i agree with the sentiment behind what you say here.
The difficult part is to shake ourselves free of any unexamined, implicit assumptions that we might be bringing to the table, when we talk about the problem.
For example, when you say:
And this is the reason why we should be worried about an AI with a poorly made utility function
... you are talking in terms of an AI that actually HAS such a thing as a "utility function". And it gets worse: the idea of a "utility function" has enormous implications for how the entire control mechanism (the motivations and goals system) is designed.
A good deal of this debate about my paper is centered in a clash of paradigms: on the one side a group of people who cannot even imagine the existence of any control mechanism except a utility-function-based goal stack, and on the other side me and a pretty large community of real AI builders who consider a utility-function-based goal stack to be so unworkable that it will never be used in any real AI.
Other AI builders that I have talked to (including all of the ones who turned up for the AAAI symposium where this paper was delivered, a year ago) are unequivocal: they say that a utility-function-and-goal-stack approach is something they wouldn't dream of using in a real AI system. To them, that idea is just a piece of hypothetical silliness put into AI papers by academics who do not build actual AI systems.
And for my part, I am an AI builder with 25 years experience, who was already rejecting that approach in the mid-1980s, and right now I am working on mechanisms that only have vague echoes of that design in them.
Meanwhile, there are very few people in the world who also work on real AGI system design (they are a tiny subset of the "AI builders" I referred to earlier), and of the four others that I know (Ben Goertzel, Peter Voss, Monica Anderson and Phil Goetz) I can say for sure that the first three all completely accept the logic in this paper. (Phil's work I know less about: he stays off the social radar most of the time, but he's a member of LW so someone could ask his opinion).
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Although technically any AI has a utility function, the usual arguments about the failings of utility functions don't apply to unusual utility functions like the type that may be more easily described using other paradigms.
For instance, Google Maps can be thought of as having a utility function: it gains higher utility the shorter the distance is on the map. However, arguments such as "you can't exactly specify what you want it to do, so it might blackmail the president into building a road in order to reduce the map distance" aren't going to work, because you can program Google Maps in such a way that it never does that sort of thing.
The reason that such arguments do not work is that you can specify exactly what it is you want to do, and the programmers did specify exactly that.
In more complex cases, where the programmers are unable to specify exactly what they want, you do get unexpected results that can be thought of as "the program wasn't optimizing what the programmers thought it should be optimizing, but only a (crude) approximation thereof". (an even better example would be one where a genetic algorithm used in circuit design unexpectedly re-purposed some circuit elements to build an antenna, but I cannot find that reference right now)