The Blue-Minimizing Robot

Imagine a robot with a turret-mounted camera and laser. Each moment, it is programmed to move forward a certain distance and perform a sweep with its camera. As it sweeps, the robot continuously analyzes the average RGB value of the pixels in the camera image; if the blue component passes a certain threshold, the robot stops, fires its laser at the part of the world corresponding to the blue area in the camera image, and then continues on its way.

Watching the robot's behavior, we would conclude that this is a robot that destroys blue objects. Maybe it is a surgical robot that destroys cancer cells marked by a blue dye; maybe it was built by the Department of Homeland Security to fight a group of terrorists who wear blue uniforms. Whatever. The point is that we would analyze this robot in terms of its goals, and in those terms we would be tempted to call this robot a blue-minimizer: a machine that exists solely to reduce the amount of blue objects in the world.

Suppose the robot had human level intelligence in some side module, but no access to its own source code; that it could learn about itself only through observing its own actions. The robot might come to the same conclusions we did: that it is a blue-minimizer, set upon a holy quest to rid the world of the scourge of blue objects.

But now stick the robot in a room with a hologram projector. The hologram projector (which is itself gray) projects a hologram of a blue object five meters in front of it. The robot's camera detects the projector, but its RGB value is harmless and the robot does not fire. Then the robot's camera detects the blue hologram and zaps it. We arrange for the robot to enter this room several times, and each time it ignores the projector and zaps the hologram, without effect.

Here the robot is failing at its goal of being a blue-minimizer. The right way to reduce the amount of blue in the universe is to destroy the projector; instead its beams flit harmlessly through the hologram.

Again, give the robot human level intelligence. Teach it exactly what a hologram projector is and how it works. Now what happens? Exactly the same thing - the robot executes its code, which says to scan the room until its camera registers blue, then shoot its laser.

In fact, there are many ways to subvert this robot. What if we put a lens over its camera which inverts the image, so that white appears as black, red as green, blue as yellow, and so on? The robot will not shoot us with its laser to prevent such a violation (unless we happen to be wearing blue clothes when we approach) - its entire program was detailed in the first paragraph, and there's nothing about resisting lens alterations. Nor will the robot correct itself and shoot only at objects that appear yellow - its entire program was detailed in the first paragraph, and there's nothing about correcting its program for new lenses. The robot will continue to zap objects that register a blue RGB value; but now it'll be shooting at anything that is yellow.

The human-level intelligence version of the robot will notice its vision has been inverted. It will know it is shooting yellow objects. It will know it is failing at its original goal of blue-minimization. And maybe if it had previously decided it was on a holy quest to rid the world of blue, it will be deeply horrified and ashamed of its actions. It will wonder why it has suddenly started to deviate from this quest, and why it just can't work up the will to destroy blue objects anymore.

The robot goes to Quirinus Quirrell, who explains that robots don't really care about minimizing the color blue. They only care about status and power, and pretend to care about minimizing blue in order to impress potential allies.

The robot goes to Robin Hanson, who explains that there are really multiple agents within the robot. One of them wants to minimize the color blue, the other wants to minimize the color yellow. Maybe the two of them can make peace, and agree to minimize yellow one day and blue the next?

The robot goes to Anna Salamon, who explains that robots are not automatically strategic, and that if it wants to achieve its goal it will have to learn special techniques to keep focus on it.

I think all of these explanations hold part of the puzzle, but that the most fundamental explanation is that the mistake began as soon as we started calling it a "blue-minimizing robot". This is not because its utility function doesn't exactly correspond to blue-minimization: even if we try to assign it a ponderous function like "minimize the color represented as blue within your current visual system, except in the case of holograms" it will be a case of overfitting a curve. The robot is not maximizing or minimizing anything. It does exactly what it says in its program: find something that appears blue and shoot it with a laser. If its human handlers (or itself) want to interpret that as goal directed behavior, well, that's their problem.

It may be that the robot was created to achieve a specific goal. It may be that the Department of Homeland Security programmed it to attack blue-uniformed terrorists who had no access to hologram projectors or inversion lenses. But to assign the goal of "blue minimization" to the robot is a confusion of levels: this was a goal of the Department of Homeland Security, which became a lost purpose as soon as it was represented in the form of code.

The robot is a behavior-executor, not a utility-maximizer.

In the rest of this sequence, I want to expand upon this idea. I'll start by discussing some of the foundations of behaviorism, one of the earliest theories to treat people as behavior-executors. I'll go into some of the implications for the "easy problem" of consciousness and philosophy of mind. I'll very briefly discuss the philosophical debate around eliminativism and a few eliminativist schools. Then I'll go into why we feel like we have goals and preferences and what to do about them.

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The conclusion I'd draw from this essay is that one can't necessarily derive a "goal" or a "utility function" from all possible behavior patterns. If you ask "What is the robot's goal?", the answer is, "it doesn't have one," because it doesn't assign a total preference ordering to states of the world. At best, you could say that it prefers state [I SEE BLUE AND I SHOOT] to state [I SEE BLUE AND I DON'T SHOOT]. But that's all.

This has some implications for AI, I think. First of all, not every computer program has a goal or a utility function. There is no danger that your TurboTax software will take over the world and destroy all human life, because it doesn't have a general goal to maximize the number of completed tax forms. Even rather sophisticated algorithms can completely lack goals of this kind -- they aren't designed to maximize some variable over all possible states of the universe. It seems that the narrative of unfriendly AI is only a risk if an AI were to have a true goal function, and many useful advances in artificial intelligence (defined in the broad sense) carry no risk of this kind.

Do humans have goals? I don't know; it's plausible that we have goals that are complex and hard to define succinctly, and it's also plausible that we don't have goals at all, just sets of instructions like "SHOOT AT BLUE." The test would seem to be if a human goal of "PROMOTE VALUE X" continues to imply behaviors in strange and unfamiliar circumstances, or if we only have rules of behavior in a few common situations. If you can think clearly about ethics (or preferences) in the far future, or the distant past, or regarding unfamiliar kinds of beings, and your opinions have some consistency, then maybe those ethical beliefs or preferences are goals. But probably many kinds of human behavior are more like sets of instructions than goals.

At best, you could say that it prefers state [I SEE BLUE AND I SHOOT] to state [I SEE BLUE AND I DON'T SHOOT]. But that's all.

No; placing a blue-tinted mirror in front of him will have him shoot himself even though that greatly diminishes his future ability to shoot. Generally a generic program really can't be assigned any nontrivial utility function.

Destroying the robot greatly diminishes its future ability to shoot, but it would also greatly diminishes its future ability to see blue. The robot doesn't prefer 'shooting blue' to 'not shooting blue', it prefers 'seeing blue and shooting' to 'seeing blue and not shooting'.

So the original poster was right.

Edit: I'm wrong, see below

If the robot knows that its camera is indestructible but its gun isn't, it would still shoot at the mirror and destroy only its gun.

It seems that the narrative of unfriendly AI is only a risk if an AI were to have a true goal function, and many useful advances in artificial intelligence (defined in the broad sense) carry no risk of this kind.

What does it mean for a program to have intelligence if it does not have a goal? (or have components that have goals)

The point of any incremental intelligence increase is to let the program make more choices, and perhaps choices at higher levels of abstraction. Even at low intelligence levels, the AI will only 'do a good job' if the basis of those choices adequately matches the basis we would use to make the same choice. (a close match at some level of abstraction below the choice, not the substrate and not basic algorithms)

Creating 'goal-less' AI still has the machine making more choices for more complex reasons, and allows for non-obvious mismatches between what it does and what we intended it to do.

Yes, you can look at paperclip-manufacturing software and see that it is not a paper-clipper, but some component might still be optimizing for something else entirely. We can reject the anthropomorphically obvious goal and there can still be an powerful optimization process that affects the total system, at the expense of both human values and produced paperclips.

Consider automatic document translation. Making the translator more complex and more accurate doesn't imbue it with goals. It might easily be the case that in a few years, we achieve near-human accuracy at automatic document translation without major breakthroughs in any other area of AI research.

Making it more accurate is not the same as making it more intelligent. The question is: How does making something "more intelligent" change the nature of the inaccuracies? In translation especially there can be a bias without any real inaccuracy .

Goallessness at the level of the program is not what makes translators safe. They are safe because neither they nor any component is intelligent.

Most professional computer scientists and programmers I know routinely talk about "smart", "dumb", "intelligent" etc algorithms. In context, a smarter algorithm exploits more properties of the input or the problem. I think this is a reasonable use of language, and it's the one I had in mind.

(I am open to using some other definition of algorithmic intelligence, if you care to supply one.)

I don't see why making an algorithm smarter or more general would make it dangerous, so long as it stays fundamentally a (non-self-modifying) translation algorithm. There certainly will be biases in a smart algorithm. But dumb algorithms and humans have biases too.

I generally go with cross domain optimization power. http://wiki.lesswrong.com/wiki/Optimization_process Note that optimization target is not the same thing as a goal, and the process doesn't need to exist within obvious boundaries. Evolution is goalless and disembodied.

If an algorithm is smart because a programmer has encoded everything that needs to be known to solve a problem, great. That probably reduces potential for error, especially in well-defined environments. This is not what's going on in translation programs, or even the voting system here. (based on reddit) As systems like this creep up in complexity, their errors and biases become more subtle. (especially since we 'fix' them so that they usually work well) If an algorithm happens to be powerful in multiple domains, then the errors themselves might be optimized for something entirely different, and perhaps unrecognizable.

By your definition I would tend to agree that they are not dangerous, so long as their generalized capabilities are below human level, (seems to be the case for everything so far) with some complex caveats. For example 'non-self-modifying' is a likely false sense of security. If an AI has access to a medium which can be used to do computations, and the AI is good at making algorithms, then it could (Edit: It could build a powerful if not superintelligent program.)

Also, my concern in this thread has never been about the translation algorithm, the tax program, or even the paperclipper. It's about some sub-process which happens to be a powerful optimizer. (in a hypothetical situation where we do more AI research on the premise that it is safe if it is in a goalless program.

What does it mean for a program to have intelligence if it does not have a goal?

This is a very interesting question, thanks for making me think about it.

(Based on your other comments elsewhere in this thread), it seems like you and I are in agreement that intelligence is about having the capability to make better choices. That is, two agents given an identical problem and identical resources to work with, the agent that is more intelligent is more likely to make the "better" choice.

What does "better" mean here? We need to define some sort of goal and then compare the outcome of their choices and how closely those outcome matches those goals. I have a couple of disorganized thoughts here:

  • The goal is just necessary for us, outsiders, to compare the intelligence of the two agents. The goal is not necessary for the existence of intelligence in the agents if no one's interested in measuring their intelligence.
  • Assuming the agents are cooperative, you can temporarily assign subgoals. For example, perhaps you and I would like to know which one of us is smarter. You and I might have many different goals, but we might agree to temporarily take on a similar goal (e.g. win this game of chess, or get the highest amount of correct answers on this IQ test, etc.) so that our intelligence can be compared.
  • The "assigning" of goals to an intelligence strongly implies to me that goals are orthogonal to intelligence. Intelligence is the capability to fulfil any general goal, and it's possible for someone to be intelligent even if they do not (currently, or ever) have any goals. If we come up with a new trait called Sodadrinkability which is the capability to drink a given soda, one can say that I possess Sodadrinkability -- that I am capable of drinking a wide range of possible sodas provided to me -- even if I do not currently (or ever) have any sodas to drink.

Let me suggest that the difference between goal-less behavior and goal-driven behavior, is that goal-driven behavior seeks means to attain its end. The means will vary with circumstances, while the end remains relatively invariant. Another indication of goal-driven behavior is that means are often prepared in anticipation of need, rather than in response to present need.

I said "relatively invariant" because goals can be and often are heirarical. An example was outlined by Maslow in his "A Theory of Human Motivation" in the Psychological Review (1943). Maslow aside, in problem solving, we often resort to staged solutions in which the means to a higher order goal become a new sub-goal and so on iteratively -- until we reach low level goals within our immediate grasp.

A second point is that terms such as "purposeful" and "goal-seeking" are analogously predicated. To be analogousley predicated, a term is applied to differnt cases with a meaning that is partly the same and partly different. Thus, a goal-seeking robot is not goal-seeking because it intends any goals of its own, but because it is the vehicle by which designer seeks to effect his or her goals. In the parable, if the goal was the destruction of blue uniformed enemies, that goal was only intended by the robots creators. Since the robot is an instantiated means of attaining that gosl, we may speak, analogously, of it as having the same goal. The important point is that we mean different things in saying "the designer has a goal" and "the robot has a goal." Each works toward same end (so the meaning iis partly the same), but only the designer intends that end (so the meaning iis partly different). (BTW this kind of analogy is an "analogy of attribution.")

The fact that the robot is ineffective in attaining its end is a side issue that might be solved by employing better algoritms (edge and pattern recognition, etc.) There is no evidence that better algorithms will give the robot intentions in the sense that the designer has intentions.

Also, you misspelled my name - it's Quirinus, not Quirinius.

Hijacking top comment... To finish reading Yvain's sequence, check out the corresponding sequence page).

The robot is not consequentialist, its decisions are not controlled by the dependence of facts about the future on its decisions.

Good point, but the fact that humans are consequentialists (at least partly) doesn't seem to make the problem much easier. Suppose we replace Yvain's blue-minimizer robot with a simple consequentialist robot that has the same behavior (let's say it models the world as a 2D grid of cells that have intrinsic color, it always predicts that any blue cell that it shoots at will turn some other color, and its utility function assigns negative utility to the existence of blue cells). What does this robot "actually want", given that the world is not really a 2D grid of cells that have intrinsic color?

What does this robot "actually want", given that the world is not really a 2D grid of cells that have intrinsic color?

Who cares about the question what the robot "actually wants"? Certainly not the robot. Humans care about the question what they "actually want", but that's because they have additional structure that this robot lacks. But with humans, you're not limited to just looking at what they do on auto-pilot; instead, you can just ask the aforementioned structure when you run into problems like this. For example, if you asked me what I really wanted under some weird ontology change, I could say, "I have some guesses, but I don't really know; I would like to defer to a smarter version of me". That's how I understand preference extrapolation: not as something that looks at what your behavior suggests that you're trying to do and then does it better, but as something that poses the question of what you want to some system you'd like to answer the question for you.

It looks to me like there's a mistaken tendency among many people here, including some very smart people, to say that I'd be irrational to let my stated preferences deviate from my revealed preferences; that just because I seem to be trying to do something (in some sense like: when my behavior isn't being controlled much by the output of moral philosophy, I can be modeled as a relatively good fit to a robot with some particular utility function), that's a reason for me to do it even if I decide that I don't want to. But rational utility maximizers get to be indifferent to whatever the heck they want, including their own preferences, so it's hard for me to see why the underdeterminedness of the true preferences of robots like this should bother me at all.

Insert standard low confidence about me posting claims on complicated topics that others seem to disagree with.

In other words, our "actual values" come from our being philosophers, not our being consequentialists.

It seems plausible to me, and I'm not sure that "many" others do disagree with you.

That would imply a great diversity of value systems, because philosophical intuitions differ much more from person to person than primitive desires. Some of these value systems (maybe including yours) would be simple, some wouldn't. For example, my "philosophical" values seem to give large weight to my "primitive" values.

preference extrapolation: not as something that looks at what your behavior suggests that you're trying to do and then does it better, but as something that poses the question of what you want to some system you'd like to answer the question for you

That might be a procedure that generates human preference, but it is not a general preference extrapolation procedure. E.g suppose we replace Wei Dai's simple consequentialist robot with a robot that has similar behavior, but that also responds to the question, "What system do you want to answer the question of what you want for you?" with the answer, "A version of myself better able to answer that question. Maybe it should be smarter and know more things and be nicer to strangers and not have scope insensitivity and be less prone to skipping over invisible moral frameworks and have conecepts that are better defined over attribute space and be automatically strategic and super commited and stuff like that? But since I'm not that smart and I pass over moral frameworks and stuff, eveything I just said is probably insufficient to specify the right thing. Maybe you can look at my source code and figure out what I mean by right and then do the thing that a person who better understood that would do?" And then goes right back to zapping blue.

Actually, this notion of consequentialism gives a new and the only clue I know of about how to infer agent goals, or how to constrain the kinds of considerations that should be considered goals, as compared to the other stuff that moves your action incidentally, such as psychological drives or laws of physics. I wonder if Eliezer had this insight before, given that he wrote a similar comment to this thread. I wasn't ready to see this idea on my own until a few weeks ago, and this thread is the first time I thought about the question given the new framework, and saw the now-obvious construction. This deserves more than a comment, so I'll be working on a two-post sequence to write this up intelligibly. Or maybe it's actually just stupid, I'll try to figure that out.


(A summary from my notes, in case I get run over by a bus; this uses a notion of "dependence" for which a toy example is described in my post on ADT, but which is much more general: )

The idea of consequentialism, of goal-directed control, can be modeled as follows. If a fact A is controlled by (can be explained/predicted based on) a dependence F: A->O, then we say that A is a decision (action) driven by a consequentialist consideration F, which in turn looks at how A controls the morally relevant fact O.

For a given decision A, there could be many different morally relevant facts O such that the dependence A->O has explanatory power about A. The more about A can a dependence A->O explain, the more morally relevant O is. Finding highly relevant facts O essentially captures A's goals.

This model has two good properties. First, logical omniscience (in particular, just knowledge of actual action) renders the construction unusable, since we need to see dependencies A->O as ambient concepts explaining A, so both A and A->O need to remain potentially unknown. (This is the confusing part. It also lends motivation to the study of complete collection of moral arguments and the nature of agent-provable collection of moral arguments.)

Second, action (decision) itself, and many other facts that control the action but aren't morally relevant, are distinguished by this model from the things that are. For example, A can't be morally relevant, for that would require the trivial identity dependence A->A to explain A, which it can't, since it's too simple. Similarly for other stuff in simple relationship with A: the relationship between A and a fact must be in tune with A for the fact to be morally relevant, it's not enough for the fact itself to be in tune with A.

This question doesn't require a fixed definition for a goal concept, instead it shows how various concepts can be regarded as goals, and how their suitability for this purpose can be compared. The search for better morally relevant facts is left open-ended.

I very much look forward to your short sequence on this. I hope you will also explain your notion of dependence in detail.

For the record, I mostly completed a draft of a prerequisite post (first of the two I had in mind) a couple of weeks ago, and it's just no good, not much better than what one would take away from reading the previously published posts, and not particularly helpful in clarifying the intuition expressed in the above comments. So I'm focusing on improving my math skills, which I expect will help with formalization/communication problem (given a few months), as well as with moving forward. I might post some version of the post, but it seems it won't be able to serve the previously intended purpose.

Bummer.

As for communication, it would help me (at least) if you used words in their normal senses unless they are a standard LW term of art (e.g. 'rationalist' means LW rationalist not Cartesian rationalist ) or unless you specify that you're using the term in an uncommon sense.

it would help me (at least) if you used words in their normal senses

Don't see how this is related to this thread, and correspondingly what kinds of word misuse you have in mind.

It isn't related to this thread. I was thinking of past confusions between us over 'metaethics' and 'motivation' and 'meaning' where I didn't realize until pretty far into the discussion that you were using these terms to mean something different than they normally mean. I'd generally like to avoid that kind of thing; that's all I meant.

Well, I'm mostly not interested in the concepts corresponding to how these words are "normally" used. The miscommunication problems resulted from both me misinterpreting your usage, and your misinterpreting my usage. I won't be misinterpreting your usage in similar cases in the future, as I now know better what you mean by which words, and in my own usage, as we discussed a couple of times, I'll be more clear through using disambiguating qualifiers, which in most cases amounts to writing word "normative" more frequently.

(Still unclear/strange why you brought it up in this particular context, but no matter...)

Yup, that sounds good.

I brought it up because you mentioned communication, and your comment showed up in my LW inbox today.

steven0461's comment notwithstanding, I can take a guess at what the robot actually wants. I think it wants to take the action that will minimize the number of blue cells existing in the world, according to the robot's current model of the world. That rule for choosing actions probably doesn't correspond to any coherent utility function over the real world, but that's not really a surprise.

The interesting question that you probably meant to ask is whether the robot's utility function over its model of the world can be converted to a utility function over the real world. But the robot won't agree to any such upgrade, so the question is kinda moot.

That might sound hopeless for CEV, but fortunately humans aren't consequentialists with a fixed model of the world. Instead they seem to be motivated by pleasure and pain, which you can't disprove out of existence by coming up with a better model. So maybe there's hope in that direction.

What does this robot "actually want"

The robot wants to minimize the amount of blue it sees in its grid representation of the world. It can do this by affecting the world with a laser. But it could also change its camera system so that it sees less blue. If there is no term in the utility function that says that the grid has to model reality, then both approaches are equally valid.

To avoid SEEING blue things. If the model is good enough for it it'd search out a mirror and laser it's own camera so that it could NEVER see a blue pixel again.

This can be modelled using human empathy by equating the sensation of seeing blue with pain. You don't care to minimize damage to your body (if it's not somehting that actually cripples you), but you care about not getting the signal about it happening, and your reaction to a pill that turned you masochist would be very different than your reaction to a murder pill.

Edit: huh? I am surprised that this is downvoted, and the most probable reason is that I'm wrong in some obvious way that I can't see, can someone please tell me how? (Or maybe my usage of empathy was interpreted way to literally. )

(Reading this comment first might be helpful.)

To answer your thought experiment. It doesn't matter what the agent thinks it's acting based on, we look at it from the outside instead (but using a particular definition/dependence that specifies the agent), and ask how its action depends on the dependence of the actual future on its actual action. Agent's misconceptions don't enter this question. If the misconceptions are great, it'll turn out that the dependence of actual future on agent's action doesn't control its action, or controls it in some unexpected way. Alternatively, we could say that it's not the actual future that is morally relevant for the agent, but some other strange fact, in which case the agent could be said to be optimizing a world that is not ours. From yet another perspective, the role of the action could be played by something else, but then it's not clear why we are considering such a model and talking about this particular actual agent at the same time.

Is that something you can see from the outside? If I argmax over actions in expected-paper-clips or over updateless-prior-expected-paper-clips, how can you translate my black box behavior over possible worlds into the dependence of my behavior on the dependence of the worlds on my behavior?

See the section "Utility functions" of this post: it shows how a dependence between two fixed facts could be restored in an ideal case where we can learn everything there is to learn about it. Similarly, you could consider the fact of which dependence holds between two facts, with various specific functions as its possible values, and ask what can you infer about that other fact if you assume that the dependence is given by a certain function.

More generally, a dependence follows possible inferences, things that could be inferred about one fact if you learn new things about the other fact. It needs to follow all of such inferences, to the best of agent's ability, otherwise it won't be right and you'll get incorrect decisions (counterfactual models).

Edit: Actually, never mind, I missed your point. Will reply again later. (Done.)

What does this robot "actually want", given that the world is not really a 2D grid of cells that have intrinsic color?

Our world is not really a 2D grid, its world could be. It won't be a consequentialist about our world then, for that requires the dependence of its decisions on the dependence of our world on its decisions. It looks like the robot you described wants to minimize the 2D grid greenness, and would do that sensibly in the context of 2D grid world, or any world that can influence the 2D grid world. For the robot, our world doesn't exist in the same sense as 2D grid world doesn't exist for us, even though we could build an instance of the robot in our world. If we do build such an instance, the robot, if extremely rational and not just acting on heuristics adapted for its natural habitat, could devote its our-worldly existence to finding ways of acausally controlling its 2D world. For example, if there are some 2D-worlders out there simulating our world, it could signal to them something that is expected to reduce greenness.

(This all depends on the details of robot's decision-making tools, of course. It could really be talking about our world, but then its values collapse, and it could turn out to not be a consequentialist after all, or optimizing something very different, to the extent the conflict in the definitions is strong.)

I'll be interested to see where you go with this, but it seems to me that saying, "look, this is the program the robot runs, therefore it doesn't really have a goal", is exactly like saying "look, it's made of atoms, therefore it doesn't really have a goal".

Goals are precisely explained (like rainbows), and not explained away (like kobolds), as the controlled variables of control systems. This robot is such a system. The hypothetical goal of its designers at the Department of Homeland Security is also a goal. That does not make the robot's goal not a goal; it just makes it a different goal.

We feel like we have goals and preferences because we do, in fact, have goals and preferences, and we not only have them, but we are also aware of having them. The robot is not aware of having the goal that it has. It merely has it.

First of all, your control theory work was...not exactly what started me thinking along these lines, but what made it click when I realized the lines I had been thinking along were similar to the ones I had read about in one of your introductory posts about performing complex behaviors without representations. So thank you.

Second - When you say the robot has a "different goal", I'm not sure what you mean. What is the robot's goal? To follow the program detailed in the first paragraph?

Let's say Robot-1 genuinely has the goal to kill terrorists. If a hacker were to try to change its programming to "make automobiles" instead, Robot-1 would do anything it could to thwart the hacker; its goal is to kill terrorists, and letting a hacker change its goal would mean more terrorists get left alive. This sort of stability, in which the preference remains a preference regardless of context are characteristic of my definition of "goal".

This "blue-minimizing robot" won't display that kind of behavior. It doesn't thwart the person who places a color inversion lens on it (even though that thwarts its stated goal of "minimizing blue"), and it wouldn't try to take the color inversion lens off even if it had a manipulator arm. Even if you claim its goal is just to "follow its program", it wouldn't use its laser to stop someone walking up to it and changing its program, which means its program no longer got followed.

This isn't just a reduction of a goal to a program: predicting the robot's goal-based behavior and its program-based behavior give different results.

If goals reduce to a program like the robot's in any way, it's in the way that Einsteinian mechanics "reduce" to Newtonian mechanics - giving good results in most cases but being fundamentally different and making different predictions on border cases. Because there are other programs that goals do reduce to, like the previously mentioned Robot-1, I don't think it's appropriate to call what the blue-minimizer is doing a "goal".

If you still disagree, can you say exactly what goal you think the robot is pursuing, so I can examine your argument in more detail?

When you say the robot has a "different goal", I'm not sure what you mean. What is the robot's goal? To follow the program detailed in the first paragraph?

The robot's goal is not to follow its own program. The program is simply what the robot does. In the environment it is designed to operate in, what it does is destroy blue objects. In the vocabulary of control theory, the controlled variable is the number of blue objects, the reference value is zero, the difference between the two is the error, firing the laser is the action is takes when the error is positive, and the action has the effect of reducing the error. The goal, as with any control system, is to keep the error at zero. It does not have an additional goal of being the best destroyer of blue objects possible. Its designers might have that goal, but if so, that goal is in the designers, not in the system they have designed.

In an environment containing blue objects invulnerable to laser fire, the robot will fail to control the number of blue objects at zero. That does not make it not a control system, just a control system encountering disturbances it is unable to control. To ask whether it is still a control system veers into a purely verbal argument, like asking whether a table is still a table if one leg has broken off and it cannot stand upright.

People are more complex. They have (according to PCT) a large hierarchy of control systems (very broad, but less than a dozen levels deep), in which the reference signal for each controller is set by the output signals of higher level controllers. (At the top, reference signals are presumably hard-wired, and at the bottom, output signals go to organs not made of neurons -- muscles, mainly.) In addition, the hierarchy is subject to reorganisation and other forms of adaptation. The adaptations present to consciousness are the ability to think about our goals, consider whether we are doing the best things to achieve them, and change what we are doing. The robot in the example cannot do this.

You might be thinking of "goal" as meaning this sort of conscious, reflective, adaptive attempt to achieve what we "really" want, but I find that too large and fuzzy a concept. It leads into a morass of talk about our "real" goals vs. the goals we think we have, self-reflexive decision theory, extreme thought experiments, and so on. A real science of living things has to start smaller, with theories and observations that can be demonstrated as surely and reproducibly as the motion of balls rolling down inclined planes.

(ETA: When the neuroscience fails to discover this huge complex thing that never carved reality at the joints in the first place, people respond by saying it doesn't exist, that it went the way of kobolds rather than rainbows.)

Maybe you're also thinking of this robot's program as a plain stimulus-response system, as in the behaviourist view of living systems. But what makes it a control system is the environment it is embedded in, an environment in which shooting at blue objects destroys them.

If goals reduce to a program like the robot's in any way, it's in the way that Einsteinian mechanics "reduce" to Newtonian mechanics - giving good results in most cases but being fundamentally different and making different predictions on border cases.

If I replace "program" by "behaviourism", then I would say that it is behaviourism that is explained away by PCT.

Now I'm very confused. I understand that you think humans are PCT systems and that you have some justifications for that. But unlike humans, we know exactly what motivates this robot (the program in the first paragraph) and it doesn't contain a controlled variable corresponding to the number of blue objects, or anything else that sounds PCT.

So are you saying that any program can be modeled by PCT better than by looking at the program itself, or that although this particular robot isn't PCT, a hypothetical robot that was more reflective of real human behavior would be?

As for goals, if I understand your definition correctly, even a behaviorist system could be said to have goals (if you reinforce it every time it pulls the lever, then its new goal will be to pull a lever). If that's your definition, I agree that this robot has goals, and I would rephrase my thesis as being that those goals are not context-independent and reflective.

So are you saying that any program can be modeled by PCT better than by looking at the program itself, or that although this particular robot isn't PCT, a hypothetical robot that was more reflective of real human behavior would be?

I am saying that this particular robot (without the add-on human module) is a control system. It consists of nothing more than that single control system. It contains no representation of any part of itself. It does not reflect on its nature, or try to find other ways of achieving its goal.

The hierarchical arrangement of control systems that HPCT (Hierarchical PCT) ascribes to humans and other living organisms, is more complex. Humans have goals that are instrumental towards other goals, and which are discarded as soon as they become ineffective for those higher-level goals.

As for goals, if I understand your definition correctly, even a behaviorist system could be said to have goals (if you reinforce it every time it pulls the lever, then its new goal will be to pull a lever). If that's your definition, I agree that this robot has goals, and I would rephrase my thesis as being that those goals are not context-independent and reflective.

Behaviourism is a whole other can of worms. It models living organisms as stimulus-response systems, in which outputs are determined by perceptions. PCT is the opposite: perceptions are determined by outputs.

I agree with you that behaviorism and PCT are different, which is why I don't understand why you're interpreting the robot as PCT and not behaviorist. From the program, it seems pretty clearly (STIMULUS: see blue -> RESPONSE: fire laser) to me.

Do you have GChat or any kind of instant messenger? I feel like real-time discussion might be helpful here, because I'm still not getting it.

I agree with you that behaviorism and PCT are different, which is why I don't understand why you're interpreting the robot as PCT and not behaviorist. From the program, it seems pretty clearly (STIMULUS: see blue -> RESPONSE: fire laser) to me.

Well, your robot example was an intuition pump constructed so as to be as close as possible to stimulus-response nature. If you consider something only slightly more complicated the distinction may become clearer: a room thermostat. Physically ripped out of its context, you can see it as a stimulus-response device. Temperature at sensor goes above threshold --> close a switch, temperature falls below threshold --> open the switch. You can set the temperature of the sensor to anything you like, and observe the resulting behaviour of the switch. Pure S-R.

In context, though, the thermostat has the effect of keeping the room temperature constant. You can no longer set the temperature of the sensor to anything you like. Put a candle near it, and the temperature of the rest of the room will fall while the sensor remains at a constant temperature. Use a strong enough heat source or cold source, and you will be able to overwhelm the control system's efforts to maintain a constant temperature, but this fails to tell you anything about how the control system works normally. Do the analogous thing to a living organism and you either kill it or put it under such stress that whatever you observe is unlikely to tell you much about its normal operation -- and biology and psychology should be about how organisms work, not how they fail under torture.

Did you know that lab rats are normally starved until they have lost 20% of their free-feeding weight, before using them in behavioural experiments?

Here's a general block diagram of a control system. The controller is the part above the dotted line and its environment the part below (what would be called the plant in an industrial context). R = reference, P = perception, O = output, D = disturbance (everything in the environment besides O that affects the perception). I have deliberately drawn this to look symmetrical, but the contents of those two boxes makes its functioning asymmetrical. P remains close to R, but O and D need have no visible relationship at all.

                  R |
                    |
                    V
                +-------+
                |       |
           +--->|       |----+
           |    |       |    |
           ^    +-------+    v
           |                 |
....... P  | ............... | O .......
           |                 |
           ^    +-------+    v
           |    |       |    |
           +----|       |<---+
                |       |
                +-------+
                    ^
                    |
                  D |

When you are dealing with a living organism, R is somewhere inside it. You probably cannot measure it even if you know it exist. (E.g. just what and where, physically, is the set point for deep body temperature in a mammal? Not an easy question to answer.) You may or may not know what P is -- what the organism is actually sensing. It is important to realise that when you perform an experiment on an animal, you have no way of setting P. All you can do is create a disturbance D that may influence P. D, from a behavioural point of view, is the "stimulus" and O, the creature's action on its environment, is the "response". the behaviourist description of the situation is this:

                +-------+
            D   |       |   O
          ----->|       |----->
                |       |
                +-------+

This is simply wrong. The system does not work like that and cannot be understood like that. It may look as if D causes O, but that is like thinking that a candle put in a certain place chills the room, a fact that will seem mysterious and paradoxical when you do not know that the thermostat is present, and will only be explained by discovering the actual mechanism, discarding the second diagram in favour of the first. No amount of data collection will help until one has made that change. This is why correlations are so lamentably low in psychological experiments.

Do you have GChat or any kind of instant messenger?

No, I've never used any of those systems. I prefer a medium in which I can take my time to work out exactly what I want to say.

Okay, we agree that the simple robot described here is behaviorist and the thermostat is PCT. And I certainly see where you're coming from with the rats being PCT because hunger only works as a motivator if you're hungry. But I do have a few questions:

  1. There are some things behaviorism can explain pretty well that I don't know how to model in PCT. For example, consider heroin addiction. An animal can go its whole life not wanting heroin until it's exposed to some. Then suddenly heroin becomes extraordinarily motivating and it will preferentially choose shots of heroin to food, water, or almost anything else. What is the PCT explanation of that?

  2. I'm not entirely sure which correlation studies you're talking about here; most psych studies I read are done in an RCT type design and so use p-values rather than r-values; they can easily end up with p < .001 if they get a large sample and a good hypothesis. Some social psych studies work off of correlations (eg correlation between being observer-rated attractiveness and observer-rated competence at a skill); correlations are "lamentably low" in social psychology because high level processes (like opinion formation, social interaction, etc.) have a lot of noise. Are there any PCT studies of these sorts of processes (not simple motor coordination problems) that have any higher correlation than standard models do? Any with even the same level of correlation?

  3. What's the difference between control theory and stimulus-response in a context? For example, if we use a simplified version of hunger in which the hormone leptin is produced in response to hunger and the hormone ghrelin is produced in response to satiety, we can explain this in two ways: the body is trying to PCT itself to the perfect balance of leptin and ghrelin, or in the context of the stimulus leptin the response of eating is rewarded and in the context of the stimulus ghrelin the response of eating is punished. Are these the same theory, or are there experiments that would distinguish between them? Do you know of any?

  4. Does PCT still need reinforcement learning to explain why animals use some strategies and not others to achieve equilibrium? For example, when a rat in a Skinner box is hungry (ie its satiety variable has deviated in the direction of hunger), and then it presses a lever and gets a food pellet and its satiety variable goes back to its reference range, would PCTists explain that as getting rewarded for pressing the lever and expect it to press the lever again next time it's hungry?

An animal can go its whole life not wanting heroin until it's exposed to some. Then suddenly heroin becomes extraordinarily motivating and it will preferentially choose shots of heroin to food, water, or almost anything else

Rats don't always choose drugs over everything else

Summary: An experimenter thought drug addiction in rats might be linked to being kept in distressing conditions, made a Rat Park to test the idea, and found that the rats in the enriched Rat Park environment ignored the morphine on offer.

EDIT: apparently the study had methodological issues and hasn't been replicated, making the results somewhat suspect, as pointed out by Yvain below

I hate to admit I get science knowledge from Reddit, but the past few times this was posted there it was ripped apart by (people who claimed to be) professionals in the field - riddled with metholodogical errors, inconsistently replicated, et cetera. The fact that even its proponents admit the study was rejected by most journals doesn't speak well of it.

I think it's very plausible that situation contributes to addiction; we know that people in terrible situations have higher discount rates than others and so tend to short-term thinking that promotes that kind of behavior, and certainly they have fewer reasons to try to live life as a non-addict. But I think the idea that morphine is no longer interesting and you can't become addicted when you live a stimulating life is wishful thinking.

Damn. Oh well, noted and edited in to the original comment.

Well, like I said, all I have to go on is stuff people said on Reddit and one failed replication study I was able to find somewhere by a grad student of the guy who did the original research. The original research is certainly interesting and relevant and does speak to the problems with a very reductionist model.

This actually gets to the same problem I'm having looking up stuff on perceptual control theory, which is that I expect a controversial theory to be something where there are lots of passionate arguments on both sides, but on both PCT and Rat Park, when I've tried to look them up I get a bunch of passionate people arguing that they're great, and then a few scoffs from more mainstream people saying "That stuff? Nah." without explaining themselves. I don't know whether it's because of Evil Set-In-Their-Ways Mainstream refusing to acknowledge the new ideas, or whether they're just so completely missing the point that people think it's not worth their while to respond. It's a serious problem and I wish that "skeptics" would start addressing this kind of thing instead of debunking ghosts for the ten zillionth time.

Just a brief note to say that I do intend to get back to this, but I've been largely offline since the end of last week, and will be very busy at least until the end of this month on things other than LessWrong. I would like to say a lot more about PCT here than I have in the past (here, here, and in various comments), but these things take me long periods of concentrated effort to write.

BTW, one of the things I'm busy with is PCT itself, and I'll be in Boulder, Colorado for a PCT-related meeting 28-31 July, and staying on there for a few days. Anyone around there then?

For example, when a rat in a Skinner box is hungry (ie its satiety variable has deviated in the direction of hunger), and then it presses a lever and gets a food pellet and its satiety variable goes back to its reference range, would PCTists explain that as getting rewarded for pressing the lever and expect it to press the lever again next time it's hungry?

The PCT learning model doesn't require reinforcement at the control level, as its model of memory is a mapping from reference levels to predicted levels of other variables. I.e., when the rat notices that the lever-pressing is paired with food, a link is made between two perceptual variables: the position of the lever, and the availability of food. This means that the rat can learn that food is available, even when it's not hungry.

Where reinforcement is relevant to PCT is in the strength of the linkage and in the likelihood of its being recorded. If the rat is hungry, then the linkage is more salient, and more likely to be learned.

Notice though, that again the animal's internal state is of primary importance, not the stimulus/response. In a sense, you could say that you can teach an animal that a stimulus and response are paired, but this isn't the same as making the animal behave. If we starved you and made you press a lever for your food, you might do it, or you might tell us to fork off. Yet, we don't claim that you haven't learned that pressing the lever leads to food in that case.

(As Richard says, it's well established that you can torture living creatures until they accede to your demands, but it won't necessarily tell you much about how the creature normally works.)

In any case, PCT allows for the possibility of learning without "reinforcement" in the behaviorist sense, unless you torture the definition of reinforcement to the point that anything is reinforcement.

Regarding the leptin/ghrelin question, my understanding is that PCT as a psych-physical model primarily addresses those perceptual variables that are modeled by neural analog -- i.e., an analog level maintained in a neural delay loop. While Powers makes many references to other sorts of negative feedback loops in various organisms from cats to E. coli, the main thrust of his initial book deals with building up a model of what's going on, feedback-loopwise, in the nervous system and brain, not the body's endocrine systems.

To put it another way, PCT doesn't say that control systems are universal, only that they are ubiquitous, and that the bulk of organisms' neural systems are assembled from a relatively small number of distinct component types that closely resemble the sort of components that humans use when building machinery.

IOW, we should not expect that PCT's model of neural control systems would be directly applicable to a hormone level issue. However, we can reason from general principles and say that one difference between a PCT model of the leptin/ghrelin question is that PCT includes an explicit model of hierarchy and conflict in control networks, so that we can answer questions about what happens if both leptin and ghrelin are present (for example).

If those signals are at the same level of control hierarchy, we can expect conflict to result in oscillation, where the system alternates between trying to satisfy one or the other. Or, if they're at different levels of hierarchy, then we can expect one to override the other.

But, unlike a behavioral model where the question of precedence between different stimuli and contexts is open to interpretation, PCT makes some testable predictions about what actually constitutes hierarchy, both in terms of expected behavior, and in terms of the physical structure of the underlying control circuitry.

That is, if you could dissect an organism and find the neurons, PCT predicts a certain type of wiring to exist, i.e., that a dominant controller will have wiring to set the reference levels for lower-level controllers, but not vice-versa.

Second, PCT predicts that a dominant perception must be measured at a longer time scale than a dominated one. That is, the lower-level perception must have a higher sampling rate than the higher-level perception. Thus, for example, as a rat becomes hungrier (a longer-term perceptual variable), its likelihood of pressing a lever to receive food in spite of a shock is increased.

AFAICT, behaviorism can "explain" results like these, but does not actually predict them, in the sense that PCT is spelling out implementation-level details that behaviorism leaves to hand-waving. IOW, PCT is considerably more falsifiable than behaviorism, at least in principle. Eventually, PCT's remaining predictions (i.e., the ones that haven't already panned out at the anatomical level) will either be proven or disproven, while behaviorism doesn't really make anatomical predictions about these matters.

To answer question 3, one could perform the experiment of surgically balancing leptin and ghrelin and not feeding or otherwise nourishing the subject. If the subject eventually dies of starvation, I would say the second theory is more likely.

Outstanding comment - particularly the point at the end about the candle cooling the room.

It might be worthwhile to produce a sequence of postings on the control systems perspective - particularly if you could use better-looking block diagrams as illustrations. :)

My interpretation of this interaction (which is fascinating to read, btw, because both of you are eloquently defending a cogent and interesting theory as far as I can tell) is that you've indirectly proposed Robot-1 as the initial model of an agent (which is clearly not a full model of a person and fails to capture many features of humans) in the first of a series of articles. I think Richard is objecting to the connections he presumes that you will eventually draw between Robot-1 and actual humans, and you're getting confused because you're just trying to talk about the thing you actually said, not the eventual conclusions he expects you to draw from your example.

If he's expecting you to verbally zig when you're actually planning to zag and you don't notice that he's trying to head you off at a pass you're not even heading towards, its entirely reasonable for you to be confused by what he's saying. (And if some of the audience also thinks you're going to zig they'll also see the theory he's arguing against, and see that his arguments against "your predicted eventual conclusions" are valid, and upvote his criticism of something you haven't yet said. And both of you are quite thoughtful and polite and educated so its good reading even if there is some confusion mixed into the back and forth.)

The place I think you were ambiguous enough to be misinterpreted was roughly here:

Suppose the robot had human level intelligence in some side module, but no access to its own source code; that it could learn about itself only through observing its own actions. The robot might come to the same conclusions we did: that it is a blue-minimizer, set upon a holy quest to rid the world of the scourge of blue objects.

You use the phrase "human level intelligence" and talk about the robot making the same fuzzy inferential leap that outside human observer's might make. Also, this is remarkably close to how some humans with very poor impulse control actually seem to function, modulo some different reflexes and an moderately unreasonable belief in their own deliberative agency (a la Blindsight with the "Jubyr fcrpvrf vf ntabfvp ol qrsnhyg" line and so on).

If you had said up front that you're using this as a toy model which has (for example) too few layers and no feedback from the "meta-observer" module to be a honestly plausible model of "properly functioning cohesively agentive mammals" I think Richard would not have made the mistake that I think he's making about what you're about to say. He keeps talking about a robust and vastly more complex model than Robot-1 (that being a multi-layer purposive control system) and talking about how not just hypothetical PCT algorithms but actual humans function and you haven't directly answered these concerns by saying clearly "I am not talking about humans yet, I'm just building conceptual vocabulary by showing how something clearly simpler might function to illustrate mechanistic thinking about mental processes".

It might have helped if you were clear about the possibility that Robot-1 would emit words more like we might expect someone to emit several years after a serious brain lesion that severed some vital connections in their brain, after they're verbal reasoning systems had updated on the lack of a functional connection between their conscious/verbal brain parts and their deeper body control systems. Like Robot-1 seems likely to me to end up saying something like "Watch out, I'm not just having a mental breakdown but I've never had any control over my body+brainstems's actions in the first place! I have no volitional control over my behavior! If you're wearing blue then take off the shirt or run away before I happen to turn around and see you and my reflex kicks in and my body tries to kill you. Dear god this sucks! Oh how I wish my mental architecture wasn't so broken..."

For what its worth, I think the Robot-1 example is conceptually useful and I'm really looking forward to seeing how the whole sequence plays out :-)

What is the robot's goal? To follow the program detailed in the first paragraph?

I suspect Richard would say that the robot's goal is minimizing its perception of blue. That's the PCT perspective on the behavior of biological systems in such scenarios.

However, I'm not sure this description actually applies to the robot, since the program was specified as "scan and shoot", not "notice when there's too much blue and get rid of it.". In observed biological systems, goals are typically expressed as perception-based negative feedback loops implemented in hardware, rather than purely rote programs OR high-level software algorithms. But without more details of the robot's design, it's hard to say whether it really meets the PCT criterion for goals.

Of course, from a certain perspective, you could say at a high level that the robot's behavior is as if it had a goal of minimizing its perception of blue. But as your post points out, this idea is in the mind of the beholder, not in the robot. I would go further as to say that all such labeling of things as goals occurs in the minds of observers, regardless of how complex or simple the biological, mechanical, electronic, or other source of behavior is.

I suspect Richard would say that the robot's goal is minimizing its perception of blue. That's the PCT perspective on the behavior of biological systems in such scenarios.

This 'minimization' goal would require a brain that is powerful enough to believe that lasers destroy or discolor what they hit.

If this post were read by blue aliens that thrive on laser energy, they'd wonder they we were so confused as to the purpose of a automatic baby feeder.

This 'minimization' goal would require a brain that is powerful enough to believe that lasers destroy or discolor what they hit.

From the PCT perspective, the goal of an E. coli bacterium swimming away from toxins and towards food is to keep its perceptions within certain ranges; this doesn't require a brain of any sort at all.

What requires a brain is for an outside observer to ascribe goals to a system. For example, we ascribe a thermostat's goal to be to keep the temperature in a certain range. This does not require that the thermostat itself be aware of this goal.

< If this post were read by blue aliens that thrive on laser energy, they'd wonder they we were so confused as to the purpose of a automatic baby feeder.

Clever!

Although I find PCT intriguing, all the examples of it I've found have been about simple motor tasks. I can take a guess at how you might use the Method of Levels to explain larger-level decisions like which candidate to vote for, or whether to take more heroin, but it seems hokey, I haven't seen any reputable studies conducted at this level (except one, which claimed to have found against it) and the theory seems philosophically opposed to conducting them (they claim that "statistical tests are of no use in the study of living control systems", which raises a red flag large enough to cover a small city)

I've found behaviorism much more useful for modeling the things I want to model; I've read the PCT arguments against behaviorism and they seem ill-founded - for example, they note that animals sometimes auto-learn and behaviorist methodological insistence on external stimuli shouldn't allow that, but once we relax the methodological restrictions, this seems to be a case of surprise serving the same function as negative reinforcement, something which is so well understood that neuroscientists can even point to the exact neurons in charge of it.

Richard's PCT-based definition of goal is very different from mine, and although it's easily applicable to things like controlling eye movements, it doesn't have the same properties as the philosophical definition of "goal", the one that's applicable when you're reading all the SIAI work about AI goals and goal-directed behavior and such.

By my definition of goal, if the robot's goal were to minimize its perception of blue, it would shoot the laser exactly once - at its own visual apparatus - then remain immobile until turned off.

By my definition of goal, if the robot's goal were to minimize its perception of blue, it would shoot the laser exactly once - at its own visual apparatus - then remain immobile until turned off.

Ironically, quite a lot of human beings goals would be more easily met in such a way, and yet we still go around shooting our lasers at blue things, metaphorically speaking.

Or, more to the point, systems need not efficiently work towards their goals' fulfillment.

In any case, your comments just highlight yet again the fact that goals are in the eye of the beholder. The robot is what it is and does what it does, no matter what stories our brains make up to explain it.

(We could then go on to say that our brains have a goal of ascribing goals to things that appear to be operating of their own accord, but this is just doing more of the same thing.)

Richard's PCT-based definition of goal is very different from mine, and although it's easily applicable to things like controlling eye movements, it doesn't have the same properties as the philosophical definition of "goal", the one that's applicable when you're reading all the SIAI work about AI goals and goal-directed behavior and such.

Can you spell out the philosophical definition? My previous comment, which I posted before reading this, made only a vague guess at the concept you had in mind: "this sort of conscious, reflective, adaptive attempt to achieve what we 'really' want".

I think we agree, especially when you use the word "reflective". As opposed to, say, a reflex, which is an unconscious, nonreflective effort to acheive something which evolution or our designers decided to "want" for us. When the robot's reflection that shooting the hologram projector instead of the hologram fails to motivate it to do so, I start doubting its behaviors are goal-driven, and suspecting they're reflexive.

Every time you bring up PCT, I have to bring up my reasons for concluding that it's pseudoscience of the worst sort. (Note that this is an analysis of an experiment that PJ Eby himself picked to support his claims.)

Every time you bring up PCT,

Actually, Yvain brought it up.

I have to bring up my reasons for concluding that it's pseudoscience of the worst sort.

Which linking I don't mind a bit, since you're effectively linking to my reply as well, which is then followed by your hasty departure from the thread with a claim that you'd answer my other points "later"... with no further comment for just under two years. Guess it's not "later" yet.. ;-)

(Also, anyone who cares to read upthread from that link can see where I agreed with you about Marken's paper, or how much time it took me to get you to state your "true rejection" before you dropped out of the discussion. AFAICT, you were only having the discussion so you could find ammunition for a conclusion you'd reached long before that point.)

You also seem to have the mistaken notion that I'm an idea partisan, i.e., that because I say an idea has some merit or that it isn't completely worthless, that this means I'm an official spokesperson for that idea as well, and therefore am an Evil Outsider to be attacked.

Well, I'm not, and you're being rude. Not only to me, but to everyone in the thread who's now had to listen to both your petty hit-and-run pa(troll)ing, and to me replying.

So, I'm out of here (the subthread), but I won't be coming back later to address any missed points, since the burden is still on you to actually address any of the many, MANY questions I asked you in that two-year-old thread, for which you still have yet to offer any reply, AFAICT.

I entered that discussion with a willingness to change my mind, but from the evidence at hand, it seems you did not.

(Note: if you do wish to have an intelligent discussion on the topic, you may reach me via the old thread. I'm pre-committing not to reply to you in this one, where you can indulge your obvious desire to score points off an audience, vs. actually discussing anything.)

(Note: if you do wish to have an intelligent discussion on the topic, you may reach me via the old thread. I'm pre-committing not to reply to you in this one, where you can indulge your obvious desire to score points off an audience, vs. actually discussing anything.)

Thanks for the poisoned well, but I don't intend to abuse the last word. I think more highly of you now than I did when we had our prior altercation, but it remains true that I've seen zero experimental evidence for PCT in a cognitive context, and that Marken's paper is an absolute mathematical sham. There may be valid aspects to PCT, but it hasn't yet justified its use as a cognitive theory, and I feel that it's important to note this whenever it comes up on Less Wrong.

(Incidentally, the reason I trailed off in that thread is because I'd done something that in retrospect was poor form: I'd written up a full critique of the Marken paper before I asked you whether you thought it constituted experimental evidence, and I was frustrated that you didn't walk into the trap. If we both agree that the paper is pseudoscience, though, there's nothing left to add.)

P.S. I don't doubt that you've had success working with people through a PCT framework, but I suspect that it's a placebo effect: a sufficiently fuzzy framework gives you room to justify your (usually correct) unconscious intuitions about what's going on, and grants it the gravitas of a deep-sounding theory. (You might do just as well if you were a Freudian.) That's one reason why I discount anecdotal evidence of that form.

This isn't just a reduction of a goal to a program: predicting the robot's goal-based behavior and its program-based behavior give different results.

If goals reduce to a program like the robot's in any way, it's in the way that Einsteinian mechanics "reduce" to Newtonian mechanics - giving good results in most cases but being fundamentally different and making different predictions on border cases. Because there are other programs that goals do reduce to, like the previously mentioned Robot-1, I don't think it's appropriate to call what the blue-minimizer is doing a "goal".

If you still disagree, can you say exactly what goal you think the robot is pursuing, so I can examine your argument in more detail?

I recall that a big problem we had before was trying to unpack what different people meant by the words "goal", "model", etc. But your description of at least this distinction you're drawing between the things which you're calling "goals" and the things which you're calling "programs" is very good, IMO!

This robot is not a consequentialist - it doesn't have a model of the world which allows it to extrapolate (models of) outcomes that follow causally from its choices. It doesn't seem to steer the universe any particular place, across changes of context, because it explicitly doesn't contain a future-steering engine.

What exactly is meant by the robot having a human-level intelligence? Does it have two non-interacting programs: shoot blue and think?

This seems to be the key point. Everything interesting about the whole project of human rationality is contained in the interaction between the parts of us that think and the parts of us that do. All of the theories Yvain is criticising are about, ultimately, explaining and modeling the relationship between these two entities

Ah, excellent. This post comes at a great time. A few weeks ago, I talked with someone who remarked that although decision theory speaks in terms of preferences and information being separate, trying to apply that into humans is fitting the data to the theory. He was of the opinion that humans don't really have preferences in the decision theoretic sense of the word. Pondering that claim, I came to the conclusion that he's right, and have started to increasingly suspect that CEV-like plans to figure out the "ultimate" preferences of people are somewhat misguided. Our preferences are probably hopelessly path-, situation- and information-dependent. Which is not to say that CEV would be entirely pointless - even if the vast majority of our "preferences" would never converge, there might be some that did. And of course, CEV would still be worth trying, just to make sure I'm not horribly mistaken on this.

The ease at which I accepted the claim "humans don't have preferences" makes me suspect that I've myself had a subconscious intuition to that effect for a long time, which was probably partially responsible for an unresolved disagreement between me and Vladimir Nesov earlier.

I'll be curious to hear what you have to say.

...CEV-like plans to figure out the "ultimate" preferences of people are somewhat misguided. Our preferences are probably hopelessly path-, situation- and information-dependent.

This is off-topic but since you mentioned it and since I don't think it warrants a new post, here are my latest thoughts on CEV (a convergence of some of my recent comments originally posted as a response to a post by Michael Anissimov):

Consider the difference between a hunter-gatherer, who cares about his hunting success and to become the new clan chief, and a member of lesswrong who wants to determine if a “sufficiently large randomized Conway board could turn out to converge to a barren ‘all off’ state.”

The utility of the success in hunting down animals and proving abstract conjectures about cellular automata is largely determined by factors such as your education, culture and environmental circumstances. The same hunter gatherer who cared to kill a lot of animals, to get the best ladies in its clan, might have under different circumstances turned out to be a vegetarian mathematician solely caring about his understanding of the nature of reality. Both sets of values are to some extent mutually exclusive or at least disjoint. Yet both sets of values are what the person wants, given the circumstances. Change the circumstances dramatically and you change the persons values.

You might conclude that what the hunter-gatherer really wants is to solve abstract mathematical problems, he just doesn’t know it. But there is no set of values that a person “really” wants. Humans are largely defined by the circumstances they reside in. If you already knew a movie, you wouldn’t watch it. To be able to get your meat from the supermarket changes the value of hunting.

If “we knew more, thought faster, were more the people we wished we were, and had grown up closer together” then we would stop to desire what we learnt, wish to think even faster, become even different people and get bored of and rise up from the people similar to us.

A singleton will inevitably change everything by causing a feedback loop between the singleton and human values. The singleton won’t extrapolate human volition but implement an artificial set values as a result of abstract high-order contemplations about rational conduct. Much of our values and goals, what we want, are culturally induced or the result of our ignorance. Reduce our ignorance and you change our values. One trivial example is our intellectual curiosity. If we don’t need to figure out what we want on our own, our curiosity is impaired.

Knowledge changes and introduces terminal goals. The toolkit that is called ‘rationality’, the rules and heuristics developed to help us to achieve our terminal goals are also altering and deleting them. A stone age hunter-gatherer seems to possess very different values than I do. If he learns about rationality and metaethics his values will be altered considerably. Rationality was meant to help him achieve his goals, e.g. become a better hunter. Rationality was designed to tell him what he ought to do (instrumental goals) to achieve what he wants to do (terminal goals). Yet what actually happens is that he is told, that he will learn what he ought to want. If an agent becomes more knowledgeable and smarter then this does not leave its goal-reward-system intact if it is not especially designed to be stable. An agent who originally wanted to become a better hunter and feed his tribe would end up wanting to eliminate poverty in Obscureistan. The question is, how much of this new “wanting” is the result of using rationality to achieve terminal goals and how much is a side-effect of using rationality, how much is left of the original values versus the values induced by a feedback loop between the toolkit and its user?

Take for example an agent is facing the Prisoner’s dilemma. Such an agent might originally tend to cooperate and only after learning about game theory decide to defect and gain a greater payoff. Was it rational for the agent to learn about game theory, in the sense that it helped the agent to achieve its goal or in the sense that it deleted one of its goals in exchange for a more “valuable” goal?

It seems to me that becoming more knowledgeable and smarter is gradually altering our utility functions. But what is it that we are approaching if the extrapolation of our volition becomes a purpose in and of itself? A living treaty will distort or alter what we really value by installing a new cognitive toolkit designed to achieve an equilibrium between us and other agents with the same toolkit.

Would a singleton be a tool that we can use to get what we want or would the tool use us to do what it does, would we be modeled or would it create models, would we be extrapolating our volition or rather follow our extrapolations?

Is becoming the best hunter really one of the primitive man's terminal values? I would say his terminal values are more things like "achieving a feeling of happiness, contentment, and pride in one's self and one's relatives". The other things you mention are just effective instrumental goals.

I mostly agree with this.

I think that the idea of desires converging if “we knew more, thought faster, were more the people we wished we were, and had grown up closer together” relies on assumptions of relatively little self-modification. Once we get uploads and the capability for drastic self-modification, all kinds of people and subcultures will want to use it. Given the chance and enough time, we might out-speciate the beetle (to borrow Anders Sandberg's phrase), filling pretty much every corner of posthuman mindspace. There'll be minds so strange that we won't even recognize them as humans, and we'll hardly have convergent preferences with them.

Of course, that's assuming that no AI or mind with a first-mover advantage simply takes over and outcompetes everyone else. Evolutionary pressures might prune the initial diversity a lot, too - if you're so alien that you can't even communicate with ordinary humans, you may have difficulties paying the rent for your server farm.

At the end of this, I'm going to try to argue that something like CEV is still justified. Before I started thinking it through I was hoping that taking an eliminativist view of preferences to its conclusion would help tie up the loopholes in CEV, and so far it hasn't done that for me, but it hasn't made it any harder either.

CEV has worse problems that worries about convergence. The big one is that it's such a difficult thing to implement that any AI capable of doing so has already crossed the threshold of extremely dangerous transhuman capability, and there's no real solution to how to regulate its behavior while it's in the process of working on the extrapolation. It could very well turn the planet into computronium before it gets a satisfactory implementation, by which point it doesn't much matter what result it arrives at.

Presumably it matters if it then turns the planet back?