I’d think you can define a tedrahedron for non-euclidean space
If you relax the definition of a tetrahedron to cover figures embedded in non-Euclidean spaces, sure. It wouldn’t be the exact same concept, however. In a similar way to how “a number” is different if you define it as a natural number vs. real number.
Perhaps more intuitively, then: the notion of a geometric figure with specific properties is dependent on the notion of a space in which it is embedded. (You can relax it further – e. g., arguably, you can define a “tetrahedron” for any set with a distance function over it – but the general point stands, I think.)
Just consider if you take the assumption that the system would not change in arbitrary ways in response to it’s environment. There might be certain constrains. You can think about what the constrains need to be such that e.g. a self modifying agent would never change itself such that it would expect that in the future it would get less utility than if it would not selfmodify.
Yes, but: those constraints are precisely the principles you’d need to code into your AI to give it general-intelligence capabilities. If your notion of alignment only needs to be robust to certain classes of changes, because you’ve figured out that an efficient generally intelligent system would only change in such-and-such ways, then you’ve figured out a property of how generally intelligent systems ought to work – and therefore, something about how to implement one.
Speaking abstractly, the “negative image” of the theory of alignment is precisely the theory of generally intelligent embedded agents. A robust alignment scheme would likely be trivial to transform into an AGI recipe.
A robust alignment scheme would likely be trivial to transform into an AGI recipe.
Perhaps if you did have the full solution, but it feels like that there are some things of a solution that you could figure out, such that that part of the solution doesn’t tell you as much about the other parts of the solution.
And it also feels like there could be a book such that if you read it you would gain a lot of knowledge about how to align AIs without knowing that much more about how to build one. E.g. a theoretical solution to the stop button problem seems like it would not tell you that much about how to build an AGI compared to figuring out how to properly learn a world model of Minecraft. And knowing how to build a world model of minecraft probably helps a lot with solving the stop button problem, but it doesn’t just trivially yield a solution.
Perhaps if you did have the full solution, but it feels like that there are some things of a solution that you could figure out, such that that part of the solution doesn’t tell you as much about the other parts of the solution.
If you relax the definition of a tetrahedron to cover figures embedded in non-Euclidean spaces, sure. It wouldn’t be the exact same concept, however. In a similar way to how “a number” is different if you define it as a natural number vs. real number.
Perhaps more intuitively, then: the notion of a geometric figure with specific properties is dependent on the notion of a space in which it is embedded. (You can relax it further – e. g., arguably, you can define a “tetrahedron” for any set with a distance function over it – but the general point stands, I think.)
Yes, but: those constraints are precisely the principles you’d need to code into your AI to give it general-intelligence capabilities. If your notion of alignment only needs to be robust to certain classes of changes, because you’ve figured out that an efficient generally intelligent system would only change in such-and-such ways, then you’ve figured out a property of how generally intelligent systems ought to work – and therefore, something about how to implement one.
Speaking abstractly, the “negative image” of the theory of alignment is precisely the theory of generally intelligent embedded agents. A robust alignment scheme would likely be trivial to transform into an AGI recipe.
Perhaps if you did have the full solution, but it feels like that there are some things of a solution that you could figure out, such that that part of the solution doesn’t tell you as much about the other parts of the solution.
And it also feels like there could be a book such that if you read it you would gain a lot of knowledge about how to align AIs without knowing that much more about how to build one. E.g. a theoretical solution to the stop button problem seems like it would not tell you that much about how to build an AGI compared to figuring out how to properly learn a world model of Minecraft. And knowing how to build a world model of minecraft probably helps a lot with solving the stop button problem, but it doesn’t just trivially yield a solution.
I agree with that.