I disagree. You need to know much more than just the drive for grandchildren, given the massively diverse ways we observe even in our present world for species to propagate, all of which correspond to different articulable values once they reach human intelligence.
Human values should be expected to have a high K-complexity because you would need to specify both the genes/early environment, and the precise place in history/Everett branches where humans are now.
The idea was to “approximate human values”—not to express them in precise detail: nobody cares much if Jim likes strawberry jam more than he likes raspberry jam.
The idea was to “approximate human values”—not to express them in precise detail
Sure, but I take “approximation” to mean something like getting you within 10 or so bits of the true distribution, but the heuristic you gave still leaves you maybe 500 or so bits away, which is huge, and far more than you implied.
The environment mostly drops out of the equation—because most of it is shared between the agents involved—and because of the phenomenon of Canalisation
That would help you on message length if you had already stored one person’s values and were looking to store a second person’s. It does not for describing the first person’s value, or some aggregate measure of humans’ values.
The idea of a shared environment arises because the proposed machine—in which the human-like values are to be implemented—is to live in the same world as the human. So, one does not need to specify all the details of the environment—since these are shared naturally between the agents in question.
10 bits short of the needed message, not a 10-bit message. I mean that e.g. an approximation gives 100 bits when full accuracy would be 110 bits (and 10 bits is an upper bound).
The idea of a shared environment arises because the proposed machine—in which the human-like values are to be implemented—is to live in the same world as the human. So, one does not need to specify all the details of the environment—since these are shared naturally between the agents in question.
That still doesn’t answer my point; it just shows how once you have one agent, adding others is easy. It doesn’t show how getting the first, or the “general” agent is easy.
Re: “That still doesn’t answer my point; it just shows how once you have one agent, adding others is easy. It doesn’t show how getting the first, or the “general” agent is easy.”
To specify the environment, choose the universe, galaxy, star, planet, lattiude, longitude and time. I am not pretending that information is simple, just that it is already there, if your project is building an intelligent agent.
Yes, I got that the first time. I don’t think you are appreciating the difficulty of coding even relatively simple utility functions. A couple of ASCII characters is practically nothing!
ASCII characters aren’t a relevant metric here. Getting within 10 bits of the correct answer means that you’ve narrowed it down to 2^10 = 1024 distinct equiprobable possibilities [1], one of which is correct. Sounds like an approximation to me! (if a bit on the lower end of the accuracy expected out of one)
[1] or probability distribution with the same KL divergence from the true governing distribution
I disagree. You need to know much more than just the drive for grandchildren, given the massively diverse ways we observe even in our present world for species to propagate, all of which correspond to different articulable values once they reach human intelligence.
Human values should be expected to have a high K-complexity because you would need to specify both the genes/early environment, and the precise place in history/Everett branches where humans are now.
The idea was to “approximate human values”—not to express them in precise detail: nobody cares much if Jim likes strawberry jam more than he likes raspberry jam.
The environment mostly drops out of the equation—because most of it is shared between the agents involved—and because of the phenomenon of Canalisation: http://en.wikipedia.org/wiki/Canalisation_%28genetics%29
Sure, but I take “approximation” to mean something like getting you within 10 or so bits of the true distribution, but the heuristic you gave still leaves you maybe 500 or so bits away, which is huge, and far more than you implied.
That would help you on message length if you had already stored one person’s values and were looking to store a second person’s. It does not for describing the first person’s value, or some aggregate measure of humans’ values.
10 bits!!! That’s not much of a message!
The idea of a shared environment arises because the proposed machine—in which the human-like values are to be implemented—is to live in the same world as the human. So, one does not need to specify all the details of the environment—since these are shared naturally between the agents in question.
10 bits short of the needed message, not a 10-bit message. I mean that e.g. an approximation gives 100 bits when full accuracy would be 110 bits (and 10 bits is an upper bound).
That still doesn’t answer my point; it just shows how once you have one agent, adding others is easy. It doesn’t show how getting the first, or the “general” agent is easy.
Re: “That still doesn’t answer my point; it just shows how once you have one agent, adding others is easy. It doesn’t show how getting the first, or the “general” agent is easy.”
To specify the environment, choose the universe, galaxy, star, planet, lattiude, longitude and time. I am not pretending that information is simple, just that it is already there, if your project is building an intelligent agent.
Re: “10 bits short of the needed message”.
Yes, I got that the first time. I don’t think you are appreciating the difficulty of coding even relatively simple utility functions. A couple of ASCII characters is practically nothing!
ASCII characters aren’t a relevant metric here. Getting within 10 bits of the correct answer means that you’ve narrowed it down to 2^10 = 1024 distinct equiprobable possibilities [1], one of which is correct. Sounds like an approximation to me! (if a bit on the lower end of the accuracy expected out of one)
[1] or probability distribution with the same KL divergence from the true governing distribution
Or you can implement constant K-complexity learn-by-example algorithm and get all the rest from environment.
How about “Do as your creators do (generalize this as your creators generalize)”?