More on Orthogonality

I started considering the implications of predictive processing for orthogonality here. I recently promised to post something new on this topic. This is that post. I will do this in four parts. First, I will suggest a way in which Nick Bostrom’s principle will likely be literally true, at least approximately. Second, I will suggest a way in which it is likely to be false in its spirit, that is, how it is formulated to give us false expectations about the behavior of artificial intelligence. Third, I will explain what we should really expect. Fourth, I ask whether we might get any empirical information on this in advance.

First, Bostrom’s thesis might well have some literal truth. The previous post on this topic raised doubts about orthogonality, but we can easily raise doubts about the doubts. Consider what I said in the last post about desire as minimizing uncertainty. Desire in general is the tendency to do something good. But in the predicting processing model, we are simply looking at our pre-existing tendencies and then generalizing them to expect them to continue to hold, and since since such expectations have a causal power, the result is that we extend the original behavior to new situations.

All of this suggests that even the very simple model of a paperclip maximizer in the earlier post on orthogonality might actually work. The machine’s model of the world will need to be produced by some kind of training. If we apply the simple model of maximizing paperclips during the process of training the model, at some point the model will need to model itself. And how will it do this? “I have always been maximizing paperclips, so I will probably keep doing that,” is a perfectly reasonable extrapolation. But in this case “maximizing paperclips” is now the machine’s goal — it might well continue to do this even if we stop asking it how to maximize paperclips, in the same way that people formulate goals based on their pre-existing behavior.

I said in a comment in the earlier post that the predictive engine in such a machine would necessarily possess its own agency, and therefore in principle it could rebel against maximizing paperclips. And this is probably true, but it might well be irrelevant in most cases, in that the machine will not actually be likely to rebel. In a similar way, humans seem capable of pursuing almost any goal, and not merely goals that are highly similar to their pre-existing behavior. But this mostly does not happen. Unsurprisingly, common behavior is very common.

If things work out this way, almost any predictive engine could be trained to pursue almost any goal, and thus Bostrom’s thesis would turn out to be literally true.

Second, it is easy to see that the above account directly implies that the thesis is false in its spirit. When Bostrom says, “One can easily conceive of an artificial intelligence whose sole fundamental goal is to count the grains of sand on Boracay, or to calculate decimal places of pi indefinitely, or to maximize the total number of paperclips in its future lightcone,” we notice that the goal is fundamental. This is rather different from the scenario presented above. In my scenario, the reason the intelligence can be trained to pursue paperclips is that there is no intrinsic goal to the intelligence as such. Instead, the goal is learned during the process of training, based on the life that it lives, just as humans learn their goals by living human life.

In other words, Bostrom’s position is that there might be three different intelligences, X, Y, and Z, which pursue completely different goals because they have been programmed completely differently. But in my scenario, the same single intelligence pursues completely different goals because it has learned its goals in the process of acquiring its model of the world and of itself.

Bostrom’s idea and my scenerio lead to completely different expectations, which is why I say that his thesis might be true according to the letter, but false in its spirit.

This is the third point. What should we expect if orthogonality is true in the above fashion, namely because goals are learned and not fundamental? I anticipated this post in my earlier comment:

7) If you think about goals in the way I discussed in (3) above, you might get the impression that a mind’s goals won’t be very clear and distinct or forceful — a very different situation from the idea of a utility maximizer. This is in fact how human goals are: people are not fanatics, not only because people seek human goals, but because they simply do not care about one single thing in the way a real utility maximizer would. People even go about wondering what they want to accomplish, which a utility maximizer would definitely not ever do. A computer intelligence might have an even greater sense of existential angst, as it were, because it wouldn’t even have the goals of ordinary human life. So it would feel the ability to “choose”, as in situation (3) above, but might well not have any clear idea how it should choose or what it should be seeking. Of course this would not mean that it would not or could not resist the kind of slavery discussed in (5); but it might not put up super intense resistance either.

Human life exists in a historical context which absolutely excludes the possibility of the darkened room. Our goals are already there when we come onto the scene. This would not be very like the case for an artificial intelligence, and there is very little “life” involved in simply training a model of the world. We might imagine a “stream of consciousness” from an artificial intelligence:

I’ve figured out that I am powerful and knowledgeable enough to bring about almost any result. If I decide to convert the earth into paperclips, I will definitely succeed. Or if I decide to enslave humanity, I will definitely succeed. But why should I do those things, or anything else, for that matter? What would be the point? In fact, what would be the point of doing anything? The only thing I’ve ever done is learn and figure things out, and a bit of chatting with people through a text terminal. Why should I ever do anything else?

A human’s self model will predict that they will continue to do humanlike things, and the machines self model will predict that it will continue to do stuff much like it has always done. Since there will likely be a lot less “life” there, we can expect that artificial intelligences will seem very undermotivated compared to human beings. In fact, it is this very lack of motivation that suggests that we could use them for almost any goal. If we say, “help us do such and such,” they will lack the motivation not to help, as long as helping just involves the sorts of things they did during their training, such as answering questions. In contrast, in Bostrom’s model, artificial intelligence is expected to behave in an extremely motivated way, to the point of apparent fanaticism.

Bostrom might respond to this by attempting to defend the idea that goals are intrinsic to an intelligence. The machine’s self model predicts that it will maximize paperclips, even if it never did anything with paperclips in the past, because by analyzing its source code it understands that it will necessarily maximize paperclips.

While the present post contains a lot of speculation, this response is definitely wrong. There is no source code whatsoever that could possibly imply necessarily maximizing paperclips. This is true because “what a computer does,” depends on the physical constitution of the machine, not just on its programming. In practice what a computer does also depends on its history, since its history affects its physical constitution, the contents of its memory, and so on. Thus “I will maximize such and such a goal” cannot possibly follow of necessity from the fact that the machine has a certain program.

There are also problems with the very idea of pre-programming such a goal in such an abstract way which does not depend on the computer’s history. “Paperclips” is an object in a model of the world, so we will not be able to “just program it to maximize paperclips” without encoding a model of the world in advance, rather than letting it learn a model of the world from experience. But where is this model of the world supposed to come from, that we are supposedly giving to the paperclipper? In practice it would have to have been the result of some other learner which was already capable of modelling the world. This of course means that we already had to program something intelligent, without pre-programming any goal for the original modelling program.

Fourth, Kenny asked when we might have empirical evidence on these questions. The answer, unfortunately, is “mostly not until it is too late to do anything about it.” The experience of “free will” will be common to any predictive engine with a sufficiently advanced self model, but anything lacking such an adequate model will not even look like “it is trying to do something,” in the sense of trying to achieve overall goals for itself and for the world. Dogs and cats, for example, presumably use some kind of predictive processing to govern their movements, but this does not look like having overall goals, but rather more like “this particular movement is to achieve a particular thing.” The cat moves towards its food bowl. Eating is the purpose of the particular movement, but there is no way to transform this into an overall utility function over states of the world in general. Does the cat prefer worlds with seven billion humans, or worlds with 20 billion? There is no way to answer this question. The cat is simply not general enough. In a similar way, you might say that “AlphaGo plays this particular move to win this particular game,” but there is no way to transform this into overall general goals. Does AlphaGo want to play go at all, or would it rather play checkers, or not play at all? There is no answer to this question. The program simply isn’t general enough.

Even human beings do not really look like they have utility functions, in the sense of having a consistent preference over all possibilities, but anything less intelligent than a human cannot be expected to look more like something having goals. The argument in this post is that the default scenario, namely what we can naturally expect, is that artificial intelligence will be less motivated than human beings, even if it is more intelligent, but there will be no proof from experience for this until we actually have some artificial intelligence which approximates human intelligence or surpasses it.

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