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...

> Am I the only one that can't imagine any possible value an AI assistant would bring to a game?

I use AI assistants lots when playing games!

At the moment it's Minecraft. I want to figure how to build something, e.g. a golem farm. I look for tutorials online but (1) they're all videos which I hate watching, (2) they're all hyper-specific and concrete, "place this block here then that block there", but what I want to understand are the foundational principles so I can know how to adapt the golem farm to my own purposes -- what are the mechanics, how do they spawn, how does water flow, what is the SOLUTION SPACE of possibilities.

Gemini AI has been really good at this kind of thing.

The other time is when I get stuck, or want advice on how to make a character build to achieve a certain end. Once again the online advice is typically in the form of "walkthroughs", do step 1 then step 2 then step 3, in other words just one possible way to play the game, and it's too easy to accidentally read too far and spoil the rest of it. I don't want that. I like the feeling of openness and possibilities. I again ask Gemini, and it gives me advice on just the particular bit I'm stuck on, and is better at showing for me the available options.

Lua remains the commonest choice today for games to offer scripting/modding. It's pretty much the industry standard. (Outside C# for Unity).

"Opt out of all FOG DATA SCIENCE data sets"

What -- exactly -- does that do, how quickly, and what are some of the side-effects?\

Underneath, it says "You will be removed from all our data sets." And yet I doubt that very much. Surely there will be an entry in a database somewhere saying "Device identifier ________-____-_____-_____-_____ requested removed date-and-time _____ from IP address _____", etc.
And does that only retroactively remove data? Suppose they snarf up another dataset, bought from someone else or collected by themselves. Is that data also removed from their datasets, or does another removal request have to be made?