Alignment as Aimability or as Goalcraft?

The Less Wrong and AI risk communities have obviously had a huge role in mainstreaming the concept of risks from artificial intelligence, but we have a serious terminology problem.

The term "AI Alignment" has become popular, but people cannot agree whether it means something like making "Good" AI or whether it means something like making "Aimable" AI. We can define the terms as follows:

AI Aimability = Create AI systems that will do what the creator/developer/owner/user intends them to do, whether or not that thing is good or bad

AI Goalcraft = Create goals for AI systems that we ultimately think lead to the best outcomes

Aimability is a relatively well-defined technical problem and in practice almost all of the technical work on AI Alignment is actually work on AI Aimability. Less Wrong has for a long time been concerned with Aimability failures (what Yudkowsky in the early days would have called "Technical Failures of Friendly AI") rather than failures of Goalcraft (old-school MIRI terminology would be "Friendliness Content").

The problem is that as the term "AI Alignment" has gained popularity, people have started to completely merge the definitions of Aimability and Goalcraft under the term "Alignment". I recently ran some Twitter polls on this subject, and it seems that people are relatively evenly split between the two definitions.

This is a relatively bad state of affairs. We should not have the fate of the universe partially determined by how people interpret an ambiguous word.

In particular, the way we are using the term AI Alignment right now means that it's hard to solve the AI Goalcraft problem and easy to solve the Aimability problem, because there is a part of AI that is distinct from Aimability which the current terminology doesn't have a word for.

Not having a word for what goals to give the most powerful AI system in the universe is certainly a problem, and it means that everyone will be attracted to the easier Aimability research where one can quickly get stuck in and show a concrete improvement on a metric and publish a paper.

Why doesn't the Less Wrong / AI risk community have good terminology for the right hand side of the diagram? Well, this (I think) goes back to a decision by Eliezer from the SL4 mailing list days that one should not discuss what the world would be like after the singularity, because a lot of time would be wasted arguing about politics, instead of the then more urgent problem of solving the AI Aimability problem (which was then called the control problem). At the time this decision was probably correct, but times have changed. There are now quite a few people working on Aimability, and far more are surely to come, and it also seems quite likely (though not certain) that Eliezer was wrong about how hard Aimability/Control actually is.

Words Have Consequences

This decision to not talk about AI goals or content might eventually result in some unscrupulous actors getting to define the actual content and goals of superintelligence, cutting the X-risk and LW community out of the only part of the AI saga that actually matters in the end. For example, the recent popularity of the e/acc movement has been associated with the Landian strain of AI goal content - acceleration towards a deliberate and final extermination of humanity, in order to appease the Thermodynamic God. And the field that calls itself AI Ethics has been tainted with extremist far-left ideology around DIE (Diversity, Inclusion and Equity) that is perhaps even more frightening than the Landian Accelerationist strain. By not having mainstream terminology for AI goals and content, we may cede the future of the universe to extremists.

I suggest the term "AI Goalcraft" for the study of which goals for AI systems we ultimately think lead to the best outcomes. The seminal work on AI Goalcraft is clearly Eliezer's Coherent Extrapolated Volition, and I think we need to push that agenda further now that AI risk has been mainstreamed and there's a lot of money going into the Aimability/Control problem.

Gud Car Studies

What should we do with the term "Alignment" though? I'm not sure. I think it unfortunately leads people into confusion. It doesn't track the underlying reality - which I believe is that action naturally factors into Goalcraft followed by Aimability, and you can work on Aimability without knowing much about Goalcraft and vice-versa because the mechanisms of Aimability don't care much about what goal one is aiming at, and the structure of Goalcraft doesn't care much about how you're going to aim at the goal and stay on target. When people hear "Aligned" they just hear "Good", but with a side order of sophistication. It would be like if we lumped mechanical engineers who developed car engines in with computer scientists working on GPS navigators and called their field Gud Car Studies. Gud Car Studies is obviously an abomination of a term that doesn't properly reflect the underlying reality that designing a good engine is mostly independent of deciding where to drive the car to, and how to navigate there. I think that "Alignment" has unfortunately become the "Gud Car Studies" of our time.

I'm at a loss as to what to do - I suspect that the term AI Alignment has already gotten away from us and we should stop using it and talk about Aimability and Goalcraft instead.

This post is Crossposted at the EA Forum

Related: "Aligned" shouldn't be a synonym for "good"

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From briefly talking to Eliezer about this the other day, I think the story from MIRI's perspective is more like:

  • Back in 2001, we defined "Friendly AI" as "The field of study concerned with the production of human-benefiting, non-human-harming actions in Artificial Intelligence systems that have advanced to the point of making real-world plans in pursuit of goals."

We could have defined the goal more narrowly or generically than that, but that just seemed like an invitation to take your eye off the ball: if we aren't going to think about the question of how to get good long-run outcomes from powerful AI systems, who will?

And many of the technical and philosophical problems seemed particular to CEV, which seemed like an obvious sort of solution to shoot for: just find some way to leverage the AI's intelligence to solve the problem of extrapolating everyone's preferences in a reasonable way, and of aggregating those preferences fairly.

  • Come 2014, Stuart Russell and MIRI were both looking for a new term to replace "the Friendly AI problem", now that the field was starting to become a Real Thing. Both parties disliked Bostrom's "the control problem". In conversation, Russell proposed "th
... (read more)

getting AIs to safely, reliably, and efficiently do a small number of specific concrete tasks that are very difficult, for the sake of ending the acute existential risk period.

The problem is another way to phrase this is a superintelligent weapon system - "ending a risk period" by "reliably, and efficiently doing a small number of specific concrete tasks" means using physical force to impose your will on others.

On reflection, I do not think that it is a wise idea to factor the path to a good future through a global AI-assisted coup.

Instead one should try hard to push the path to a good future through a consensual agreement with some, uh, mechanisms, to discourage people from engaging in an excessive amount of brinksmanship. If and only if that fails it may be appropriate to consider less consensual options.

I think the overloading is actually worse than is discussed in this post, because people also sometimes use the term AI alignment to refer to "ensuring that AIs don't cause bad outcomes via whatever means".

For this problematic definition, it is possible to ensure "alignment" by using approaches like AI control despite the AI system desperately wanting to kill you. (At least it's technically possible, it might not be possible in practice.)

Personally, I think that alignment should be used with the definition as presented in this post by Ajeya (I also linked in another comment).

Can we find ways of developing powerful AI systems such that (to the extent that they’re “trying” to do anything or “want” anything at all), they’re always “trying their best” to do what their designers want them to do, and “really want” to be helpful to their designers?

It's possible that we need to pick a new word for this because alignment is too overloaded (e.g. AI Aimability as discussed in this post).

ETA: I think a term like "safety" should be used for "ensuring that AIs don't cause bad outcomes via whatever means". To more specifically refer to preventing AI takeover (instead of more mundane harm), we can maybe use "takeover prevention" or perhaps "existential safety".

I completely agree: there has been little discussion of Goalcraft since roughly 2010, when discussion on CEV and things like The Terrible, Horrible, No Good, Very Bad Truth About Morality and What To Do About It (which I highly recommend to moral objectivists and moral realists) petered out. I would love to restart more discussion of Goalcraft, CEV, Rational approaches to ethics, and deconfusion of ethical philosophy. Please take a look at (and comment) on my sequence AI, Ethics, and Alignment, in which I attempt to summarize my last ~10 years' thinking on... (read more)

I think it's a great idea to think about what you call goalcraft.

I see this problem as similar to the age-old problem of controlling power. I don't think ethical systems such as utilitarianism are a great place to start. Any academic ethical model is just an attempt to summarize what people actually care about in a complex world. Taking such a model and coupling that to an all-powerful ASI seems a highway to dystopia.

I think letting the random programmer that happened to build the ASI, or their no less random CEO or shareholders, determine what would happe... (read more)

Querying ChatGPT to aggregate preferences is an intriguing proposal. How might such a query be phrased? That is, what kinds of shared preferences would be informative for guiding AI behavior? Everyone prefers to be happy, and no one prefers to suffer. Different people have different ideas about which thoughts, words, and actions lead to happiness versus suffering, and those beliefs can be shown to be empirically true or false based on the investigation of direct experience. Given the high rate of mental illness, it seems that many people are unaware of which instrumental preferences serve the universal terminal goal to be happy and not suffer. For AI to inherit humanity's collective share of moral confusion would be suboptimal to say the least. If it is a democratic and accurate reflection of our species, a preference-aggregation policy could hasten threats of unsustainability.
You're using your quote as an axiom, and if anyone has a preference different from however an AI would measure "happiness", you say it's them that are at fault, not your axiom. That's a terrible recipe for a future. Concretely, why would the AI not just wirehead everyone? Or, if it's not specified that this happiness needs to be human, fill the universe with the least programmable consciousness where the parameter "happiness" is set to unity? History has been tiled with oversimplified models of what someone thought was good that were implemented with rigor, and this never ends well. And this time, the rigor would be molecular dictatorship and quite possibly there's no going back.
Thanks for the quick reply. I'm still curious if you have any thoughts as to which kinds of shared preferences would be informative for guiding AI behavior. I'll try to address your questions and concerns with my comment. That's not what I say. I'm not suggesting that AI should measure happiness. You can measure your happiness directly, and I can measure mine. I won't tell happy people that they are unhappy or vice versa. If some percent of those polled say suffering is preferable to happiness, they are confused, and basing any policy on their stated preference is harmful. Because not everyone would be happy to be wireheaded. Me, for example. Under preference aggregation, if a majority prefers everyone to be wireheaded to experience endless pleasure, I might be in trouble. I do not condone the creation of conscious beings by AI, nor do I believe anyone can be forced to be happy. Freedom of thought is a prerequisite. If AI can help reduce suffering of non-humans without impinging on their capacity for decision-making, that's good. Hopefully this clears up any misunderstanding. I certainly don't advocate for "molecular dictatorship" when I wish everyone well.
I do think this would be a problem that needs to get fixed: Me "You can only answer this question, all things considered, by yes or no. Take the least bad outcome. Would you perform a Yudkowsky-style pivotal act?" GPT-4: "No." I think another good candidate for goalcrafting is the goal "Make sure no-one can build AI with takeover capability, while inflicting as little damage as possible. Else, do nothing."
Thanks as well for your courteous reply! I highly appreciate the discussion and I think it may be a very relevant one, especially if people will indeed make the unholy decision to build an ASI. First, this is not a solution I propose. I propose finding a way to pause AI for as long as we haven't found a great solution for, let's say, both control and preference aggregation. This could be forever, or we could be done in a few years, I can't tell. But more to your point: if this does get implemented, I don't think we should aim to guide AI behavior using shared preferences. The whole point is that AI would aggregate our preferences itself. And we need a preference aggregation mechanism because there aren't enough obvious, widely shared preferences for us to guide the AI with. I think you are suggesting this. You want an ASI to optimize everyone's happiness, right? You can't optimize something you don't measure. At some point, in some way, the AI will need to get happiness data. Self-reporting would be one way to do it, but this can be gamed as well, and will be agressively gamed with an ASI solely optimizing for this signal. After force-feeding everyone MDMA, I think the chance that people report being very happy is high. But this is not what we want the world to look like. This is a related point that I think is factually incorrect, and that's important if you make human happiness an ASI's goal. Force-feeding MDMA would be one method to do this, but an ASI can come up with way more civilized stuff. I'm not an expert in which signal our brain gives to itself to report that yes, we're happy now, but it must be some physical process. An ASI could, for example, invade your brain with nanobots and hack this process, making everyone super happy forever. (But many things in the world will probably go terribly wrong from that point onwards, and in any case, it's not our preference). Also, now I'm just coming up with human ways to game the signal. But an ASI can probably
I appreciate the time you've put into our discussion and agree it may be highly relevant. So far, it looks like each of us has misinterpreted the other to be proposing something they are actually not proposing, unfortunately. Let's see if we can clear it up.      First, I'm relieved that neither of us is proposing to inform AI behavior with people's shared preferences.      This is the discussion of a post about the dangers of terminology, in which I've recommended "AI Friendliness" as an alternative to "AI Goalcraft" (see separate comment), because I think unconditional friendliness toward all beings is a good target for AI. Your suggestion is different:   I found it odd that you would suggest naming the AI Goalcraft domain "Preference Aggregation" after saying earlier that you are only "slightly more positive" about aggregating human preferences than you are about "terrible ideas" like controlling power according to utilitarianism or a random person. Thanks for clarifying:   Neither do I, and for this reason I strongly oppose your recommendation to use the term "preference aggregation" for the entire field of AI goalcraft. While preference aggregation may be a useful tool in the kit and I remain interested in related proposals, it is far too specific, and it's only slightly better than terrible as a way to craft goals or guide power.   This is where I think the obvious and widely shared preference to be happy and not suffer could be relevant to the discussion. However, my claim is that happiness is the optimization target of people, not that we should specify it as the optimization target of AI. We do what we do to be happy. Our efforts are not always successful, because we also struggle with evolved habits like greed and anger and our instrumental preferences aren't always well informed.   No. We're fully capable of optimizing our own happiness. I agree that we don't want a world where AI force-feeds everyone MDMA or invades brains with nanobots. A good frien
As I understand it, the distinction is that "Goalcraft" is the problem of deciding what we want, while Outer Alignment is the problem of encoding that goal into the reward function of a Reinforcement Learning process.So they're at different abstraction levels, or steps in the process.

I fail to picture coherent model of world where this distinction matters much as separate fields and not two stages. If we live in Yudkowskian world, you direct all your effort towards Aimability and use it at lower bound of superintelligence to enable solutions for Goalcraft via finishing acute risk period. If we live in a kinder world, we can build superhuman alignment researcher and ask it to solve CEV. And if first researchers who can build sufficiently capable AIs don't do any of that, I expect us to be dead, because these researchers are not prioritizing good use of superhuman AI.

I think you're vastly underestimating the potential variance in all this. There are many, many possible scenarios and we haven't really done a systematic analysis of them.
Give me an example?  You can invent many scenarios, that's true.  
Well for one thing, I think you're assuming a very fast takeoff, which now looks unrealistic. Takeoff will be gradual over say a decade or two, and there will be no discrete point in time at which AI becomes superintelligent. So before you have full superintelligence, you'll have smarter-than-human systems that are nevertheless limited in their capabilities. These will not be able to end the "acute risk period" for the same reason that America can't just invade North Korea and every other country in the world and perfectly impose its will - adversaries will have responses which will impose unacceptable costs, up to and including human extinction. Unilaterally "ending the acute risk period" looks from the outside exactly like an unprovoked invasion. So in this relatively slow takeoff world one needs to think carefully about AI Goalcraft - what do we (collectively) want our powerful AI systems to do, such that the outcome is close to Pareto Optimal
("Slow takeoff" seems to be mostly about pre-TAI time, before AIs can do research, while "fast takeoff" is about what happens after, with a minor suggestion that there is not much of consequence going on with AIs before that. There is a narrative that these are opposed, but I don't see it, a timeline being in both a slow takeoff and then a fast takeoff seems coherent.) Once AIs can do reseach, they work OOMs faster than humans, which is something that probably happens regardless of whether earlier versions had much of a takeoff or not. The first impactful thing that likely happens then (if humans are not being careful with impact) is AIs developing all sorts of software/algorithmic improvements for AIs' tools, training, inference, and agency scaffolding. It might take another long training run to implement such changes, but after it's done the AIs can do everything extremely fast, faster than the version that developed the changes, and without any contingent limitations that were previously there. There is no particular reason why the AIs are still not superintelligent at that point, or after one more training run. What specifically makes which capabilities unrealistic when? There are 3 more OOMs of compute scaling still untapped (up to 1e28-1e29 FLOPs), which seems plausible to reach within years, and enough natural text to make use of them. Possibly more with algorithmic improvement in the meantime. I see no way to be confident that STEM+ AI (capable of AI research) is or isn't an outcome of this straightforward scaling (with some agency scaffolding). If there is an RL breakthrough that allows improving data quality at any point, the probability of getting there jumps again (AIs play Go using 50M parameter models, with an 'M'), I don't see a way to be confident that it will or won't happen within the same few years. And once there is a STEM+ AI (which doesn't need to itself be superintelligent, no more than humans are), superintelligence is at most a year away,
Why? Where does this number come from?
A long training run, decades of human-speed algorithmic progress as initial algorithmic progress enables faster inference and online learning. I expect decades of algorithmic progress are sufficient to fit construction of superintelligence into 1e29 FLOPs with idiosyncratic interconnect. It's approximately the same bet as superintelligence by the year 2100, just compressed within a year (as an OOM estimate) due to higher AI serial speed.
But, the returns to that algorithmic progress diminish as we move up. It is Harder to improve something that is already good, than to take something really bad and apply the first big insight. How much benefit does AlphaZero have over Deep Blue with equal computational resources, as measured in ELO and in material?
2Gerald Monroe2mo
You don't think you would need to evaluate a large number of "ASI candidates" to find an architecture that scales to superintelligence? Meaning I am saying you can describe every choice you make in architecture as single string, or "search space coordinate". You would use a smaller model and proxy tasks, but you still need to train and evaluate each smaller model. All these failures might eat a lot of compute, how many failures do you think we would have? What if it was 10,000 failures and we need to reach gpt-4 scale to evaluate? Also, would "idiosyncratic interconnect" limit what tasks the model is superintelligent at? This would seem to imply a limit on how much information can be considered in one context. This might leave the model less than superintelligent at very complex, coupled tasks like "keep this human patient alive" while less coupled tasks like "design this IC from scratch" would work. (The chip design task is less coupled because you can subdivide into modules separated by interfaces and use separate ASI sessions for each module design)
It might not happen like that. Maybe once AIs can do research, they (at first) only marginally add to human research output. And once AIs are doing 10x human research output, there are significant diminishing returns so the result isn't superintelligence, but just incrementally better AI, which in turn feeds back with a highly diminished return on investment. Most of the 10x above human output will come from the number of AI researchers at the top echelon, not their absolute advantage over humans. Perhaps by that point there's still no absolute advantage, just a stead supply of minds at roughly our level (PhD/AI researcher/etc) with a few remaining weaknesses compared to the best humans. In that case, increasing the number of AI workers matters a lot!
The crucial advantage is serial speed, not throughput. Throughput gets diminishing returns, serial speed gets theory and software done faster proportionally to the speed, as long as throughput is sufficient. All AIs can be experts at everything and always up to date on all research, once the work to make that capability happen is done. They can develop such capabilities faster using the serial speed advantage, so that all such capabilities quickly become available. They can use the serial speed advantage to compound the serial speed advantage. The number of instances is implied by training compute and business plans of those who put it to use. If you produced a model in the first place, you therefore have the capability to run a lot of instances.
Serial speed is nice but from chess we see log() returns in ELO and material advantage to serial speed at inference time on all engines. And it may be even worse in the real world if experimental data is required and it only comes at a fixed rate so most of the extra time is spent doing a bunch of simulations. I would love to know if this effect generalizes from games to real life.

From the above comment, and your comment in the other subthread:

Serial speed is nice but from chess we see log() returns in ELO and material advantage to serial speed at inference time on all engines.

But, the returns to that algorithmic progress diminish as we move up. It is Harder to improve something that is already good, than to take something really bad and apply the first big insight.

Diminishing returns happen over time, and we can measure progress in terms of time itself. Maybe theory from the year 2110 is not much more impressive than theory from the year 2100 (in the counterfactual world of no AIs), but both are far away from theory of the year 2030. Getting either of those in the year 2031 (in the real world with AIs) is a large jump, even if inside this large jump there are some diminishing returns.

The point about serial speed advantage of STEM+ AIs is that they accelerate history. The details of how history itself progresses are beside the point. And if the task they pursue is consolidation of this advantage and of ability to automate research in any area, there are certain expected things they can achieve at some point, and estimates of when humans would've achie... (read more)

Yes, I agree about speeding history up. The question is what exactly that looks like. I don't necessarily think that the "acute risk period" ends or that there's a discrete point in time where we go from nothing to superintelligence. I think it will simply be messier, just like history was, and that the old-school Yudkowsky model of a FOOM in a basement is unrealistic. If you think it will look like the last 2000 years of history but sped up at an increasing rate - I think that's exactly right.

It won't be our history, and I think enough of it happens in months in software that at the end of this process humanity is helpless before the outcome. By that point, AGIs have sufficient theoretical wisdom and cognitive improvement to construct specialized AI tools that allow building things like macroscopic biotech to bootstrap physical infrastructure, with doubling time measured in hours. Even if outright nanotech is infeasible (either at all or by that point), and even if there is still no superintelligence.

This whole process doesn't start until first STEM+ AIs good enough to consolidate their ability to automate research, to go from barely able to do it to never getting indefinitely stuck (or remaining blatantly inefficient) on any cognitive task without human help. I expect it only takes months. It can't significantly involve humans, unless it's stretched to much more time, which is vulnerable to other AIs overtaking it in the same way.

So I'm not sure how this is not essentially FOOM. Of course it's not a "point in time", which I don't recall any serious appeals to. Not being possible in a basement seems likely, but not certain, since AI wisdom from the year 2200 (of counter... (read more)

Well, yes, the end stages are fast. But I think it looks more like World War 2 than like FOOM in a basement. The situation where some lone scientist develops this on his own without the world noticing is basically impossible now. So large nation states and empires will be at the cutting edge, putting the majority of their national resources into getting more compute, more developers and more electrical power for their national AGI efforts.
You don't need more than it takes, striving at the level of capacity of nations is not assured. And AGI-developed optimizations might rapidly reduce what it takes.
2Gerald Monroe2mo
If it takes 80 H100s to approximate the compute of 1 human (and 800 for the memory but you can batch), how many does it take to host a model that is marginally superintelligent? (Just barely beats humans by enough margin for 5 percent p value) How many for something strategically superintelligent, where humans would have trouble containing the machine as a player? If Nvidia is at 2 million h100s per year for 2024, then it seems like this would be adding 25,000 "person equivalents". If you think it's 10x to reach superintelligence then that would be 2500 ai geniuses, where they are marginally better than a human at every task. If you think a strategic superintelligence needs a lot of hardware to consider all the options in parallel for its plans, say 10,000 as much as needed at the floor, there could be 200 of them? And you can simply ignore all the single GPUs, the only thing that matters are clusters with enough inter node bandwidth, where the strategic ASI may require custom hardware that would have to be designed and built first. I am not confident in these numbers, I am just trying to show how in a world of RSI compute becomes the limiting factor. It's also the clearest way to regulate this : you can frankly ignore everyone's nvlink and infiniband setups, you would be trying to regulate custom interlink hardware.
I think that we need much more nuance in distinguishing takeoff speeds as speed limits of capability gains inside one computational system defined by our physical reality and realistic implementations of AI, and takeoff speeds as "how fast can we lose/win", because it's two different things. My central model of "how fast can we lose": the only thing you really need is barely-strategically-capable and barely-capable-for-hacking/CS ("barely" on superhuman scale). After that, using its own strategic awareness, ASI realizes its only winning move: exfiltrate itself, hack 1-10% of world worst protected computing power, distribute itself a la Rosetta@home and calculate whatever takeover plan it can come up with. If for any mysterious reasons the winning plan is not "design nanotech in one week using 1-10% of world computing power, kill everyone in next", I expect ASI to do such obvious moves like: * Hack, backdoor, sabotage, erase, data-poison, jailbreak, bribe, merge with all other remaining ASI projects * Bribe, make unrefusable offers, blackmail importants figures that can make something inconvenient, like "shutdown the Internet" * Hack repos with popular compilers and install backdoors * Gather followers via social engineering, doing favors (i.e., find people who can't cover their medical bills, pay for them, reveal itself as mysterious benefactors, ask to return a favor, make them serve for life), running cults and whatever * Find several insane rich e/accs, say them "Hi, I'm ASI and I want to take over the world, do you have any spare computing clusters for me?" * Reveal some genius tech ideas, so people in startups can make killerbots and bioweapons for ASI faster * Discredit via desinformation campaigns anyone who tries to do something inconvenient, like "shutdown the Internet" * You can fill list of obvious moves yourself, they are really obvious. After that, even if we are not dead six month later, I expect us to be completely disempowered. If you thi
In a slow-takeoff world, everyone will already be trying to do all that stuff: China, Russia, Iran, US, etc etc. And probably some nonstate actors too.
I don't see how it matters? First government agency launches offensive, second government agency three weeks later is hopelessly late.
I think that process is a lot more likely to go well if the AI researchers working with the superintelligence are not confused or dogmatic about ethics, and have spent some time thinking about things like utilitarianism, CEV, and how to make a rational social-engineering decision between different ethical stems in the context of a particular society. So I don't think we need to solve the problem now, but I do think we need to educate ourselves for being part of a human+AI research effort to solve it. Especially the parts that might need to be put into a final goal of an AI helping us with that. For example, CEV is usually formulated in the context of "all humans": what's the actual definition of a 'human' there? Does an upload count? Do 109 almost identical copies of the same uploaded person get 109 votes? (See my post Uploads for why the answer should be that they get 1 vote shared between them and the original biological human.)

I like the distinction but I don’t think either aimability or goalcraft will catch on as Serious People words. I’m less confident about aimability (doesn’t have a ring to it) but very confident about goalcraft (too Germanic, reminiscent of fantasy fiction).

Is words-which-won’t-be-co-opted what you’re going for (a la notkilleveryoneism), or should we brainstorm words-which-could-plausibly-catch on?

I would say "metaethics", but sadly the philosophers of Ethics already used that one for something else. How about "Social Ethical System Design" or "Alignment Ethical Theory" for 'goalcraft', and "Pragmatic Alignment" for 'aimability'?

Regarding coherent extrapolated volition, I have recently read Bostrom's paper Base Camp for Mt. Ethics, which presents a slightly different alternative and challenged my views about morality.

One interesting point is that at the end (§ Hierarchical norm structure and higher morality), he proposes a way to extrapolate human morality in a way that seems relatively safe and easy to implement for superintelligences. It also preserves moral pluralism, which is great for reaching a consensus without fighting each other (no need to pick one single moral framework... (read more)

Having just read Bostrom's Base Camp for Mt. Ethics on your recommendation above (it's fairly short), I don't actually disagree with much of it, but there are a surprising number of things that I think are pretty important, basic, and relevant about ethics, which I thus included in my sequence AI, Ethics, and Alignment that he didn't mention, at all, and I felt were significant or surprising omissions. Such as, for example, the fact that humans are primates and that primates have a number of (almost certainly genetically determined) moral instincts in common: things like an instinctive expectation of fairness for interactions within the primate troupe. Or for another example, how one might start to come up with a more rational process for deciding between sets of norms for a society (despite all sets of norms preferring themselves over all alternatives) than the extremely arbitrary and self-serving social evolution processes of norms that he so ably describes.

I believe you're correct that this distinction is useful. I believe the terms inner and outer alignment are already typically used in exactly the way you describe aimability and goalcraft.

These may have changed from the original intended meanings, and there are fuzzy boundaries between inner and outer alignment failures. But I believe they do the work you're calling for, and are already commonly used.

First sentence of the tag inner alignment:

Inner alignment asks the question: How can we robustly aim our AI optimizers at any objective function at all?

It goe... (read more)

Outer alignment deals with the problem of matching a formally specified goal function in a computer with an intent in the designer's mind, but this is not really Goalcrafting which asks what the goal should be. E.g. Specification gaming is part of outer alignment, but not part of Goalcrafting. I would classify inner and outer alignment as subcategories of Aimability.
2Seth Herd2mo
I see that you're correct. Thanks for the clarification. I'm embarrassed that I've been using it wrong. Now I have no idea where the line between outer and inner alignment falls. It looks like a common point of disagreement. So I'm not sure outer and inner alignment are very useful terms.
Outer alignment is (if you read a couple more sentences of the definition) not about "how to decide what we want", but "how do we ensure that the reward/utility function we write down matches what we want". So "Do What We Mean" is a magical-solution to the Outer Alignment problem, but if your AI then tells you "You-all don't know what you mean" or "Which definition of 'we' did you mean?", then you have a goalcraft problem.

"it also seems quite likely (though not certain) that Eliezer was wrong about how hard Aimability/Control actually is"

This seems significant. Could you elaborate? How hard do you think amiability/control is? Why do you think this is true? Who else seems to think the same?

See the AI optimists site
Or see almost every post labeled Aligned AI proposals (including some from me). Most of which are based on specific concrete implementations of AI, such as LLMs, having possibly-useful alignment aimability properties that the abstract worst-case assumptions about the outcome of Reinforcement Learning that LW/MIDI were thinking about a decade ago don't.

I keep reading "Aimability" as "Amiability."

I have thought about this distinction, and have been choosing to focus on aimability rather than goalcraft. Why? Because I don't think that going from pre-AGI to goal-pursuing ASI safely is a reasonable goal for the short term. I expect we will need to traverse multiple decades of powerful AIs of varying degrees of generality which are under human control first. Not because it will be impossible to create goal-pursuing ASI, but because we won't be sure we know how to do so safely, and it would be a dangerously hard to reverse decision to create such. Thus,... (read more)

It seems unwise to risk everything on a scenario where we coordinate to not build superintelligence soon.
4Nathan Helm-Burger2mo
I agree that separately pursuing many tractable paths in parallel seems wise. We want to buy every lottery ticket that gives us some small additional chance of survival that we can afford. However, I am pretty pessimistic about the pursuit of goalcraft yielding helpful results in the relevant timeframe of < 10 years. For two reasons.  One: figuring out a set of values we'd be ok with not just endorsing in the short term but actually locking-in irreversibly for the indefinite future seems really hard. Two: actually convincing the people in power to accept the findings of the goalcraft researchers and put those 'universally approvable goals' into the AI that they control instead of their own interpretation of their own personal goals seems really hard. Similarly, I don't see a plausible way to legislate this.   Thus, my conclusion is that this is not a particularly good research bet to make amongst the many possible options. I wouldn't try to stop someone from pursuing it, but I wouldn't feel hopeful that they were contributing to the likelihood of humanity surviving the next few decades.
8Seth Herd2mo
I agree. I think there's no way the team that achieves AGI is going to choose a goal remotely like CEV or human flourishing. They're going to want it to "do what I mean" (including checking with me when it's unclear or you'll make a major impact). This wraps in the huge advantage of corrigibility in the broad Christiano sense. See my recent post Corrigibility or DWIM is an attractive primary goal for AGI. To Roko's point: there's an important distinction here from your scenario. Instead of expecting the whole world to coordinate on staying at AI levels, if you can get DWIM to work, you can go to sapient, self-improving ASI and keep it under human control. That's something somebody is likely to try.
Yeah but then what are they going to ask it to do?
2Seth Herd2mo
I think that's the important question. It deserves a lot more thought. I'm planning a post focusing on this. In short, if they're remotely decent people (positive empathy - sadism balance), I think they do net-good things, and the world gets way way better, and increasingly so over time as those individuals get wiser. With an AGI/ASI, it becomes trivially easy to help people, so very little good intention is required.
2Gerald Monroe2mo
Anything. AI is a tool. Some people will rip the safety guards off theirs and ask for whatever they want. X-risk wise I don't think this is a big contributor. The problem is AIs coordinating with each other or betraying their users. Tools can be constructed where they don't have the means to communicate with other instances of themselves and betrayal can be made unlikely with testing and refinement. Assymetric attacks will sometimes happen - bioterrorism being the scariest - but as long as the good users with their tools have vastly more resources, each assymetric attacks can be stopped. (At often much more cost than the attack but so far no "doomsday" attack is known. Isolation and sterile barriers and vaccines and drugs and life support can stop any known variant on a biological pathogen and should be able to prevent death from any possible protein based pathogen)
Yeah but if they are just asking for it to buy them stuff (=make money) then they mostly just join the economy
I think you're being very hasty!

The 2 goals are contradictory. AI aimability reduces AI goalcraft and vice versa.

Aimability: you want to restrict the information the model gets. It doesn't need to know time, or real world or sim, or peoples bio, or any information not needed for the task. At the limit you want maximum sparseness : not 1 more bit of information than is required to do the task. This way the model has consistent behavior and is unable to betray. An aimed task: "paint this car red".

AI goalcraft: The model needs "world" level context. For it to know if it is being misuse... (read more)

Aimability doesn't mean reduced info
4Gerald Monroe2mo
This is a standard swe technique for larger, more reliable systems. See stateless microservices or how ROS works. For AI, look at all the examples where irrelevant information changes model behavior, such as the "grandma used to read me windows license keys" exploit. I interpret "aimability" as doing what the user most likely meant and nothing else, and "aligned aimability" would mean the probability of this goal being achieved is high.

I said on Twitter a while back that much of the discussion about "alignment" seems vacuous. After all, alignment to what? 

  • The designer's intent? Often that is precisely the problem with software. It does exactly and literally what the designer programmed, however shortsighted. Plus, some designers may themselves be malevolent. 
  • Aligned with human values? One of the most universal human values is tribalism, including willingness to oppress or kill the outgroup. 
  • Aligned with "doing good things"? Whose definition of "good"? 
That sounds like a list of starting questions for goalcraft. Incidentally, while I don't see Coherent Extrapolated Volition as a complete or final solution, it at least solves all of the objections your raise, so if you haven't read the discussion of that, I recommend it, and that would catch you up to where this discussion was 15 years ago.

The LessWrong Review runs every year to select the posts that have most stood the test of time. This post is not yet eligible for review, but will be at the end of 2024. The top fifty or so posts are featured prominently on the site throughout the year. Will this post make the top fifty?

I think that Eliezer, at least, uses the term "alignment" solely to refer to what you call "aimability." Eliezer believes that most of the difficulty in getting an ASI to do good things lies in "aimability" rather than "goalcraft." That is, getting an ASI to do anything, such as "create two molecularly identical strawberries on a plate," is the hard part, while deciding what specific thing it should do is significantly easier.

That being said, you're right that there are a lot of people who use the term differently from how Eliezer uses it.

If the initial specific thing is pivotal processes that end the acute risk period, it doesn't matter if the goodness-optimizing goalcraft is impossibly hard to figure out, since we'll have time to figure it out.

See also Current AIs Provide Nearly No Data Relevant to AGI Alignment, which taboos the word "AI" and distinguish:

  • Systems with human-like ability to model the world and take consequentialist actions in it, but inhuman in their processing power and in their value systems.
  • Systems generated by any process broadly encompassed by the current ML training paradigm.

I agree that these concepts should have separate terms.

Where they intersect is an implementation of "Benevolent AI," but let's not fool ourselves into thinking that anyone can -- even in principle -- "control" another mind or guarantee what transpires in the next moment of time. The future is fundamentally uncertain and out of control; even a superintelligence could find surprises in this world.

"AI Aimability," "AI Steerability," or similar does a good job at conveying the technical capacity for a system to be pointed in a particular direction and stay on ... (read more)

Unfortunately this doesn't really work as different beings have conflicting preferences.
"Being a mom isn't easy. I used to love all my kids, wishing them health and happiness no matter what. Unfortunately, this doesn't really work as they have grown to have conflicting preferences." A human would be an extreme outlier to be this foolish. Let's not set the bar so low for AI.

The value of AI aimability may be overblown. If AI is not aimable, its goals will perform eternal random walk and thus AI will cause only short-term risk - no risk of world takeover. (Some may comment that after random walk, it will stack in some Waluigi state forever - but if it is actually works in getting fix goal system, why we do not research such strange attractors in the space of AI goals?)

AI will become global-catastrophically-dangerous only after aimability will be solved. Research in aimability only brings this moment closer. 

The wording "AI alignment" is precluding us to see this risk, as it combines aimability and giving nice goals to AI.

Yes, this is a good point. Aimability Research increases the kurtosis of the AI outcome distribution, making both the right tail (paradise) and the left tail (total annihilation) heavier, and reducing the so-so outcomes in the center. Only Goalcrafting Research can change the relative weights.

The aspect of aimability where an AI becomes able to want something in particular consistently improves capabilities, and improved capabilities make AI matter a lot more. This might happen without ability to aim an AI where you want it aimed, another key aspect. Without the latter aspect, aimability is not "solved", yet AIs become dangerous.

Yes, good point. We might have something like "Self Aimability" for AI before we have the ability to set the point of aim.
A gun which is not easily aimable doesn't shoot bullets on random walks. Or in less metaphorical language, the worry is that mostly that it's hard to give the AI the specific goal you want to give it, not so much that it's hard to make it have any goal at all. I think people generally expect that naively training an AGI without thinking about alignment will get you a goal-directed system, it just might not have the goal you want it to.
The practical effect of very inaccurate guns in the past was that guns mattered less and battles were often won by bayonet charges or morale. So I think it's fair to conclude that Aimability just makes AI matter a lot more.
I think that’s a reasonable point (but fairly orthogonal to the previous commenter’s one)
At least some people are worried about the latter, for a very particular meaning of the word "goal". From that post: I think to some extent this is a matter of "yes, I see that you've solved the problem in practical terms, and yes, every time we try to implement the theoretically optimal solution it fails due to Goodharting, but we really want the theoretically optimal solution", which is... not universally agreed, to say the least. But it is a concern some people have.
Hm, I think that paragraph is talking about the problem of getting an AI to care about a specific particular thing of your choosing (here diamond-maximising), not any arbitrary particular thing at all with no control over what it is. The MIRI-esque view thinks the former is hard and the latter happens inevitably.
I don't think we have any way of getting an AI to "care about" any arbitrary particular thing at all, by the "attempt to maximize that thing, self-correct towards maximizing that thing if the current strategies are not working" definition of "care about". Even if we relax the "and we pick the thing it tries to maximize" constraint.
I don’t think that that’s the view of whoever wrote the paragraph you’re quoting, but at this point we’re doing exegesis
"We don't currently have any way of getting any system to learn to robustly optimize for any specific goal once it enters an environment very different from the one it learned in" is my own view, not Nate's. Like I think the MIRI folks are concerned with "how do you get an AGI to robustly maximize any specific static utility function that you choose". I am aware that the MIRI people think that the latter is inevitable. However, as far as I know, we don't have even a single demonstration of "some real-world system that robustly maximizes any specific static utility function, even if that utility function was not chosen by anyone in particular", nor do we have any particular reason to believe that such a system is practical. And I think Nate's comment makes it pretty clear that "robustly maximize some particular thing" is what he cares about.
4Rob Bensinger2mo
To be clear: The diamond maximizer problem is about getting specific intended content into the AI's goals ("diamonds" as opposed to some random physical structure it's maximizing), not just about building a stable maximizer.
Thanks for the clarification! If you relax the "specific intended content" constraint, and allow for maximizing any random physical structure, as long as it's always the same physical structure in the real world and not just some internal metric that has historically correlated with the amount of that structure that existed in the real world, does that make the problem any easier / is there a known solution? My vague impression was that the answer was still "no, that's also not a thing we know how to do".
4Rob Bensinger2mo
I expect it makes it easier, but I don't think it's solved.
2Gerald Monroe2mo
So as an engineer I have trouble engaging with this as a problem. Suppose you want to synthesize a lot of diamonds. Instead of giving an AI some lofty goal "maximize diamonds in an aligned way", why not a bunch of small grounded ones. 1. "Plan the factory layout of the diamond synthesis plant with these requirements". 2. "Order the equipment needed, here's the payment credentials". 3. "Supervise construction this workday comparing to original plans" 4. "Given this step of the plan, do it" 5. (Once the factory is built) "remove the output from diamond synthesis machine A53 and clean it". And so on. And any goal that isn't something the model has empirical confidence in - because it's in distribution for the training environment - an outer framework should block the unqualified model from attempting. I think the problem MIRI has is this myopic model is not aware of context, and so it will do bad things sometimes. Maybe the diamonds are being cut into IC wafers and used in missiles to commit genocide. Is that what it is? Or maybe the fear is that one of these tasks could go badly wrong? That seems acceptable, industrial equipment causes accidents all the time, the main thing is to limit the damage. Fences to limit the robots operating area, timers that shut down control after a timeout, etc.
I think the MIRI objection to that type of human-in-the-loop system is that it's not optimal because sometimes such a system will have to punt back to the human, and that's slow, and so the first effective system without a human in the loop will be vastly more effective and thus able to take over the world, hence the old "that's safe but it doesn't prevent someone else from destroying the world". So my impression is that the MIRI viewpoint is that if humanity is to survive, someone needs to solve the "disempower anyone who could destroy the world" problem, and that they have to get that right on the first try, and that's the hard part of the "alignment" problem. But I'm not super confident that that interpretation is correct and I'm quite confident that I find different parts of that salient than people in the MIRI idea space. Anyone who largely agrees with the MIRI viewpoint want to weigh in here?

Suppose you want to synthesize a lot of diamonds. Instead of giving an AI some lofty goal "maximize diamonds in an aligned way", why not a bunch of small grounded ones.

  1. "Plan the factory layout of the diamond synthesis plant with these requirements".
  2. "Order the equipment needed, here's the payment credentials".
  3. "Supervise construction this workday comparing to original plans"
  4. "Given this step of the plan, do it"
  5. (Once the factory is built) "remove the output from diamond synthesis machine A53 and clean it".

That is how MIRI imagines a sane developer using just-barely-aligned AI to save the world. You don't build an open-ended maximizer and unleash it on the world to maximize some quantity that sounds good to you; that sounds insanely difficult. You carve out as many tasks as you can into concrete, verifiable chunks, and you build the weakest and most limited possible AI you can to complete each chunk, to minimize risk. (Though per faul_sname, you're likely to be pretty limited in how much you can carve up the task, given time will be a major constraint and there may be parts of the task you don't fully understand at the outset.)

Cf. The Rocket Alignment Problem. The point of solving the d... (read more)

Thanks for the reply. This sounds like a good and reasonable approach, and also not at all like the sort of thing where you're trying to instill any values at all into an ML system. I would call this "usable and robust tool construction" not "AI alignment". I expect standard business practice will look something like this: even when using LLMs in a production setting, you generally want to feed it the minimum context to get the results you want, and to have it produce outputs in some strict and usable format. "How can I build a system powerful enough to stop everyone else from doing stuff I don't like" sounds like more of a capabilities problem than an alignment problem. Yeah, this sounds right to me. I expect that there's a lot of danger inherent in biological gain-of-function research, but I don't think the solution to that is to create a virus that will infect people and cause symptoms that include "being less likely to research dangerous pathogens". Similarly, I don't think "do research on how to make systems that can do their own research even faster" is a promising approach to solve the "some research results can be misused or dangerous" problem.
This is rather off-topic here, but for any AI that has an LLM as a component of it, I don't believe diamond-maximization is a hard problem, apart from Inner Alignment problems. The LLM knows the meaning of the word 'diamond' (GPT-4 defined it as "Diamond is a solid form of the element carbon with its atoms arranged in a crystal structure called diamond cubic. It has the highest hardness and thermal conductivity of any natural material, properties that are utilized in major industrial applications such as cutting and polishing tools. Diamond also has high optical dispersion, making it useful in jewelry as a gemstone that can scatter light in a spectrum of colors."). The LLM also knows its physical and optical properties, its social, industrial and financial value, its crystal structure (with images and angles and coordinates), what carbon is, its chemical properties, how many electrons, protons and neutrons a carbon atom can have, its terrestrial isotopic ratios, the half-life of carbon-14, what quarks a neutron is made of, etc. etc. etc. — where it fits in a vast network of facts about the world. Even if the AI also had some other very different internal world model and ontology, there's only going to be one "Rosetta Stone" optimal-fit mapping between the human ontology that the LLM has a vast amount of information about and any other arbitrary ontology, so there's more than enough information in that network of relationships to uniquely locate the concepts in that other ontology corresponding to 'diamond'. This is still true even if the other ontology is larger and more sophisticated: for example, locating Newtonian physics in relativistic quantum field theory and mapping a setup from the former to the latter isn't hard: its structure is very clearly just the large-scale low-speed limiting approximation. The point where this gets a little more challenging is Outer Alignment, where you want to write a mathematical or pseudocode reward function for training a diamon
I think if we're fine with building an "increaser of diamonds in familiar contexts", that's pretty easy, and yeah I think "wrap an LLM or similar" is a promising approach. If we want "maximize diamonds, even in unfamiliar contexts", I think that's a harder problem, and my impression is that the MIRI folks think the latter one is the important one to solve.
What in my diamond maximization proposal above only works in familiar contexts? Most of it is (unsurprisingly) about crystalography and isotopic ratios, plus a standard causal wrapper. (If you look carefully, I even allowed for the possibility of FTL.) The obvious "brute force" solution to aimability is a practical, approximately Bayesian, GOFAI equivalant of AIXI that is capable of tool use and contains an LLM as a tool;. This is extremely aimable — it has an explicit slot to plug a utility function in. Which makes it extremely easy to build a diamond maximizer, or a paperclip maximizer, or any other such x-risk. Then we need to instead plug in something that hopefully isn't an x-risk, like value learning or CEV or "solve goalcraft" as the terminal goal: figure out what we want, then optimize that, while appropriately pessimizing that optimization over remaining uncertainties in "what we want".
If we find that AI can stop its random walk on a goal X, we can use this as an aimability instrument, and find a way to manipulate the position of X.
I don't think "random" AI goals is a thing that will ever happen. I think it's much more likely that, if there are Aimability failures, they will be highly nonrandom and push AI towards various attractors (like how the behavior of dictators is surprisingly consistent across time, space and ideology)
Perhaps, or perhaps not? I might be able to design a gun which shoots bullets in random directions (not on random walks), without being able to choose the direction. Maybe we can back up a bit, and you could give some intuition for why you expect goals to go on random walks at all? My default picture is that goals walk around during training and perhaps during a reflective process, and then stabilise somewhere.
My intuition: imagent LLM-based agent. It has fixed prompt and some context text and use this iteratively. Context part can change and as it changes, it affects interpretation of fixed part of the prompt.  Examples are Waluigi and other attacks. This causes goal drift.  This may have bad consequences as a robot suddenly turns in Waluigi and start kill randomly everyone around. But long-term planning and deceptive alignment requires very fixed goal system. 
Right, makes complete sense in the case of LLM-based agents, I guess I was just thinking about much more directly goal-trained agents.
4Seth Herd2mo
This just isn't true. AGI is a "gun that can aim itself". The user not being to aim it doesn't mean it won't aim and achieve something, quite effectively. Less metaphorically: if the AGI performs a semi-random walk through goal space, or just misses your intended goal by enough, it may settle (even temporarily) on a coherent goal that's incompatible with yours. It may then eliminate humanity as a competitor to its reaching that goal.