IANAL nor intelligent, but after skimming the text of the directive I felt like the definition of damage is very limited. In particular, if I understand correctly:

our business to lose this giant contract

would not be covered by this directive, this directive is only about a human being hurt in some way,

thousands of consumers left with bricked devices

would be covered in case of “your game installs a kernel-level anticheat and the anticheat breaks PCs”, but not in the case of “you uploaded an upgrade to a firmware of the washing machine you produced and it bricked the machines”; the directive is not about a product breaking, but about the product breaking your health, other property or data,

my washing machine to eat my dog

is basically the exact case this directive covers.

Same in Python, Rust, Haskell and probably many others.

But apparently JS does work that way, that is its filter always iterates over everything and returns a new array and not some iterator object.

Hasn’t Google already made advances through its Alpha Geometry AI?? Admittedly, that’s a geometry setting which may be easier to code than other parts of Math and there isn’t yet a clear indication AI will ever be able to reach a certain level of creativity that the human mind has, but at the same time it might get there by sheer volume of attempts.

Wanted to focus a bit on this. The thing with AlphaGeometry and AlphaProof is that they really treat doing math as a game, not unlike chess. For example, AlphaGeometry has a basic set of rules, it can apply them and it knows when it is done. And when it is done, you can be 100% sure that the solution is correct, because the rules of the game are known; the 28/42 score reported in the article is really four perfect scores and three zeros. Those systems do use LLMs, but they really are only there to suggest to the system what to try doing next. There is a very enlightening picture in the AlphaGeometry paper here: https://www.nature.com/articles/s41586-023-06747-5#Fig1

You can automatically verify correctness of code the same way. For example Lean, the language AlphaProof uses internally, can be used for general programming. In general, we call similar programming techniques formal methods. But most people don’t do this, since this is more time-consuming than normal programming, and in many cases we don’t even know how to define the goal of our code (how to define correct rendering in a game?). So this is only really done when the correctness of the program is critical, like famously they verified the code of the automatic metro in Paris this way. And so most people don’t try to make programming AI work this way.

Oracle trilateration refers to an attack on apps that have filters like “only show users closer than 5 km”. In case of the vulnerable apps, this was very accurate, so the attacker could change their position from the victim (which does not require physical movement, the application has to trust your device on this, so the position can be spoofed) until the victim disappeared from the list, and end up a point that is almost exactly 5 km from the victim.

Like if it said the user is 5km away, that is still going to give a pretty big area if someone were to trilateral it because the line of the circle would have to include 4.5-5.5km away.

This does not help, since the attacker can find a point where it switches between 4 km and 5 km, and then this point (in the simplest case) is exactly 4.5 km from the victim. The paper refers to this as rounded distance trilateration.

I really need to try out Mercury one day. When we did a project in Prolog at uni, it felt cool, but also incredibly dynamic in a bad way. There were a few times when we misspelled some clause, which normally would be an error, but in our case it just meant falsehood. We then spent waaay to much time searching for these. I can’t help but think that Mercury would be as fun as Prolog, but less annoying.

I actually use from time to time the Bower email client, which is written in Mercury.