Thanks for typing that out. Note that your answer can't be right, though.

Thank you for this comment. I learned a lot.

https://web.archive.org/web/20...

I.e. "It does."

One of the defining papers in this field is 2013 "Intriguing properties of neural networks" by Szegedy et al. In their own words from the abstract, "we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extent"

I'm using the word "continuous" in the same sense as them. (Perhaps it does indeed mean what I think it means...)

In image processing like this article is talking about, the classic example of a harmful response is that your car's camera sees "speed limit 30" sign, but a small sticker it makes the image processor believe it saw a "speed limit 70" sign.

(this is an actual demonstrated attack. It means that pranksters could cripple self driving.)

The thing about these image classifiers is that they're not "continuous". You can make it see a stop sign as a right-of-way sign, or a green light.

My main AGENTS.md has ten lines about the most important coding principles:

The instruction on "meaning" ended up carrying a lot of weight to the AI. It adopted the habit of putting a comment on every single field and function that starts with the word "// Meaning: " and they're honestly, genuinely good ones! Single-line sentences on fields that carry a lot of good weight.

Separately, I have a LEARNINGS.md file which I have the AI auto-update every time it gets course corrected by me. Over the first two weeks there were a lot of course corrections, but now there are only a few a day. The file ended up carrying my senior engineer wisdom, more or less, the kind of things I normally mentor to junior developers on the team over several years. Here's an extract: https://gist.github.com/ljw100...

My CS degree had a lot of theorem proofs in it, invariants, that kind of thing. I've always had the habit of aiming to prove my code correct under all possible circumstances. Usually not a formal proof, but using the same skeleton as a formal proof would.

It got me a job on the C# language design team (when I tried to prove an algorithm correct, couldn't, discovered a counter-proof that the runtime had a flaw).

As I mentor junior devs and review their code, I'm always telling them to reason about their invariants better and document them.

Now in the age of AI, I find that invariant-heavy and proof-heavy guidance to the AI ends up getting its work done quicker and higher quality. OpenAI mentioned the same thing in a blog post in February.

Sure, there are many paths to professional success and engineering excellence that don't involve this kind of CS heavy approach. But, there are many that do...

cootys rat semen

Denotating it is exactly what the Bluetooth name does. *Detonating* it, on the other hand...

You realize that's the point, right?

I think a tool that lets one person do the work of 20 will result in 17 people being laid off, as the company triples its rate of work. (I can see triple being feasible and achievable in the software industry, but maybe not more)

I seem to have had a very similar progression. I still tend to think in "/." replacement operators, but it's been perhaps 15 years since I've really used Wolfram Language.

Do you really say "lol"?

Oh, and a "train" is a bit like 100 cars back to back.

"this belief is utterly false. To quote the U.S. Supreme Court opinion in the recent Hobby Lobby case: 'Modern corporate law does not require for-profit corporations to pursue profit at the expense of everything else, and many do not.'"

https://www.nytimes.com/roomfo...

"We ... show that [the Shareholder Primacy Norm] is not a legal requirement, at least under the guise of shareholder value maximization. This is in contrast to the common assertion that managers are legally constrained from addressing corporate social responsibility issues if doing so would be inconsistent with the economic interests of shareholders."

https://papers.ssrn.com/sol3/p...