> Of course, this won’t be anywhere near as efficient as just implementing the decoder in C
> It is quite easy to add new built-in modules to Python, if you know how to program in C. Such extension modules can do two things that can’t be done directly in Python: they can implement new built-in object types, and they can call C library functions and system calls.
Then why are you talking about this instead of extension modules?
> quiet preloading of an attacker-selected state while the machine is off.
That attacker with physical access can chose from multiple other attacks, so why bother making this one more secure. They can sniff keyboard strokes, install malware into the UEFI, etc. pp.
> To give you an example, fsck.ext4 will happily carve a working filesystem out of random data
What application does this have for recovering user data? Would it not be more interesting to see how much of the user data can be recovered? Or are you implying that the random data can symbolize the user data here and much of it would be recovered?
A missing part that AI needs is a experimental way to verify its results. In some contexts, like math, this is entirely possible because it does not involve manipulation of real world objects. For example, i asked GTP-4, In the context of optimization, does the generalized assignment problem satisfy total unimodularity. Then experimental way would be to look up the formulas of the integer program on the internet or in its training data, write down the constraint matrix of the given problem, fill it with values, and check if the matrix is unimodular. Instead, the way gpt-4 answered this question, correctly, albeit with a weak reasoning, is by saying that researchers worked on heuristics to solve the problem, thus it is probably not unimodular as that would allow a quick solution without resorting to heuristics.
42^3 = 138 mod 493
It should read
42^3 mod 493 = 138