(I think the most likely explanation for Mythos is that it's asymmetrically a very big deal. Come to your own conclusions, but don't simply fall back on the "oh this fits the hype pattern" thought terminating cliché.)

Also be aware of what you want to see. If you want the world to fit your narrative, you're more likely construct explanations for that. (In my friend group at least, I feel like most fall prey to this, at least some of the time, including myself. These people are successful and intelligent by most measures.)

Then make a plan to become more disciplined about thinking clearly and probabilistically. Make it a system, not just something you do sometimes. I recommend the book "the Scout Mindset".

Concretely, if one hasn't spent a couple of quality hours really studying AI safety I think one is probably missing out. Dan Hendrycks has a great book.

So it really helps when people explain (1) their context** and (2) their reasoning. Communicating well is harder than people think. Many comments are read by hundreds or more (thousands?) of people, most of whom probably have no idea who we are, what we know, or what we do with our brains on a regular basis. It is generous and considerate to other people to slow down and really explain where we're coming from.

So, when I read "most people use Taylor approximations"...

1. my first question is "on what basis can someone say this?"

2. for what domains might this somewhat true? False?

3. but the bigger problem is that claims like the above don't teach. i.e. When do Taylor series methods fall short? Why? When are the other approaches more useful?

Here's my quick take... Taylor expansions tends to work well when you are close to the expansion point and the function is analytic. Taylor expansions work less well when these assumptions don't hold. More broadly they don't tend to give uniform accuracy across a range. So Taylor approximations are usually only local. Other methods (Padé, minimax, etc) are worth reaching for when other constraints matter.

* I think this is a huge area we're going to need to work on in the age where anyone can sound like an expert.

** In the case above, does "comp. EM" mean "computational electromagnetics" or something else? The paper talks about "EML" so it makes me wonder if "EM" is a typo. All of these ambiguities add up and make it hard for people to understand each other.

"All" is a tall claim. Have a look at https://perso.ens-lyon.fr/jean-michel.muller/FP5.pdf for example. Jump to slide 18:

> Forget about Taylor series

> Taylor series are local best approximations: they cannot compete on a whole interval.

There is no need to worry about "sh-tt-ng" on their result when there is so much to learn about other approximation techniques.

"Just"? How would you build a predictive model that inferred aggregate individual qualities such as "% atheists" based on voting results? That would be a rather indirect and distorted path for estimation. There are better ways.

But standing up a house of cards is pretty cheap. Examples include: shell corporations, formulaic business plans, AI slop, surface-level conversation, color by numbers, tract housing, cravenly only appealing the base desires of people, & c. (This might be the first time I've connected the dots in this way -- and it explains my distaste for all those things.)

But "cheap" isn't necessarily insecure. Installing bollards around building entrances is relatively cheap insurance against vehicular attacks. So this is more complicated than it seems. "Fast" doesn't mean unsafe. Even "hastily created" software _could_ be (relatively) secure if it was highly constrained to provably hardened patterns. A big problem comes when attacking a cheap target builds capability for the attacker. In a way, this analogous to how viruses attack. Start with an easy target, hijack the cell machinery, multiply, repeat.

Maybe this formulation is accurate?: If you creates something beyond your ability to understand it, then get ready to get pwned. "Staying in one's lane" in this sense might be 'safe' at least narrowly speaking (unless an entire industry is operating in a state of delusion, which is arguably the case now.)

The question isn't "are companies making some security improvements?". That's one-sided. The question is "are companies making security improvements FAST ENOUGH to deal with the increased risks?"

/headscratching They don't have to be, do they? It is possible that some people will build identity systems with norms that e.g. humans type with their own hands. These could become popular, at least conceivably, in certain areas. Hard to enforce for sure. And getting harder and harder to distinguish reliably.

  > ## Accelerating Change [One School]
  >
  > Our intuitions about change are linear; we expect roughly
  > as much change as has occurred in the past over our own
  > lifetimes. But technological change feeds on itself, and
  > therefore accelerates. Change today is faster than it was
  > 500 years ago, which in turn is faster than it was 5000
  > years ago. Our recent past is not a reliable guide to how
  > much change we should expect in the future.
  >
  > Strong claim: Technological change follows smooth curves, 
  > typically exponential. Therefore we can predict with fair
  > precision when new technologies will arrive, and when they
  > will cross key thresholds, like the creation of [AI].
  >
  > Advocates: Ray Kurzweil, Alvin Toffler(?), John Smart

  https://www.yudkowsky.net/singularity/schools

> Even when considering just three dimensions, fewer than 5% of pilots were “average” in all. [1]

I would guess many/most people probably think they fall into either (1) the normal bucket or (変) the weird/fringe bucket. Either "I am pretty normal" or "I am an outsider". How many think "We're all fairly different once you cluster in any 3 interesting dimensions!"?

But people feel that dichotomy, which makes me think it is largely about perception relative to a dominant culture: the in-group versus out-group feeling. For example, atheists might feel like outsiders in many parts of the U.S., but less so in big cities and in other countries. In dense urban walkable cities (like NYC), people see diversity more directly and more often. Seeing a bunch of people is different than seeing a bunch of cars.

[1]: From "Curse of Dimensionality: Lessons from the U.S. Air Force Cockpit Design" by Maciej Nasinski (2025): https://polkas.github.io/posts/cursedim/

I appreciate++ the design except for the too-perfect rebar and the exposed wire directly _in_ the concrete. Pros would use a conduit methinks.

Seems reasonable, but this doesn't settle probably-empirical questions like: (a) to what degree is 'more' better?; (b) how important are filler words? (c) how important are words that signal connection, causality, influence, reasoning?