Any deterministic system with a finite number of states is decidable, in the halting problem sense. Either it halts or repeats a state. The halting problem only applies for infinite memory. Now, there are finite state systems where the halting problem is arbitrarily hard. But that's not undecidability. That's complexity. That's a problem in the space where P=NP lives. The article does not make this distinction, and i…
I don't think this is an important distinction. The point of Turing machines is that you can ask questions like "can all instances of this problem be solved uniformly, and how relatively expensive is it?". This requires infinite memory in a formal sense , because instances of (most) problems can be arbitrarily large. Yes, if you ask the same question with a fixed (finite) memory restriction on everything, the answer…
> (first and foremost Turing machines are a tool for analysing problems mathematically, not a model of your laptop)
I think what makes Animats's distinction worth stressing is that a lot of people miss this and fall into the trap of making false/misleading claims about the impact of the halting problem on actual computers/software.
For instance, claiming that the halting problem proves there are problems humans can solve that computers fundamentally can't, giving "the halting problem makes this impossible" as reason against adding a halt/loop detector to a chip emulator, making all sorts of weird claims about AI[0][1], or arguably even this author's claim that a halt detector would make bcrypt cracking trivial.
[0]: https://mindmatters.ai/2025/02/agi-the-halting-problem-and-t...