Because an AI built as a utility-maximizer will consider any rules restricting its ability to maximize its utility as obstacles to be overcome. If an AI is sufficiently smart, it will figure out a way to overcome those obstacles. If an AI is superintelligent, it will figure out ways to overcome those obstacles which humans cannot predict even in theory and so cannot prevent even with multiple well-phrased fail-safes.
I hope AGI's will be equipped with as many fail-safes as your argument rests on assumptions.
A paperclip maximizer with a built-in rule "Only create 10,000 paperclips per day" will still want to maximize paperclips. It can do this by deleting the offending fail-safe, or by creating other paperclip maximizers without the fail-safe, or by creating giant paperclips which break up into millions of smaller paperclips of their own accord, or by connecting the Earth to a giant motor which spins it at near-light speed and changes the length of a day to a fraction of a second.
I just don't see how one could be sophisticated enough to create a properly designed AGI capable of explosive recursive self-improvement and yet fail drastically on its scope boundaries.
Unless you feel confident you can think of every way it will get around the rule and block it off, and think of every way it could get around those rules and block them off, and so on ad infinitum, the best thing to do is to build the AI so it doesn't want to break the rules...
What is the difference between "a rule" and "what it wants". You seem to assume that it cares to follow a rule to maximize a reward number but doesn't care to follow another rule that tells it to hold.
I hope AGI's will be equipped with as many fail-safes as your argument rests on assumptions.
Fail safes would be low cost: if it can't think of a way to beat them, it isn't the bootstrapping AI we were hoping for anyway, and might even be harmful, so it would be good to have the fail-safes.
I just don't see how one could be sophisticated enough to create a properly designed AGI capable of explosive recursive self-improvement and yet fail drastically on its scope boundaries.
It seems to me evolution based algorithms could do the trick.
...What is the diff
Many people think you can solve the Friendly AI problem just by writing certain failsafe rules into the superintelligent machine's programming, like Asimov's Three Laws of Robotics. I thought the rebuttal to this was in "Basic AI Drives" or one of Yudkowsky's major articles, but after skimming them, I haven't found it. Where are the arguments concerning this suggestion?