since by that argument dogs and cats are optimization, and blue and green are optimization, and everything is optimization

I have no idea what you are talking about. Optimization isn't that vague of a word, and I tried to give examples of what I meant by it. The ability to solve problems and design technologies. Dogs and cats can't design technology. Blue and green can't design technology. Call it what you want, but to me that's what intelligence is.

And that's all that really matters about intelligence, is it's ability to do that. If you gave me a computer program that could solve arbitrary optimization problems, who cares if it can't speak language? Who cares if it isn't an agent? It would be enormously powerful and useful.

That is also why when AI is actually programmed, people will do it by trying to get something to understand language, and that will in fact result in the kind of AI that I was talking about, namely one that aims at vague goals that can change from day to day, not at paperclips.

Again this claim doesn't follow from your premise at all. AIs will be programmed to understand language... therefore they won't have goals? What?

Humans definitely have goals. We have messy goals. Nothing explicit like maximizing paperclips, but a hodge podge of goals that evolution selected for, like finding food, getting sex, getting social status, taking care of children, etc. Humans are also more reinforcement learners than pure goal maximizers, but it's the same principle.

Okay. By saying "If they have failed to grasp that morality is obligatory, have they understood it at all? They might continue caring more about eggnog, of course. That is beside the point... morality means what you should care about, not what you happen to do."

it seems you have not understood the idea. Were there any parts of the the post that seemed unclear that you think I might make clearer?

Because the whole point is that to say something is moral = you should do it = it is valued according to the morality equation.

For an Elf to agree something is moral is also to agree that they should do it. When I say they agree it's moral and don't care, that also means they agree they should do it and don't care.

Something being Christmas Spiritey = you Spiritould do it. Humans might agree that something is Christmas Spirit-ey, and agree that they spiritould do it, they just don't care about what they spiritould do, they only care about what they should do.

moral is to Christmas spiritey what "should" is to (make up a word like) "spiritould"

Obligatory is just a kind of "should." Elves agree that some things are obligatory, and don't care, they care about what's ochristmastory.

.

Likewise, to say that today's morality equation is the "best" is to say that today's morality equation is the equation which is most like today's morality equation. Tautology.

Best = most good, and good = valued by the morality equation.

AIXI-tl is very inefficient. Practical AIs will not be like that, and they will not be limited to one rigid goal like that.

Your second claim doesn't follow from the first. Practical AIs will of course be different. But the basic structure of AIXI, reinforcement learning, is agnostic to the model used. It just requires some algorithm to do learning/prediction. As prediction algorithms get better and better, they will still suffer the same problems as AIXI. Unless you are proposing some totally different model of AI than reinforcement learning, that somehow doesn't suffer from these problems.

And even if you find my theory of intelligence unconvincing, one that implies that evolution is intelligent is even less convincing, since it does not respect what people actually mean by the word.

Now we are debating definitions, which is not productive.

Evolution is not typically thought of as intelligent because it's not an agent. It doesn't exist in an environment, make observations, and adjust it's model of the world, etc. I accept that evolution is not an agent. But that doesn't matter.

There is only one problem that we really care about. Optimization. This is the only thing that really matters. The advantage humans have in this world is our ability to solve problems and develop technology. The risk and benefit of superintelligent AI comes entirely from its potential to solve problems and engineer technologies better than humans.

And that's exactly what evolution does. It's an algorithm that can solve problems and design very sophisticated and efficient machines. It does the thing we care about, despite not being an agent. Whether it meets the definition of "intelligence" or not, is really irrelevant. All that matters is if it's an algorithm that can solve the types of problems we care about. There is no reason that solving problems or designing machines should require an algorithm to be an agent.

Smells like a homunculus. What guides your reasoning about your goals?

It's possible to construct a paperclipper in theory. AIXI-tl is basically a paperclipper. It's goal is not paperclips but maximizing a reward signal, which can come from anything (perhaps a paperclip recognizer...) AIXI-tl is very inefficient, but it's a proof of concept that paperclipers are possible to construct. AIXI-tl is fully capable of speaking, solving problems, anything that it predicts will lead to more reward.

A real AI would be much more efficient approximation of AIXI. Perhaps something like modern neural nets, that can predict what actions will lead to reward. Probably something more complicated. But it's definitely possible to construct paperclippers that only care about maximizing some arbitrary reward. The idea that just having the goal of getting paperclips would somehow make it incapable of doing anything else, is just absurd.

As for your hypothesis of what intelligence is, I find it incredibly unconvincing. It's true I don't necessarily have a better hypothesis. Because no one does. No one knows how the brain works. But just asserting a vague hypothesis like doesn't help anyone unless it actually explains something or helps us build better models of intelligence. I don't think it explains anything. Its definitely not specific enough to build an actual model out of.

But really it's irrelevant to this discussion. Even if you are correct, it doesn't say anything about AI progress. In fact if you are right, it could mean AI is even sooner. Because if it's correct, it means AI researchers just need to figure out that one idea, to suddenly make intelligent AIs. If we are only one breakthrough like that away from AGI, we are very close indeed.

I agree that one problem with the wand is that it is not general. The same thing is true of paperclippers. Just as the wand is limited to converting things to gold, the paperclipper is limited to making paperclips.

The paperclipper can be programmed to value any goal other than paperclips. Paperclips is just it's current goal. The gold wand can not do anything else.

But even if it's desire for paperclips is immutable and hard wired, it's still clearly intelligent. It can solve problems, speak language, design machines, etc, so long as it serves it's goal of making paperclips.

Humans certainly do recognize patterns in patterns. For example, we recognize that some things are red. That means recognizing a pattern: this red thing is similar to that red thing. Likewise, we recognize that some things are orange.

Artificial neural networks can do the same thing. This is a trivial property of NNs, similar objects produce similar internal representations. Internal representations tend to be semantically meaningful, lookup word vectors.

And within those patterns we recognize other similarities, and so people talk about "warm" and "cool" colors, noticing that blue and green are similar to each other in some way, and that orange and red are similar to each other in another way.

That's not a "pattern within a pattern". That's just a typical pattern, that green and blue appear near "cool" things and that orange and red appear near "hot" things.

Likewise we have the concept of "color", which is noting that all of these patterns are part of a more general pattern.

That's just language. The word "color" happens to be useful to communicate with people. I agree that language learning is important for AI. And this is a field that is making rapid progress.

In that case, the magic wand has a high degree of optimization power. It is extremely good at converting things it touches into gold, in all possible environments. But it is perfectly plain that the wand is not intelligent. So that definition of intelligence is mistaken.

The wand isn't generally intelligent. Maybe by some stretch of the definition we could sorta say it's "intelligent" at the task of turning things to gold. But it can't do any tasks other than turning things into gold. The whole point of AGI is general intelligence. That's what the G stands for.

Humans are generally intelligent. We can apply our brains to widely different tasks, including many that we weren't evolved to be good at at all. From playing Go to designing rockets. Evolution is generally intelligent. It can find remarkably good designs for totally arbitrary objective functions.

I think general optimization ability is a perfectly fine definition of intelligence. It includes things like humans and evolution, and some kinds of simple but general AI, but excludes things like animals and domain specific AI. It defines intelligence only by results. If you can optimize an arbitrary goal you are intelligent. It doesn't try to specify what the internal mechanisms should be, just whether or not they work. And it's continuous - you can have a degree of very stupid optimizer like evolution, all the way to very good/intelligent ones like humans.

Intelligence is the ability to engage in abstract thought. You could characterize that as pattern recognition, except that it is the ability to recognize patterns in patterns in patterns, recursively.

This definition is really vague. You are just shoving the hard problem of defining intelligence into the hard problem of defining "abstract thought". I guess the second sentence kind of clarifies that you mean. But it's not clear at all that humans even meet that definition. Do humans recognize patterns in patterns? I don't think so. I don't think we are consciously aware of the vast majority of our pattern recognition ability.

The most intelligent AI we have, is not remotely close to that. It can only recognize very particular patterns in very particular sorts of data.

Not really. Deep neural networks are extraordinary general. The same networks that win at Go could be applied to language translation, driving cars, playing pacman, or recognizing objects in an image.

One example of that was the case of AlphaGo, where Eliezer called it "superintelligent with bugs,"

The exact quote is "superhuman with bugs". In the context, he was describing the fact that the AI plays far above human level. But still makes some mistakes a human might not make. And it's not even clear when it makes mistakes, because it is so far above human players and may see things we don't see, that makes those moves not mistakes.

The orthogonality thesis is a similar mistake of that kind; something that is limited to seeking a limited goal like "maximize paperclips" cannot possibly be intelligent, because it cannot recognize the abstract concept of a goal.

A paperclip maximizer can recognize the concept of a goal. It's not stupid, it just only cares about paperclips. In the same way humans are programmed by evolution to maximize sex, social status, and similarly arbitrary goals, there is no reason an AI couldn't be programmed to maximize paperclips. Again, perhaps humans are not intelligent by your definition.

Unless you believe there is some magical point where there is a sudden change from stupid to intelligent, we are still extremely far off from intelligent machines.

Yeah that seems quite obviously true. Just look at the chimpanzees. By some accounts the main difference in human brains is they are just scaled up primate brains - 3 times as large, with a bit more sophisticated language ability. And suddenly you go from creatures that can barely master simple tools and can't communicate ideas, to creatures capable of technological civilization. 500 million years of evolution refined the mammal brain to get chimps, but only about a million was needed to go from stupid animals to generally intelligent humans.

I don't see any reason to believe AI progress should be linear. In practice it is clearly not. Areas of AI often has sudden discontinuities or increasing rates of progress. I don't see any reason why there can't be a single breakthrough that causes enormous progress, or why even incremental progress must be slow. If evolution can make brains by a bunch of stupid random mutations, surely thousands of intelligent engineers can do so much better on a much shorter time scale.

as is evident in the behavior of children, which passes imperceptibly from stupid to intelligent.

This isn't a valid analogy at all. Baby humans still have human brains running the same algorithms as adult humans. Their brains are just slightly smaller and have had less time to learn and train. Individual AIs may increase in ability linearly as they grow and learn. But the AI algorithms themselves have no such constraint, someone could theoretically figure out the perfect AI algorithm tomorrow and code it up. There is certainly no law of nature that says AI progress must be slow.

The fact that something is possible in principle doesn't mean there's any easy way to do it in practice. The easy way to produce arbitrarily complex intelligent behavior in practice is to produce something that can abstract to an arbitrary degree of generality, and that means recognizing abstractions like "goal", "good," and so on.

I actually had specific examples in mind, basically all GOFAI approaches to general AI. But in any case this logic doesn't seem to hold up. You could argue that something needs to HAVE goals in order to be intelligent -- I don't think so, at least not with the technical definition typically given to 'goals', but I will grant it for the purpose of discussion. It still doesn't follow that the thing has to be aware of these goals, or introspective of them. One can have goals without being aware that one has them, or able to represent those goals explicitly. Most human beings fall in this category most of the time, it is sad to say.

Your example of a magic wand doesn't sound correct to me. By what basis is a Midas touch "optimizing"? It is powerful, yes, but why "optimizing"? A supernova that vaporizes entire planets is powerful, but not optimizing. Seems like a strawman.

Defining intelligence as pattern recognizing is not new. Ben Goertzel has espoused this view for some twenty years, and written a book on the subject I believe. I'm not sure I buy the strong connection with "recognizing the abstract concept of a goal" and such, however. There are plenty of conceivable architectures for which this meta level thinking is incapable of happening, yet nevertheless are capable of producing arbitrarily complex intelligent behavior.

Regarding your last point, your terminology is unnecessarily obscuring. There doesn't have to be a "magic point" -- it could be simply a matter of correct software, but insufficient data or processing power. A human baby is a very stupid device, incapable of doing anything intelligent. But with experiential data and processing time it becomes a very powerful general intelligence over the course of 25 years, without any designer intervention. You bring up this very point yourself which seems to counteract your claim.

Your example of a magic wand doesn't sound correct to me. By what basis is a Midas touch "optimizing"? It is powerful, yes, but why "optimizing"? A supernova that vaporizes entire planets is powerful, but not optimizing. Seems like a strawman.

Defining intelligence as pattern recognizing is not new. Ben Goertzel has espoused this view for some twenty years, and written a book on the subject I believe. I'm not sure I buy the strong connection with "recognizing the abstract concept of a goal" and such, however. There are plenty of conceivable architectures for which this meta level thinking is incapable of happening, yet nevertheless are capable of producing arbitrarily complex intelligent behavior.

Regarding your last point, your terminology is unnecessarily obscuring. There doesn't have to be a "magic point" -- it could be simply a matter of correct software, but insufficient data or processing power. A human baby is a very stupid device, incapable of doing anything intelligent. But with experiential data and processing time it becomes a very powerful general intelligence over the course of 25 years, without any designer intervention. You bring up this very point yourself which seems to counteract your claim.