Three Approaches to "Friendliness"

Without catastrophic risks (including UFAI and value drift), we could take as much time as necessary and/or go with making people smarter first.

You appear to be operating under the assumption that it's already too late or otherwise impractical to "go with making people smarter first", but I don't see why, compared to "build FAI first".

Human cloning or embryo selection look like parallelizable problems that would be easily amenable to the approach of "throwing resources at it". It just consists of a bunch of basic science and engineering problems, which humans are generally pretty good at, compared to the kind of philosophical problems that need to be solved for building FAI. Nor do we have to get all those problems right on the first try or face existential disaster. Nor is intelligence enhancement known to be strictly harder than building UFAI (i.e., solving FAI requires solving AGI as a subproblem). And there must be many other research directions that could be funded in addition to these two. All it would take is for some government or maybe even large corporation or charitable organization to take the problem of "astronomical waste" seriously (again referring to the more general concept than Bostrom's, which I wish had its own established name).

If it's not already too late or impractical to make people smarter first (and nobody has made a case that it is, as far as I know) then FAI work has the counterproductive consequence of making it harder to make people smarter first (by shortening AI timelines). MIRI and other FAI advocates do not seem to have taken this into account adequately.

Doesn't negative utilitarianism present us with the analogous challenge of preventing "astronomical suffering", which requires an FAI to have solutions to the same philosophical problems mentioned in the post? I guess I was using "astronomical waste" as short for "potentially large amounts of negative value compared to what's optimal" but if it's too much associated with total utilitarianism then I'm open to suggestions for a more general term.

we still want to get as good a bargaining position as we possibly can, or to coordinate with the watchers as well as we possibly can, or in a more fundamental sense we want to not waste any of our potential, which I think is the real driving intuition here

It seems that we have more morally important potential in some possible worlds than others, and although we don't want our language to commit us to the view that we only have morally important potential in possible worlds where we can prevent astronomical waste, neither do we want to suggest (as I think "not waste any of our potential" does) the view that we have the same morally important potential everywhere and that we should just minimize the expected fraction of our potential that is wasted. A more neutral way of framing things could be "minimize wasted potential, especially if the potential is astronomical", leaving the strength of the "especially" to be specified by theories of how much one can affect the world from base reality vs simulations and zoos, theories of how to deal with moral uncertainty, and so on.

Of course, this is still just a proxy measure... say that we're "in a simulation", or that there are already superintelligences in our environment who won't let us eat the stars, or something like that—we still want to get as good a bargaining position as we possibly can, or to coordinate with the watchers as well as we possibly can, or in a more fundamental sense we want to not waste any of our potential, which I think is the real driving intuition here.

Agreed. I was being lazy and using "astronomical waste" as a pointer to this more general concept, probably because I was primed by people talking about "astronomical waste" a bunch recently.

Further clarifying and expanding on that intuition might be very valuable, both for polemical reasons and for organizing some thoughts on AI strategy.

Also agreed, but I currently don't have much to add to what's already been said on this topic.

The thing I've seen that looks closest to white-box metaphilosophical AI in the existing literature is Eliezer's causal validity semantics, or more precisely the set of intuitions Eliezer was drawing on to come up with the idea of causal validity semantics.

Ugh, I found CFAI largely impenetrable when I first read it, and have the same reaction reading it now. Can you try translating the section into "modern" LW language?

Coincidentally, I ended up reading Evolutionary Psychology: Controversies, Questions, Prospects, and Limitations today, and noticed that it makes a number of points that could be interpreted in a similar light: in that humans do not really have a "domain-general rationality", and that instead we have specialized learning and reasoning mechanisms, each of which are carrying out a specific evolutionary purpose and which are specialized for extracting information that's valuable in light of the evolutionary pressures that (used to) prevail. In other words, each of them carries out inferences that are designed to further some specific evolutionary value that helped contribute to our inclusive fitness.

The paper doesn't spell out the obvious implication, since that isn't its topic, but it seems pretty clear to me: since our various learning and reasoning systems are based on furthering specific values, our philosophy has also been generated as a combination of such various value-laden systems, and we can't expect an AI reasoner to develop a philosophy that we'd approve of unless its reasoning mechanisms also embody the same values.

That said, it does suggest a possible avenue of attack on the metaphilosophy issue... figure out exactly what various learning mechanisms we have and which evolutionary purposes they had, and then use that data to construct learning mechanisms that carry out similar inferences as humans do.

Quotes:

Hypotheses about motivational priorities are required to explain empirically discovered phenomena, yet they are not contained within domain-general rationality theories. A mechanism of domain-general rationality, in the case of jealousy, cannot explain why it should be “rational” for men to care about cues to paternity certainty or for women to care about emotional cues to resource diversion. Even assuming that men “rationally” figured out that other men having sex with their mates would lead to paternity uncertainty, why should men care about cuckoldry to begin with? In order to explain sex differences in motivational concerns, the “rationality” mechanism must be coupled with auxiliary hypotheses that specify the origins of the sex differences in motivational priorities. [...]

The problem of combinatorial explosion. Domain-general theories of rationality imply a deliberate cal- culation of ends and a sample space of means to achieve those ends. Performing the computations needed to sift through that sample space requires more time than is available for solving many adaptive problems, which must be solved in real time. Consider a man coming home from work early and discovering his wife in bed with another man. This circumstance typically leads to immediate jealousy, rage, violence, and sometimes murder (Buss, 2000; Daly & Wilson, 1988). Are men pausing to rationally deliberate over whether this act jeopardizes their paternity in future offspring and ultimate reproductive fitness, and then becoming enraged as a consequence of this rational deliberation? The predictability and rapidity of men’s jealousy in response to cues of threats to paternity points to a specialized psychological circuit rather than a response caused by deliberative domain-general rational thought. Dedicated psychological adaptations, because they are activated in response to cues to their corresponding adaptive problems, operate more efficiently and effectively for many adaptive problems. A domain-general mechanism “must evaluate all alternatives it can define. Permutations being what they are, alternatives increase exponentially as the problem complexity increases” (Cosmides & Tooby, 1994, p. 94). Consequently, combinatorial explosion paralyzes a truly domain-general mechanism (Frankenhuis & Ploeger, 2007). [...]

In sum, domain-general mechanisms such as “rationality” fail to provide plausible alternative explanations for psychological phenomena discovered by evolutionary psychologists. They are invoked post hoc, fail to generate novel empirical predictions, fail to specify underlying motivational priorities, suffer from paralyzing combinatorial explosion, and imply the detection of statistical regularities that cannot be, or are unlikely to be, learned or deduced ontogenetically. It is important to note that there is no single criterion for rationality that is independent of adaptive domain. [...]

The term learning is sometimes used as an explana- tion for an observed effect and is the simple claim that something in the organism changes as a consequence of environmental input. Invoking “learning” in this sense, without further specification, provides no additional explanatory value for the observed phenomenon but only regresses its cause back a level. Learning requires evolved psychological adaptations, housed in the brain, that enable learning to occur: “After all, 3-pound cauliflowers do not learn, but 3-pound brains do” (Tooby & Cosmides, 2005, p. 31). The key explanatory challenge is to identify the nature of the underlying learning adaptations that enable humans to change their behavior in functional ways as a consequence of particular forms of environmental input.

Although the field of psychology lacks a complete understanding of the nature of these learning adaptations, enough evidence exists to draw a few reasonable conclu- sions. Consider three concrete examples: (a) People learn to avoid having sex with their close genetic relatives (learned incest avoidance); (b) people learn to avoid eating foods that may contain toxins (learned food aversions); (c) people learn from their local peer group which actions lead to increases in status and prestige (learned prestige criteria). There are compelling theoretical arguments and empirical evidence that each of these forms of learning is best explained by evolved learning adaptations that have at least some specialized design features, rather than by a single all-purpose general learning adaptation (Johnston, 1996). Stated differently, evolved learning adaptations must have at least some content-specialized attributes, even if they share some components. [...]

These three forms of learning—incest avoidance, food aversion, and prestige criteria—require at least some content-specific specializations to function properly. Each op- erates on the basis of inputs from different sets of cues: coresidence during development, nausea paired with food ingestion, and group attention structure. Each has different functional output: avoidance of relatives as sexual partners, disgust at the sight and smell of specific foods, and emulation of those high in prestige. It is important to note that each form of learning solves a different adaptive problem.

There are four critical conclusions to draw from this admittedly brief and incomplete analysis. First, labeling something as “learned” does not, by itself, provide a satisfactory scientific explanation any more than labeling something as “evolved” does; it is simply the claim that environmental input is one component of the causal process by which change occurs in the organism in some way. Second, “learned” and “evolved” are not competing explanations; rather, learning requires evolved psychological mechanisms, without which learning could not occur. Third, evolved learning mechanisms are likely to be more numerous than traditional conceptions have held in psychology, which typically have been limited to a few highly general learning mechanisms such as classical and operant conditioning. Operant and classical conditioning are important, of course, but they contain many specialized adaptive design features rather than being domain general (Ohman & Mineka, 2003). And fourth, evolved learning mechanisms are at least somewhat specific in nature, containing particular design features that correspond to evolved solutions to qualitatively distinct adaptive problems.

I always suspected that natural kinds depended on an underdetermined choice of properties, but I had no idea there was or could be a theorem saying so. Thanks for pointing this out.

Does a similar point apply to Solomonoff Induction? How does the minimum length of the program necessary to generate a proposition, vary when we vary the properties our descriptive language uses?

If I understand correctly, in order for your designs to work, you must first have a question-answerer or predictor that is much more powerful than a human (i.e., can answer much harder questions that a human can). For example, you are assuming that the AI would be able to build a very accurate model of an arbitrary human overseer from sense data and historical responses and predict their "considered judgements", which is a superhuman ability. My concern is that when you turn on such an AI in order to test it, it might either do nothing useful (i.e., output very low quality answers that give no insights to how safe it would eventually be) because it's not powerful enough to model the overseer, or FOOM out of control due to a bug in the design or implementation and the amount of computing power it has. (Also, how are you going to stop others from making use of such powerful answers/predictors in a less safe, but more straightforward and "efficient" way?)

With a white-box metaphilosophical AI, if such a thing was possible, you could slowly increase its power and hopefully observe a corresponding increase in the quality of its philosophical output, while fixing any bugs that are detected and knowing that the overall computing power it has is not enough for it to vastly outsmart humans and FOOM out of control. It doesn't seem to require access to superhuman amounts of computing power just to start to test its safety.

Modulo complexity of value, I hope? I don't think we're in a position to pinpoint QALY's as The Thing to Tile.

The best argument reason I know that we might need to solve this problem soon is the possibility of a fast takeoff, which still seems reasonably unlikely (say < 10% probability) but is certainly worth thinking about more carefully in advance.

When you say "fast takeoff" do you mean the speed of the takeoff (how long it takes from start to superintelligence) or the timing of it (how far away it is from now)? Because later on you mention "< 40 years" which makes me think you mean that here as well, and timing would also make more sense in the context of your argument, but then I don't understanding why you would give < 10% probability for takeoff in the next 40 years.

But even granting a fast takeoff, it seems quite likely that you can build AI's that "work around" this problem in other ways, particularly by remaining controlled by human owners

Superintelligent AIs controlled by human owners, even if it's possible, seem like a terrible idea, because humans aren't smart or wise enough to handle such power without hurting themselves. I wouldn't even trust myself to control such an AI, much less a more typical, less reflective human.

or by quickly bootstrapping to a better prepared society

Not sure what you mean by this. Can you expand?

And finally, even granting that we need to design such a singleton today (because of fast-takeoff and no realistic prospects for remaining in control)

Regarding your parenthetical "because of", I think the "need" to design such a singleton comes from the present opportunity to build such a singleton, which may not last. For example, suppose your scenario of superintelligent AIs controlled by human owners become reality (putting aside my previous objection). At that time we can no longer directly build a singleton, and those AI/human systems may not be able to, or want to, merge into a singleton. They may instead just spread out into the universe in an out of control manner, burning the cosmic commons as they go.

There is a broad class of proposals in which an AI has a model of "what I would want" (either by the route that ordinary AI researchers find plausible, in which AI's concepts are reasonably aligned with human concepts

There are all kinds of ways for this to go badly wrong, which have been extensively discussed by Eliezer and others on LW. To summarize, the basic problem is that human concepts are too fuzzy and semantically dependent on how human cognition works. Given complexity and fragility of value and likely alien nature of AI cognition, it's unlikely that AIs will share our concepts closely enough for it to obtain a sufficiently accurate model of "what I would want" through this method. (ETA: Here is a particularly relevant post by Eliezer.)

It doesn't seem to be the case that you can't test such designs until you are dealing with superhuman AI---normal humans can reason about such concepts, as we do, and as long as you can design any AI which uses such concepts and isn't deliberately deceptive you can see whether it is doing something sensible.

My claim about not being able to test was limited to the black-box metaphilosophical AI, so doesn't apply here, which instead has other problems, mentioned above.

The more fundamental issue is just that we haven't thought about this much, and so without formal justification I am pretty dubious of any claimed taxonomy or fundamental difficulty.

Since you seem to bring up ideas that others have already considered and rejected, I wonder if perhaps you're underestimating how much we've thought about this? (Or were you already aware of their rejection and just wanted to indicate your disagreement?)

I think there are better indirect approaches to making the world better though (rather than directly launching in on human enhancement).

This is quite possible. I'm not arguing that directly pushing for human enhancement is the best current invention, just that it ought to be done at some point, prior to trying to build FAI.

White-box is, nevertheless, the accepted name for the concept he was referring to - probably as an antonym to black-box.

English. What can you do.

“White-box” isn’t an obvious antonym in the sense I think you want.

I actually checked Wikipedia before using the term, since I had the same thought as you, but "white-box testing" seems to be the most popular term (it's the title of the article and used throughout) in preference to "clear box testing" and a bunch of others that are in parenthesis under "also known as".

If we can get a safe AI, we've essentially done the whole of the work. Optimality can be tacked on easily at that point

I'm not seeing this. Suppose we've got a Oracle AI that's been safely boxed, which we can use to help us solve various technical problems. How do we get to optimality from there, before others people take our Oracle AI technology and start doing unsafe things with it? I've argued, in this post, that getting to optimality requires solving many hard philosophical problems, and it doesn't seem like having an AI that's merely "safe" helps much with that.

so some sort of balanced view of optimality will be needed

Sure, no argument there.