Know Your Meme says it started off as video game jargon; my impression is that it's pretty common online outside of that.

"The only reason we're still talking to these people is we need them and we need them now. The problem for these guys is they are that good," a Defense official told Axios ahead of the meeting.

I'm confused. Why would you ever say this before a negotiation :O

How much of a post are you comfortable for AI to write?

One day the AIs can be much better at writing but that day is not today

This seems like a pretty unlikely fallacy, but I agree it's theoretically possible (and ocassionally happens in practice).

The difference between 0 and 1 is significant! And it's very valuable to figure out when the transition point happens, if you can.

PSA: regression to the mean/mean reversion is a statistical artifact, not a causal mechanism.

So mean regression says that children of tall parents are likely to be shorter than their parents, but it also says parents of tall children are likely to be shorter than their children.

Put in a different way, mean regression goes in both directions. 

This is well-understood enough here in principle, but imo enough people get this wrong in practice that the PSA is worthwhile nonetheless.

I think something a lot of people miss about the “short-term chartist position” (these trends have continued until time t, so I should expect it to continue to time t+1) for an exponential that’s actually a sigmoid is that if you keep holding it, you’ll eventually be wrong exactly once.

Whereas if someone is “short-term chartist hater” (these trends always break, so I predict it’s going to break at time t+1) for an exponential that’s actually a sigmoid is that if you keep holding it, you’ll eventually be correct exactly once.

Now of course most chartists (myself included) want to be able to make stronger claims than just t+1, and people in general would love to know more about the world than just these trends. And if you're really good at analysis and wise and careful and lucky you might be able to time the kink in the sigmoid and successfully be wrong 0 times, which is for sure a huge improvement over being wrong once! But this is very hard.

And people who ignore trends as a baseline are missing an important piece of information, and people who completely reject these trends are essentially insane.

Also seems a bit misleading to count something like "one afternoon in Vietnam" or "first day at a new job" as a single data point when it's hundreds of them bundled together?

From a information-theoretic perspective, people almost never refer to a single data point as strictly as just one bit, so whether you are counting only one float in a database or a whole row in a structured database, or also a whole conversation, we're sort of negotiating price. 

I think the "alien seeing a car" makes the case somewhat clearer. If you already have a deep model of cars (or even a shallow one), seeing another instance of a Ford Focus tells you relatively little, but an alien coming across one will get many bits about it, perhaps more than a human spending an afternoon in Vietnam.

EDIT: I noticed that in my examples I primed Claude a little, and when unprimed Claude does not reliably (or usually) get to the answer. However Claude 4.xs are still noticeable in how little handholding they need for this class of conceptual errors, Geminis often takes like 5 hints where Claude usually gets it with one. And my impression was that Claude 3.xs were kinda hopeless (they often don't get it even with short explanations by me, and when they do, I'm not confident they actually got it vs just wanted to agree).

"Most people make the mistake of generalizing from a single data point. Or at least, I do." - SA

When can you learn a lot from one data point? People, especially stats- or science- brained people, are often confused about this, and frequently give answers that (imo) are the opposite of useful. Eg they say that usually you can’t know much but if you know a lot about the meta-structure of your distribution (eg you’re interested in the mean of a distribution with low variance), sometimes a single data point can be a significant update.

This type of limited conclusion on the face of it looks epistemically humble, but in practice it's the opposite of correct. Single data points aren’t particularly useful when you know a lot, but they’re very useful when you have very little knowledge to begin with. If your uncertainty about a variable in question spans many orders of magnitude, the first observation can often reduce more uncertainty than the next 2-10 observations put together[1]. Put another way, the most useful situations for updating massively from a single data point are when you know very little to begin with.

For example, if an alien sees a human car for the first time, the alien can make massive updates on many different things regarding Earthling society, technology, biology and culture. Similarly, an anthropologist landing on an island of a previously uncontacted tribe can rapidly learn so much about a new culture from a single hour of peaceful interaction [2].

Some other examples:

• Your first day at a new job.
• First time visiting a country/region you previously knew nothing about. One afternoon in Vietnam tells you roughly how much things cost, how traffic works, what the food is like, languages people speak, how people interact with strangers.
• Trying a new fruit for the first time. One bite of durian tells you an enormous amount about whether you'll like durian.
• Your first interaction with someone's kid tells you roughly how old they are, how verbal they are, what they're like temperamentally. You went from "I know nothing about this child" to a working model.

Far from idiosyncratic and unscientific, these forms of "generalizing from a single data point" are just very normal, and very important, parts of normal human life and street epistemology. 

This is the point that Douglas Hubbard tries to hammer in repeatedly over the course of his book, How to Measure Anything: You know less than you think you do, and a single measurement can sometimes be a massive update.

[1] this is basically tautological from a high-entropy prior.

[2] I like Monolingual Fieldwork as a demonstration for the possibilities in linguistics: https://www.youtube.com/watch?v=sYpWp7g7XWU&t=2s

The significance, as I read it, is that you can now trust Claude roughly like a reasonable colleague for spotting such mistakes, both in your own drafts and in texts you rely on at work or in life.

I wouldn't go quite this far, at least from my comment. There's a saying in startups, "never outsource your core competency", and unfortunately reading blog posts and spotting conceptual errors of a certain form is a core competency of mine. Nonetheless I'd encourage other Forum users less good at spotting errors (which is most people) to try to do something like this and post posts that seem a little fishy to Claude and see if it's helpful.^[1]

For me, Claude is more helpful for identifying factual errors, and for challenging my own blog posts at different levels (eg spelling, readability, conceptual clarity, logical flow, etc). I wouldn't bet on it spotting conceptual/logical errors in my posts I missed, but again, I have a very high opinion of myself here.

1. ^{^}

   (To be clear I'm not sure the false positives/false negatives ratio is good enough for other people).