LessWrong 2.0 Reader

Same. It would take incredible effort to find one person I reasonably connect with each year. 

So much of this is just location. I've met 100s of people over the last few years. Nearly all either over 40 with kids, or those kids. I've connected with many, maybe 10%, on a pretty good level. That doesn't help with dating at all.

I just really, really don't want it to be the case that he only answer is: move to NY, SF, or Seattle, becuase I really like it here.

However, though dominance is hard-coded, it seems like something of a simple evolved hack to avoid costly fights among relatively low-cognitive-capability agents; it does not seem like the sort of thing which more capable agents (like e.g. future AI, or even future more-intelligent humans) would rely on very heavily.

This seems exactly reversed to me. It seems to me that since dominance underlies defense, law, taxes and public expenditure, it will stay crucial even with more intelligent agents. Conversely, as intelligence becomes "too cheap to meter", "getting what you want" will become less bottlenecked on relevant insights, as those insights are always available.

I use Google Chrome on Ubuntu Budgie and it does look to me like both the font and the font size changed.

Well, let’s see. Calibri is a humanist sans; Gill Sans is technically also humanist, but more more geometric in design. Geometric sans fonts tend to be less readable when used for body text.

Gill Sans has a lower x-height than Calibri. That (obviously) is the cause of all the “the new font looks smaller” comments.

(A side-by-side comparison of the fonts, for anyone curious, although note that this is Gill Sans MT Pro, not Gill Sans Nova, so the weight [i.e., stroke thickness] will be a bit different than the version that LW now uses.)

Now, as far as font rendering goes… I just looked at the site on my Windows box (adjusting the font stack CSS value to see Gill Sans Nova again, since I see you guys tweaked it to give Calibri priority)… yikes. Yeah, that’s not rendering well at all. Definitely more blurry than Calibri. Maybe something to do with the hinting, I don’t know. (Not really surprising, since Calibri was designed from the beginning to look good on Windows.) And I’ve got a hi-DPI monitor on my Windows machine…

Interestingly, the older version of Gill Sans (seen in the demo on my wiki, linked above) doesn’t have this problem; it renders crisply on Windows. (Note that this is not the flawed, broken-kerning version of the font that comes with Macs!)

I also notice that the comment font size is set to… 15.08px. Seems weird? Bumping it up to 16px improves things a bit, although it’s still not amazing.

If you can switch to the older (but not broken) version of Gill Sans, that’d be my recommendation.

If you can’t… then one option might be to check out one of the many similar fonts to see if perhaps one of them renders better on Windows while still having matching metrics.

Sure, I was just responding to this literal quote: 

Couldn't you please just set the comment font to the same as the post font?

I am confident the average user experience would become worse if you just replaced the comment font with the body font)

Yeah, I agree with that, but that’s because of a post body font that wasn’t chosen for suitability for comments also. If you pick, to begin with, a font that works for both, then it’ll work for both.

… of course, if you don’t think that any of the GW themes’ fonts work for both, then never mind, I guess. (But, uh, frankly I find that to be a strange view. But no accounting for taste, etc., so I certainly can’t say it’s wrong, exactly.)

As someone who's gambled professionally, I believe the (Chesterton's) fence around betting for normies exists because most bets are essentially scams, which is why I'm entirely okay knocking it down for LWers. Let me elaborate.

Probability is complicated and abstract. Not only that, human intuition is really bad at it. Nearly all "bets" throughout our modern history have not been the kind of skin-in-the-game prediction competition we're praising on lesswrong - they've been predatory. One person who understands probability using emotional and logical minipulation to take someone else's money, who doesn't.

Society protects people with taboos. "Betting is icky" is a meme that can easily spread, and will quickly reproduce, becuase it's adaptive in this betting environment. [Dissertation about Bayesian reasoning, calibration, and the Kelley Criterion] is NOT a meme that can easily spread, because it's far too complex and long, and thus it will not reproduce (even though it is also adaptive).

Or at least, it can't spread in the normie population, but it CAN on LessWrong, which is why, on LessWrong, most bets are not scams. They are, in fact, what the scammers falsly proclaimed their own bets to be - friendly competitions wherein two people who disagree about the future both put skin in the game.

The sportsbooks and casinos we have today are predators. From their celebrity endorsements, to the way they form their commercials, to their messaging around winning (and especially parlays), they effectively lie about what they're selling while trying to create addicts. I've engaged with many people across the betting experience spectrum (from other winners, to big losers, to smart people, who were small losers, and realized they needed to quit), and it's pretty clear to me that "betting = icky" is a reasonable idea, even today The fence around it is not Chesterton's, though. It's there to help regular people avoid a certain species of predator gunning for their capital.

We can safely knock it down on here.

I'm often surprised how little people notice, adapt to, or even punish self deception. It's not very hard to detect when someone's deceiving them self, people should notice more and disincentivise that

I do think that these things are relevant to 'compute it takes to get to a given capability level'.

In practice, there are no 2e23 FLOPs models that cost $300K to train that are anywhere close to Llama-3-405B smart. If there were such models at leading labs (based on unpublished experimental results and more algorithmic insights), they would be much smarter than Llama3-405B when trained with 8e25 FLOPs they have to give, rather than the reference 2e23 FLOPs. Better choice of ways of answering questions doesn't get us far in the actual technical capabilities.

(Post-training like o1 is a kind of "better choice of ways of answering questions" that might help, but we don't know how much compute it saves. Noam Brown gestures at 100,000x from his earlier work, but we haven't seen Llama 4 yet, it might just spontaneously become capable of coherent long reasoning traces as a result of more scale, the bitter lesson making Strawberry Team's efforts moot.)

Many improvements observed at smaller scale disappear at greater scale, or don't stack with each other. Many papers have horrible methodologies, plausibly born of scarcity of research compute, that don't even try (or make it possible) to estimate the compute multiplier. Most of them will be eventually forgotten, for a good reason. So most papers that seem to demonstrate improvements are not strong evidence for the hypothesis of a 1000x cumulative compute efficiency improvement, while this hypothesis predicts observations about what's actually already possible in practice that we are not getting, strong evidence against it. There are multiple competent teams that don't have Microsoft compute, and they don't win over Llama-3-405B, which we know doesn't have all of these speculative algorithmic improvements and uses 4e25 FLOPs (2.5 months on 16K H100s rather than 1.5 months on 128 H100s for 2e23 FLOPs).

In other words, the importance of Llama-3-405B for the question about speculative algorithmic improvements is that the detailed report shows it has no secret sauce, it merely competently uses about as much compute as the leading labs in very conservative ways. And yet it's close in capabilities to all the other frontier models. Which means the leading labs don't have significantly effective secret sauce either, which means nobody does, since the leading labs would've already borrowed it if it was that effective.

There's clearly a case in principle for it being possible to learn with much less data, anchoring to humans blind from birth. But there's probably much more compute happening in a human brain per the proverbial external data token. And a human has the advantage of not learning everything about everything, with greater density of capability over encyclopedic knowledge, which should help save on compute.

Thanks, that’s helpful!

(Fwiw, I don’t find the ‘caring a tiny bit’ story very reassuring, for the same reasons [LW · GW] as Wei Dai, although I do find the acausal trade story for why humans might be left with Earth somewhat heartening. (I’m assuming that by ‘game-theoretic reasons’ you mean acausal trade.))