I just came here to point out that even nuclear weapons were a slow takeoff in terms of their impact on geopolitics and specific wars. American nuclear attacks on Hiroshima and Nagasaki were useful but not necessarily decisive in ending the war on Japan; some historians argue that the Russian invasion of Japanese-occupied Manchuria, the firebombing of Japanese cities with massive conventional bombers, and the ongoing starvation of the Japanese population due to an increasingly successful blockade were at least as influential in the Japanese decision to surrender.

After 1945, the American public had no stomach for nuclear attacks on enough 'enemy' civilians to actually cause large countries like the USSR or China to surrender, and nuclear weapons were too expensive and too rare to use them to wipe out large enemy armies -- the 300 nukes America had stockpiled at the start of the Korean War in 1950 would not necessarily have been enough to kill the 3 million dispersed Chinese soldiers who actually fought in Korea, let alone the millions more who would likely have volunteered to retaliate against a nuclear attack.

The Soviet Union had a similarly-sized nuclear stockpile and no way to deliver it to the United States or even to the territory of key US allies; the only practical delivery vehicle at that time was via heavy bomber, and the Soviet Union had no heavy bomber force that could realistically penetrate western air defense systems -- hence the Nike anti-aircraft missiles rusting along ridgelines near the California coast and the early warning stations dotting the Canadian wilderness. If you can shoot their bombers down before they can reach your cities, then they can't actually win a nuclear war against you.

Nukes didn't become a complete gamechanger until the late 1950s, when the increased yields from hydrogen bombs and the increased range from ICBMs created a truly credible threat of annihilation.

I suspect we're talking about two different things. 

If you just naively program a super-intelligent AI to satisfice a goal, then, sure, most of the candidate pathways to satisfice will involve accruing a lot of some type of power, because power is useful for achieving goals. That's a valid point, and it's important to understand that merely switching from optimizers to satisficers won't adequately protect us against overly ambitious AIs.

However, that doesn't mean that it's futile to explicitly penalize most (but not literally all) of the paths that the AI could take to accumulate too much power. Suppose you adequately penalize all of the tactics that would have catastrophic side effects except for, oh, I don't know, cornering the world market on all types of grain, because you forgot to think of that one particular path to power. Would most of the candidate plans that the AI submits for human approval then turn out to involve secretly cornering the grain market?

I don't see why they would. All else being equal, sure, the most powerful tactic available is going to be somewhat more attractive than other, less powerful tactics. But how much more attractive? Would an AI generate 1,000x more plans that involve one specific globally disruptive tactic like cornering the grain market vs. all of the various tactics that are only locally disruptive, like publishing propaganda or hiring away competing researchers or hacking into competing research labs or interrupting the flow of electricity to those labs? 10x more such plans? 2x more such plans? I don't think that's the kind of estimate you can make just by gesturing at basic principles of game theory; you'd need some concrete domain knowledge about the AI's specific planning algorithms.

If the truly dangerous plans are only 10x more common on the initial brainstorming list, then we can probably make sure those plans aren't chosen by filtering for plans that the AI rates as safe and transparent. We can use 3 different AIs on airgapped equipment -- one that only "cares" about evaluating whether a plan is safe (i.e., would the plan cause unacceptably large changes if implemented?), one that only "cares" about evaluating whether a plan is transparent (i.e., can the plan be explained to humans so that the humans will have an accurate understanding of what actions will be taken as part of the plan?), and one that generates plans that satisfice for a substantive goal (in this case, shutting down opposing AI research).

Of the plans that are approved by all 3 AIs, most of them probably won't involve accumulating excessive amounts of power, because usually if you accumulate too much power, then that is (a) unsafe, and (b) likely to involve lying to 'your' human programmers.

If powerful plans are 10x more common on the initial brainstorming list, then you need filters for safety and/or transparency that are at least 90% effective at catching unacceptably powerful plans in order to get back up to a 50% chance of humanity surviving. If powerful plans are more than 10x more common, then you need a set of filters that's more reliable than 90%. I don't think we have any idea what those numbers are yet, but I do think it's worth trying to reduce how common it is for excessively powerful plans to show up on the initial brainstorming list, and I think we can do that by training AIs to recognize dangerously disruptive plans and to try to avoid those types of plans. It's better to at least try to get AIs to engage with the concept of "this plan is too disruptive" then to throw up our hands and say, "Oh, power is an attractor in game theory space, so there's no possible way to get brilliant AIs that don't seize infinite power."

Sure, the metaphor is strained because natural selection doesn't have feelings, so it's never going to feel satisfied, because it's never going to feel anything. For whatever it's worth, I didn't pick that metaphor; Eliezer mentions contraception in his original post.

As I understand it, the point of bringing up contraception is to show that when you move from one level of intelligence to another, much higher level of intelligence, then the more intelligent agent can wind up optimizing for values that would be anathema to the less intelligent agents, even if the less intelligent agents have done everything they can to pass along their values. My objection to this illustration is that I don't think anyone's demonstrated that human goals could plausibly be described as "anathema" to natural selection. Overall, humans are pursuing a set of goals that are relatively well-aligned with natural selection's pseudo-goals.

Right, I'm not claiming that AGI will do anything like straightforwardly maximize human utility. I'm claiming that if we work hard enough at teaching it to avoid disaster, it has a significant chance of avoiding disaster.

The fact that nobody is artificially mass-producing their genes is not a disaster from Darwin's point of view; Darwin is vaguely satisfied that instead of a million humans there are now 7 billion humans. If the population stabilizes at 11 billion, that is also not a Darwinian disaster. If the population spreads across the galaxy, mostly in the form of emulations and AIs, but with even 0.001% of sentient beings maintaining some human DNA as a pet or a bit of nostalgia, that's still way more copies of our DNA than the Neanderthals were ever going to get.

There are probably some really convincing analogies or intuition pumps somewhere that show that values are likely to be obliterated after a jump in intelligence, but I really don't think evolution/contraception is one of those analogies.

I think we're doing a little better than I predicted. Rationalists seem to be somewhat better able than their peers to sift through controversial public health advice, to switch careers (or retire early) when that makes sense, to donate strategically, and to set up physical environments that meet their needs (homes, offices, etc.) even when those environments are a bit unusual. Enough rationalists got into cryptocurrency early enough and heavy enough for that to feel more like successful foresight than a lucky bet. We're doing something at least partly right.

That said, if we really did have a craft of reliably identifying and executing better decisions, and if even a hundred people had been practicing that craft for a decade, I would expect to see a lot more obvious results than the ones I actually see. I don't see a strong correlation between the people who spend the most time and energy engaging with the ideas you see on Less Wrong, and the people who are wealthy, or who are professionally successful, or who have happy families, or who are making great art, or who are doing great things for society (with the possible exception of AI safety, and it's very difficult to measure whether working on AI safety is actually doing any real good).

If anything, I think the correlation might point the other way -- people who are distressed or unsuccessful at life's ordinary occupations are more likely to immerse themselves in rationalist ideas as an alternate source of meaning and status. There is something actually worth learning here, and there are actually good people here; it's not like I would want to warn anybody away. If you're interested in rationality, I think you should learn about it and talk about it and try to practice it. However, I also think some of us are still exaggerating the likely benefits of doing so. Less Wrong isn't objectively the best community; it's just one of many good communities, and it might be well-suited to your needs and quirks in particular.

I appreciate how much detail you've used to lay out why you think a lack of human agency is a problem -- compared to our earlier conversations, I now have a better sense of what concrete problem you're trying to solve and why that problem might be important. I can imagine that, e.g., it's quite difficult to tell how well you've fit a curve if the context in which you're supposed to fit that curve is vulnerable to being changed in ways whose goodness or badness is difficult to specify. I look forward to reading the later posts in this sequence so that I can get a sense of exactly what technical problems are arising and how serious they are.

That said, until I see a specific technical problem that seems really threatening, I'm sticking by my opinion that it's OK that human preferences vary with human environments, so long as (a) we have a coherent set of preferences for each individual environment, and (b) we have a coherent set of preferences about which environments we would like to be in. Right, like, in the ancestral environment I prefer to eat apples, in the modern environment I prefer to eat Doritos, and in the transhuman environment I prefer to eat simulated wafers that trigger artificial bliss. That's fine; just make sure to check what environment I'm in before feeding me, and then select the correct food based on my environment. What do you do if you have control over my environment? No big deal, just put me in my preferred environment, which is the transhuman environment. 

What happens if my preferred environment depends on the environment I'm currently inhabiting, e.g., modern me wants to migrate to the transhumanist environment, but ancestral me thinks you're scary and just wants you to go away and leave me alone? Well, that's an inconsistency in my preferences -- but it's no more or less problematic than any other inconsistency. If I prefer oranges when I'm holding an apple, but I prefer apples when I'm holding an orange, that's just as annoying as the environment problem. We do need a technique for resolving problems of utility that are sensitive to initial conditions when those initial conditions appear arbitrary, but we need that technique anyway -- it's not some special feature of humans that makes that technique necessary; any beings with any type of varying preferences would need that technique in order to have their utility fully optimized. 

It's certainly worth noting that standard solutions to Goodhart's law won't work without modification, because human preferences vary with their environments -- but at the moment such modifications seem extremely feasible to me. I don't understand why your objections are meant to be fatal to the utility of the overall framework of Goodhart's Law, and I hope you'll explain that in the next post.

Hmm. Nobody's ever asked me to try to teach them that before, but here's my advice:

1. Think about what dimensions or components success at the task will include. E.g., if you're trying to play a song on the guitar, you might decide that a well-played song will have the correct chords played with the correct fingering and the correct rhythm.
2. Think about what steps are involved in each of the components of success, with an eye toward ordering those steps in terms of which steps are easiest to learn and which steps are logical prerequisites for the others. E.g., in order to learn how to play a rhythm, you first need an understanding of rhythmic concepts like beats and meters. Then, once you have a language that you can use to describe a rhythm, you need some concrete examples of rhythms, e.g., a half note followed by two quarter-notes. Then you need to translate that into the physical motions taken on the guitar, e.g., downstrokes and upstrokes with greater or lesser emphasis. Those are two different steps; first you teach the difference between a downstroke and an upstroke, and then you teach the difference between a stressed beat and an unstressed beat. You might change the order of those steps if you are working with a student who's more comfortable with physical techniques than with language, e.g., demonstrate some rhythms first, and then only after that explain what they mean in words. In general, most values will have a vocabulary that lets you describe them, a series of examples that help you understand them, and a set of elements that constitute them; using each new word in the vocabulary and recognizing each type of example and recognizing each element and using each element is a separate step in learning the technique.
3. Leave some room at the end for integration, e.g., if you've learned rhythm and fingering and chords, you still need some time to practice using all three of those correctly at once. This may include learning how to make trade-offs among the various components, e.g., if you've got some very tricky fingering in one measure, maybe you simplify the chord to make that easier.