Maybe we’re just not using the same definitions, but according to the definitions in the OP as I understand them, a box might indeed contain an arbitrarily strong optimizer_1 while not containing an optimizer_2.
For example, suppose the box contains an arbitrarily large computer that runs a brute-force search for some formal optimization problem. [EDIT: for some optimization problems, the evaluation of a solution might result in the execution of an optimizer_2]
I think only if its gets its feedback from the real world. If you have gradient descent, then the true answers for its samples are stored somewhere “outside” the intended demarcation, and it might try to reach them. But how is a hillclimber that is given a graph and solves the traveling salesman problem for it an optimizer_2?
I would answer this the same way I did earlier in this thread, simply substituting in whatever problem you like for SAT in that example.
All feedback is feedback about the real world because the real world is the only place you can instantiate the computation to reason about “math land”.
Yes, obviously its going to change the world in some way to be run. But not any change anywhere makes it an optimizer_2. As defined, an optimizer_2 optimizes its environment. Changing something inside the computer does not make it an optimizer_2, and changing something outside without optimizing it doesnt either. Yes, the computer will inevitably cause some changes in its enviroment just by running, but what makes something an optimizer_2 is to systematically bring about a certain state of the environment. And this offers a potential solution other than by hardware engineering: If the feedback is coming from inside the computer, then what is incentivized are only states within the computer, and so only the computer is getting optimized.
Maybe we’re just not using the same definitions, but according to the definitions in the OP as I understand them, a box might indeed contain an arbitrarily strong optimizer_1 while not containing an optimizer_2.
For example, suppose the box contains an arbitrarily large computer that runs a brute-force search for some formal optimization problem. [EDIT: for some optimization problems, the evaluation of a solution might result in the execution of an optimizer_2]
Yes, and I’m saying that’s not possible. Every optimizer_1 is an optimizer_2.
I think only if its gets its feedback from the real world. If you have gradient descent, then the true answers for its samples are stored somewhere “outside” the intended demarcation, and it might try to reach them. But how is a hillclimber that is given a graph and solves the traveling salesman problem for it an optimizer_2?
I would answer this the same way I did earlier in this thread, simply substituting in whatever problem you like for SAT in that example.
All feedback is feedback about the real world because the real world is the only place you can instantiate the computation to reason about “math land”.
Yes, obviously its going to change the world in some way to be run. But not any change anywhere makes it an optimizer_2. As defined, an optimizer_2 optimizes its environment. Changing something inside the computer does not make it an optimizer_2, and changing something outside without optimizing it doesnt either. Yes, the computer will inevitably cause some changes in its enviroment just by running, but what makes something an optimizer_2 is to systematically bring about a certain state of the environment. And this offers a potential solution other than by hardware engineering: If the feedback is coming from inside the computer, then what is incentivized are only states within the computer, and so only the computer is getting optimized.