| I'm not sure if this will help, but happy to elaborate further: The set of Turing computable functions is computationally equivalent to the lambda calculus, is computationally equivalent to the generally recursive functions. You don't need to understand those terms, only to know that these functions define the set of functions we believe to include all computable functions. (There are functions that we know to not be computable, such as e.g. a general solution to the halting problem) That is, we don't know of any possible way of defining a function that can be computed that isn't in those sets. This is basically the Church-Turing thesis: That a function on the natural numbers can be effectively computable (note: this has a very specific meaning, it's not about performance) only if it is computable by a Turing machine. Now, any Turing machine can simulate any other Turing machine. Possibly in a crazy amount of time, but still. The brain is at least a Turing machine in terms of computabilitity if we treat "IO" (speaking, hearing, for example) as the "tape" (the medium of storage in the original description of the Turing machine). We can prove this, since the smallest Turing machine is a trivial machine with 2 states and 3 symbols that any moderate functional human is capable of "executing" with pen and paper. (As an aside: It's almost hard to construct a useful computational system that isn't Turing complete; "accidental Turing completeness" regularly happens, because it is very trivial to end up with a Turing complete system) An LLM with a loop around it and temperature set to 0 can trivially be shown to be able to execute the same steps, using context as input and the next token as output to simulate the tape, and so such a system is Turing complete as well. (Note: In both cases, this could require a program, but since for any Turing machine of a given size we can "embed" parts of the program by constructing a more complex Turing machine with more symbols or states that encode some of the actions of the program, such a program can inherently be embedded in the machine itself by constructing a complex enough Turing machine) Assuming we use a definition of intelligence that a human will meet, then because all Turing machines can simulate each other, then the only way of showing that an artificial intelligence can not theoretically be constructed to at least meet the same bar is by showing that humans can compute more than the Turing computable. If we can't then "worst case" AGI can be constructed by simulating every computational step of the human brain. Any other argument about the impossibility of AGI inherently needs to contain a something that disproves the Church-Turing thesis. As such, it's a massive red flag when someone claims to have a proof that AGI isn't possible, but haven't even mentioned the Church-Turing thesis. |
I would reframe: the only way of showing that artificial intelligence can be constructed is by showing that humans cannot compute more than the Turing computable.
Given that Turing computable functions are a vanishingly small subset of all functions, I would posit that that is a rather large hurdle to meet. Turing machines (and equivalents) are predicated on a finite alphabet / state space, which seems woefully inadequate to fully describe our clearly infinitary reality.