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A list of some posts that are pretty awesome
I recommend the major sequences to everybody, but I realize how daunting they look at first. So for purposes of immediate gratification, the following posts are particularly interesting/illuminating/provocative and don't require any previous reading:
- Your Intuitions are Not Magic
- The Apologist and the Revolutionary
- How to Convince Me that 2 + 2 = 3
- Lawful Uncertainty
- The Planning Fallacy
- Scope Insensitivity
- The Allais Paradox (with two followups)
- We Change Our Minds Less Often Than We Think
- The Least Convenient Possible World
- The Third Alternative
- The Domain of Your Utility Function
- Newcomb's Problem and Regret of Rationality
- The True Prisoner's Dilemma
- The Tragedy of Group Selectionism
- Policy Debates Should Not Appear One-Sided
- That Alien Message
More suggestions are welcome! Or just check out the top-rated posts from the history of Less Wrong. Most posts at +50 or more are well worth your time.
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Thank you.
I'd frame why I think biology matters in FAI research in terms of research applicability and toolbox dividends.
On the first reason--- applicability--- I think more research focus on biologically-inspired AGI would make a great deal of sense is because the first AGI might be a biologically-inspired black box, and axiom-based FAI approaches may not particularly apply to such. I realize I'm (probably annoyingly) retreading old ground here with regard to which method will/should win the AGI race, but SIAI's assumptions seem to run counter to the assumptions of the greater community of AGI researchers, and it's not obvious to me the focus on math and axiology isn't a simple case of SIAI's personnel backgrounds being stacked that way. 'If all you have is a hammer,' etc. (I should reiterate that I don't have any alternatives to offer here and am grateful for all FAI research.)
The second reason I think biology matters in FAI research--- toolbox dividends--- might take a little bit more unpacking. (Forgive me some imprecision, this is a complex topic.)
I think it's probable that anything complex enough to deserve the term AGI would have something akin to qualia/emotions, unless it was specifically designed not to. (Corollary: we don't know enough about what Chalmers calls "psychophysical laws" to design something that lacks qualia/emotions.) I think it's quite possible that an AGI's emotions, if we did not control for their effects, could produce complex feedback which would influence its behavior in unplanned ways (though perfectly consistent with / determined by its programming/circuitry). I'm not arguing for a ghost in the machine, just that the assumptions which allow us to ignore what an AGI 'feels' when modeling its behavior may prove to be leaky abstractions in the face of the complexity of real AGI.
Axiological approaches to FAI don't seem to concern themselves with psychophysical laws (modeling what an AGI 'feels'), whereas such modeling seems a core tool for biological approaches to FAI. I find myself thinking being able to model what an AGI 'feels' will be critically important for FAI research, even if it's axiom/math-based, because we'll be operating at levels of complexity where the abstractions we use to ignore this stuff can't help but leak. (There are other toolbox-based arguments for bringing biology into FAI research which are a lot simpler than this one, but this is on the top of my list.)
As a layman I don't have a clear picture of how to start doing that. How would it differ from this? Looks like you can find the paper in question here (WARNING: out-of-date 2002 content).