it's because they deserve defending better than someone who's sure they're guilty is likely to manage.)
It's worth noting that what someone puts on a sign doesn't necessarily indicate what they really care most about, especially if what's on the sign is more socially acceptable than what they really care about. So I don't think the findings here are inconsistent with what annacaffeina says. (They are probably, albeit not very strong, evidence against what she says, though. Only "probably" because it could be that the other sign-slogans -- especially the more generically-political ones -- are evidence about the sort of person waving the sign; maybe some of those slogans are more characteristic of wealthier people who want the service industry serving them again than of poorer service-industry employees who want to be at work again.)
Relatedly, in the scenario (in some utterly absurd counterfactual world entirely unlike the real world) where agents sometimes misrepresent the evidence in a direction that favours their actual beliefs, it seems like the policy described here might well do better than the policy of updating fully on all evidence you're presented with.
Given the limitations of our ability to investigate others' honesty, it's possible that the only options are factionalism or naivety and that the latter produces worse results than factionalism; e.g., if we happen to start with more people favouring (A,A,A) than (B,B,B) then rejecting "distrust those who disagree" may end up with everyone in the (A,A,A) corner, which is probably worse than having factions in all eight corners if the reality is (B,B,B).
As Zack says, what we want is a degree of trust that matches agents' trustworthiness. But that may be extremely hard to obtain, and if all agents are somewhat untrustworthy (but some happen to be right so that their untrustworthiness does little harm) then having trust matching trustworthiness may produce exactly the sort of factional results reported here.
So I think the most interesting question is: Are there strategies that, even when agents may be untrustworthy in their reporting of the evidence, manage to converge to the truth over a reasonable portion of the space of untrustworthiness and actual-evidence-strength? My guesses: (1) yes, there kinda are, and the price they pay instead of factionalism is slowness; (2) if there is enough untrustworthiness relative to the actual strength of evidence then no strategy will give good results; (3) there are plenty of questions in the real world for which #2 holds. But I'm not terribly confident about any of those guesses.