Zack_M_Davis(Zack M. Davis)

Karma: 8,519

Zack_M_Davis 15 Apr 2021 20:06 UTC
4 points
in reply to: Viliam’s comment on: The consequentialist case for social conservatism, or “Against Cultural Superstimuli”

actual trans people, or perverts willing to pretend to be trans if it allows them to sneak into female toilets

It gets worse: if the dominant root cause of late-onset gender dysphoria in males is actually a paraphilic sexual orientation, this is a false dichotomy! (It’s not “pretending” if you sincerely believe it.)

Zack_M_Davis 15 Apr 2021 17:51 UTC
6 points
in reply to: gjm’s comment on: The consequentialist case for social conservatism, or “Against Cultural Superstimuli”
So, I started writing an impassioned reply to this (draft got to 850 words), but I’ve been trying to keep my culture war efforts off this website (except for the Bayesian philosophy-of-language sub-campaign that’s genuinely on-topic), so I probably shouldn’t take the bait. (If nothing else, it’s not a good use of my time when I have lots of other things to write for my topic-specific blog.)

If I can briefly say one thing without getting dragged into a larger fight, I would like to note that aggressively encouraging people to consider whether they might be trans is potentially harmful if the popular theory of what “trans” is, is actually false; even if you’re a liberal who wants people to have the freedom to decide how to live their lives unencumbered by oppressive traditions, people might make worse decisions in an environment full of ideologically-fueled misinformation. (I consider trans activism to have been extremely harmful to me and people like me on this account.)

Zack_M_Davis 13 Apr 2021 18:15 UTC
2 points
in reply to: Gerald Monroe’s comment on: A Brief Review of Current and Near-Future Methods of Genetic Engineering
The effective altruist case for regime change??

Zack_M_Davis 1 Apr 2021 16:39 UTC
2 points
on: Why We Launched LessWrong.SubStack
Has anyone tried buying a paid subscription? I would assume the payment attempt just fails unless your credit card has a limit over $60,000, but I’m scared to try it.

Communication Requires Common Interests or Differential Signal Costs

Zack_M_Davis26 Mar 2021 6:41 UTC

22 points

7 comments3 min readLW link

Zack_M_Davis 23 Mar 2021 1:21 UTC
2 points
on: On future people, looking back at 21st century longtermism

I imagine them going: “Whoa. Basically all of history, the whole thing, all of everything, almost didn’t happen.”

But this kind of many-worldeaters thinking is already obsolete. It won’t be that it “almost” didn’t happen; it’s that it mostly didn’t happen. (The future will have the knowledge and compute to say what the distribution of outcomes was for a specified equivalence class of Earth-analogues across the multiverse.)

Zack_M_Davis 20 Mar 2021 1:03 UTC
4 points
in reply to: johnswentworth’s comment on: Unnatural Categories Are Optimized for Deception
This gave me a blog story idea!

Zack_M_Davis 19 Mar 2021 23:39 UTC
2 points
in reply to: Viliam’s comment on: Viliam’s Shortform
YouTube lets me watch the video (even while logged out). Is it a region thing?? (I’m in California, USA). Anyway, the video depicts

dirt, branches, animals, &c. getting in Rapunzel’s hair as it drags along the ground in the scene when she’s frolicking after having left the tower for the first time, while Flynn Rider offers disparaging commentary for a minute, before delcaring, “Okay, this is getting weird; I’m just gonna go.”

If you want to know how it really ends, check out the sequel series!

Zack_M_Davis 19 Mar 2021 21:22 UTC
2 points
in reply to: johnswentworth’s comment on: Unnatural Categories Are Optimized for Deception
So, I like this, but I’m still not sure I understand where features come from.

Say I’m an AI, and I’ve observed a bunch of sensor data that I’m representing internally as the points (6.94, 3.96), (1.44, -2.83), (5.04, 1.1), (0.07, -1.42), (-2.61, -0.21), (-2.33, 3.36), (-2.91, 2.43), (0.11, 0.76), (3.2, 1.32), (-0.43, -2.67).

The part where I look at this data and say, “Hey, these datapoints become approximately conditionally independent if I assume they were generated by a multivariate normal with mean (2, -1), and covariance matrix [[16, 0], [0, 9]]^[1]; let me allocate a new concept for that!” makes sense. (In the real world, I don’t know how to write a program to do this offhand, but I know how to find what textbook chapters to read to tell me how.)

But what about the part where my sensor data came to me already pre-processed into the list of 2-tuples?—how do I learn that? Is it just, like, whatever transformations of a big buffer of camera pixels let me find conditional independence patterns probably correspond to regularities in the real world? Is it “that easy”??
1. In the real world, I got those numbers from the Python expression ', '.join(str(d) for d in [(round(normal(2, 4), 2), round(normal(-1, 3), 2)) for _ in range(10)]) (using scipy.random.normal). ↩︎

Zack_M_Davis 19 Mar 2021 18:18 UTC
6 points
in reply to: johnswentworth’s comment on: Unnatural Categories Are Optimized for Deception
(Thinking out loud about how my categorization thing will end up relating to your abstraction thing …)

200-word recap of my thing: I’ve been relying on our standard configuration space metaphor, talking about running some “neutral” clustering algorithm on some choice of subspace (which is “value-laden” in the sense that what features you care about predicting depends on your values). This lets me explain how to think about dolphins: they simultaneously cluster with fish in one subspace, but also cluster with other mammals in a different subspace, no contradiction there. It also lets me explain what’s wrong with a fake promotion to “Vice President of Sorting”: the “what business cards say” dimension is a very “thin” subspace; if it doesn’t cluster with anything else, then there’s no reason we care. As my measurement of what makes a cluster “good”, I’m using the squared error, which is pretty “standard”—that’s basically what, say, k means clustering is doing—but also pretty ad hoc: I don’t have a proof of why squared error and only squared error is the right calculation to be doing given some simple deciderata, and it probably isn’t. (In contrast, we can prove that if you want a monotonic, nonnegative, additive measure of information, you end up with entropy: the only free choice is the base of the logarithm.)

What I’m hearing from the parent and your reply to my comment on ”… Ad Hoc Mathematical Definitions?”: talking about looking for clusters in some pre-chosen subspace of features is getting the actual AI challenge backwards. There are no pre-existing features in the territory; rather, conditional-independence structure in the territory is what lets us construct features such that there are clusters. Saying that we want categories that cluster in a “thick” subspace that covers many dimensions is like saying we want to measure information with “a bunch of functions like $X^{2}$ , sin(Y), $e^{X} + 2 X - 1$ , &c., and require that those also be uncorrelated”: it probably works, but there has to be some deeper principle that explains why most of the dimensions and ad hoc information measures agree, why we can construct a “thick” subspace.

To explain why “squiggly”, “gerrymandered” categories are bad, I said that if you needed to make a decision that depended on how big an integer is, categorizing by parity would be bad: the squared-error score quantifies the fact that 2 is more similar to 3 than 12342. But notice that the choice of feature (the decision quality depending on magnitude, not parity) is doing all the work: 2 is more similar to 12342 than 3 in the mod-2 quotient space!

So maybe the exact measure of “closeness” in the space (squared error, or whatever) is a red herring, an uninteresting part of the problem?—like the choice of logarithm in the definition of entropy. We know that there isn’t any principled reason why base 2 or base e is better than any others. It’s just that we’re talking about how uncertainty relates to information, so if we use our standard representation of uncertainty as probabilities from 0 to 1 under which independent events multiply, then we have a homomorphism from multiplication (of probability) to addition (of information), which means you have to pick a base for the logarithm if you want to work with concrete numbers instead of abstract nonsense.

If this is a good analogy, then we’re looking for some sort of deeper theorem about “closeness” and conditional independence “and stuff” that explains why the configuration space metaphor works—after which we’ll be able to show that the choice of metric on the “space” will be knowably arbitrary??

Zack_M_Davis 16 Mar 2021 6:58 UTC
16 points
on: What’s So Bad About Ad-Hoc Mathematical Definitions?
This is related to something I never quite figured out in my cognitive-function-of-categorization quest. How do we quantify how good a category is at “carving reality at the joints”?

Your first guess would be “mutual information between the category-label and the features you care about” (as suggested in the Job parable in April 2019′s “Where to Draw the Boundaries?”), but that actually turns out to be wrong, because information theory has no way to give you “partial credit” for getting close to the right answer, which we want. Learning whether a number between 1 and 10 inclusive is even or odd gives you the same amount of information (1 bit) as learning whether it’s over or under 5½, but if you need to make a decision whose goodness depends continuously on the magnitude of the number, then the high/low category system is useful and the even/odd system is not: we care about putting probability-mass “close” to the right answer, not just assigning more probability to the exact answer.

In January 2021′s “Unnatural Categories Are Optimized for Deception”, I ended up going with “minimize expected squared error (given some metric on the space of features you care about)”, which seems to work, but I didn’t have a principled justification for that choice, other than it solving my partial-credit problem and it being traditional. (Why not the absolute error? Why not exponentiate this feature and then, &c.?)

Another possibility might have been to do something with the Wasserstein metric, which reportedly fixes the problem of information theory not being able to award “partial credit”. (The logarithmic score is the special case of the Kullback–Leibler divergence when the first distribution assigns Probability One to the actual answer, so if there’s some sense in which Wasserstein generalizes Kullback–Leibler for partial credit, then maybe that’s what I want.)

My intuition doesn’t seem adequate to determine which (or something else) formalization captures the true nature of category-goodness, to which other ideas are a mere proxy.

Zack_M_Davis 13 Mar 2021 4:43 UTC
4 points
in reply to: TurnTrout’s comment on: Trapped Priors As A Basic Problem Of Rationality
Maybe it’s unfortunate that the same word is overloaded to cover “prior probability” (e.g., probability 0.2 that dogs are bad), and “prior information” in the sense of “a mathematical object that represents all of your starting information plus the way you learn from experience.”

Zack_M_Davis 12 Mar 2021 6:21 UTC
2 points
on: Where does the phrase “central example” come from?
jinx

Zack_M_Davis 12 Mar 2021 6:20 UTC
7 points
on: Where does the phrase “central example” come from?
Implied by “the noncentral fallacy”? (I’m surprised at the search engine results (Google, DuckDuckGo); I didn’t realize this was a Less Wrong-ism.)

Zack_M_Davis 10 Mar 2021 6:11 UTC
15 points
on: Defending the non-central fallacy

And a more natural clustering would reflect that.

What subspace are you doing your clustering in, though? Both the pro-capital-punishment and anti-capital-punishment side should be able to agree that capital punishment and “central” murder are similar in the “intentional killing of a human” aspects, but differ in the “motives and decision mechanism of the killer” aspects (where the “central” murderer is an individual, rather than a judicial institution). Each side has an incentive to try to bind the murder codeword in their shared language to a subspace that makes their own side’s preferred policy look natural.

Zack_M_Davis 7 Mar 2021 19:34 UTC
15 points
on: Unconvenient consequences of the logic behind the second law of thermodynamics

if entropy is decreasing maybe your memory is just working “backwards”

I think the key to the puzzle is likely to be here: there’s likely to be some principled reason why agents embedded in physics will perceive the low-entropy time direction as “the past”, such that it’s not meaningful to ask which way is “really” “backwards”.

Zack_M_Davis 3 Mar 2021 4:12 UTC
21 points
on: Firming Up Not-Lying Around Its Edge-Cases Is Less Broadly Useful Than One Might Initially Think
Cade Metz hadn’t had this much trouble with a story in years. Professional journalists don’t get writer’s block! Ms. Tam had rejected his original draft focused on the subject’s early warnings of the pandemic. Her feedback hadn’t been very specific … but then, it didn’t need to be.

For contingent reasons, the reporting for this piece had stretched out over months. He had tons of notes. It shouldn’t be hard to come up with a story that would meet Ms. Tam’s approval.

The deadline loomed. Alright, well, one sentence at a time. He wrote:

In one post, he aligned himself with Charles Murray, who proposed a link between race and I.Q. in “The Bell Curve.”

Metz asked himself: Is this statement actually and literally true?

Yes! The subject had aligned himself with Charles Murray in one post: “The only public figure I can think of in the southeast quadrant with me is Charles Murray.”.

In another, he pointed out that Mr. Murray believes Black people “are genetically less intelligent than white people.”

Metz asked himself: Is this statement actually and literally true?

Yes! The subject had pointed that out in another post: “Consider Charles Murray saying that he believes black people are genetically less intelligent than white people.”

Having gotten started, the rest of the story came out easily. Why had he been so reluctant to write the new draft, as if in fear of some state of sin? This was his profession—to seek out all the news that’s fit to print, and bring it to the light of the world!

For that was his mastery.

Zack_M_Davis 27 Feb 2021 20:35 UTC
14 points
in reply to: David Hornbein’s comment on: Anna and Oliver discuss Children and X-Risk

being the-sort-of-person-who-chooses-to-have-kids

What years were most of these biographies about? Sexual marketplace and family dynamics have changed a lot since, say, 1970ish. (Such that a lot of people today who don’t think of themselves as the-sort-of-person-who-chooses-to-have-kids would absolutely be married with children had someone with their genotype grown up in an earlier generation.)

Zack_M_Davis 27 Feb 2021 19:32 UTC
18 points
on: Anna and Oliver discuss Children and X-Risk
Two complementary pro-natalist considerations I’d like to see discussed:
- Eugenics! It doesn’t seem like there are any technical barriers to embryo selection for IQ today. If longtermist parents disproportionately become early adopters of this tech in the 2020s, could that help their children be a disproportionate share of up-and-coming AI researchers in the 2040s?
- Escaping our Society’s memetic collapse. We are the children of a memetic brood-parasite strategy. It’s a lot easier to recruit new longtermists out of universal culture than it is from Mormonism, but universal culture triumphed not because its adherents had more children than everyone else, but by capturing the school and media institutions that socialize everyone else’s children: horizontal meme transmission rather than vertical. If social-media-era universal culture is no longer as conducive to Reason as its 20th-century strain, maybe we need to switch to a more Mormon-like strategy (homeschooling, &c.) if we want there to be top reasoners in the 2040s.

Zack_M_Davis 26 Feb 2021 8:26 UTC
4 points
in reply to: Viliam’s comment on: Above the Narrative
Consider adapting this into a top-level post? I anticipate wanting to link to it (specifically for the “smaller audiences offer more slack” moral).

Zack_M_Davis(Zack M. Davis)

Com­mu­ni­ca­tion Re­quires Com­mon In­ter­ests or Differ­en­tial Sig­nal Costs

Communication Requires Common Interests or Differential Signal Costs