Counterarguments to the basic AI x-risk case

Katja Grace, 31 August 2022
This is going to be a list of holes I see in the basic argument for existential risk from superhuman AI systems1.
To start, here’s an outline of what I take to be the basic case2:
I. If superhuman AI systems are built, any given system is likely to be ‘goal-directed’
Reasons to expect this:

Goal-directed behavior is likely to be valuable, e.g. economically.
Goal-directed entities may tend to arise from machine learning training processes not intending to create them (at least via the methods that are likely to be used).
‘Coherence arguments’ may imply that systems with some goal-directedness will become more strongly goal-directed over time.

II. If goal-directed superhuman AI systems are built, their desired outcomes will probably be about as bad as an empty universe by human lights
Reasons to expect this:

Finding useful goals that aren’t extinction-level bad appears to be hard: we don’t have a way to usefully point at human goals, and divergences from human goals seem likely to produce goals that are in intense conflict with human goals, due to a) most goals producing convergent incentives for controlling everything, and b) value being ‘fragile’, such that an entity with ‘similar’ values will generally create a future of virtually no value.
Finding goals that are extinction-level bad and temporarily useful appears to be easy: for example, advanced AI with the sole objective ‘maximize company revenue’ might profit said company for a time before gathering the influence and wherewithal to pursue the goal in ways that blatantly harm society.
Even if humanity found acceptable goals, giving a powerful AI system any specific goals appears to be hard. We don’t know of any procedure to do it, and we have theoretical reasons to expect that AI systems produced through machine learning training will generally end up with goals other than those they were trained according to. Randomly aberrant goals resulting are probably extinction-level bad for reasons described in II.1 above.

III. If most goal-directed superhuman AI systems have bad goals, the future will very likely be bad
That is, a set of ill-motivated goal-directed superhuman AI systems, of a scale likely to occur, would be capable of taking control over the future from humans. This is supported by at least one of the following being true:

Superhuman AI would destroy humanity rapidly. This may be via ultra-powerful capabilities at e.g. technology design and strategic scheming, or through gaining such powers in an ‘intelligence explosion‘ (self-improvement cycle). Either of those things may happen either through exceptional heights of intelligence being reached or through highly destructive ideas being available to minds only mildly beyond our own.
Superhuman AI would gradually come to control the future via accruing power and resources. Power and resources would be more available to the AI system(s) than to humans on average, because of the AI having far greater intelligence.

***
Below is a list of gaps in the above, as I see it, and counterarguments. A ‘gap’ is not necessarily unfillable, and may have been filled in any of the countless writings on this topic that I haven’t read. I might even think that a given one can probably be filled. I just don’t know what goes in it.
This blog post is an attempt to run various arguments by you all on the way to making pages on AI Impacts about arguments for AI risk and corresponding counterarguments. At some point in that process I hope to also read others’ arguments, but this is not that day. So what you have here is a bunch of arguments that occur to me, not an exhaustive literature review.
Counterarguments
A. Contra “superhuman AI systems will be ‘goal-directed’”
Different calls to ‘goal-directedness’ don’t necessarily mean the same concept
‘Goal-directedness’ is a vague concept. It is unclear that the ‘goal-directednesses’ that are favored by economic pressure, training dynamics or coherence arguments (the component arguments in part I of the argument above) are the same ‘goal-directedness’ that implies a zealous drive to control the universe (i.e. that makes most possible goals very bad, fulfilling II above).
One well-defined concept of goal-directedness is ‘utility maximization’: always doing what maximizes a particular utility function, given a particular set of beliefs about the world.
Utility maximization does seem to quickly engender an interest in controlling literally everything, at least for many utility functions one might have3. If you want things to go a certain way, then you have reason to control anything which gives you any leverage over that, i.e. potentially all resources in the universe (i.e. agents have ‘convergent instrumental goals’). This is in serious conflict with anyone else with resource-sensitive goals, even if prima facie those goals didn’t look particularly opposed. For instance, a person who wants all things to be red and another person who wants all things to be cubes may not seem to be at odds, given that all things could be red cubes. However if these projects might each fail for lack of energy, then they are probably at odds.
Thus utility maximization is a notion of goal-directedness that allows Part II of the argument to work, by making a large class of goals deadly.
You might think that any other concept of ‘goal-directedness’ would also lead to this zealotry. If one is inclined toward outcome O in any plausible sense, then does one not have an interest in anything that might help procure O? No: if a system is not a ‘coherent’ agent, then it can have a tendency to bring about O in a range of circumstances, without this implying that it will take any given effective opportunity to pursue O. This assumption of consistent adherence to a particular evaluation of everything is part of utility maximization, not a law of physical systems. Call machines that push toward particular goals but are not utility maximizers pseudo-agents.
Can pseudo-agents exist? Yes—utility maximization is computationally intractable, so any physically existent ‘goal-directed’ entity is going to be a pseudo-agent. We are all pseudo-agents, at best. But it seems something like a spectrum. At one end is a thermostat, then maybe a thermostat with a better algorithm for adjusting the heat. Then maybe a thermostat which intelligently controls the windows. After a lot of honing, you might have a system much more like a utility-maximizer: a system that deftly seeks out and seizes well-priced opportunities to make your room 68 degrees—upgrading your house, buying R&D, influencing your culture, building a vast mining empire. Humans might not be very far on this spectrum, but they seem enough like utility-maximizers already to be alarming. (And it might not be well-considered as a one-dimensional spectrum—for instance, perhaps ‘tendency to modify oneself to become more coherent’ is a fairly different axis from ‘consistency of evaluations of options and outcomes’, and calling both ‘more agentic’ is obscuring.)
Nonetheless, it seems plausible that there is a large space of systems which strongly increase the chance of some desirable objective O occurring without even acting as much like maximizers of an identifiable utility function as humans would. For instance, without searching out novel ways of making O occur, or modifying themselves to be more consistently O-maximizing. Call these ‘weak pseudo-agents’.
For example, I can imagine a system constructed out of a huge number of ‘IF X THEN Y’ statements (reflexive responses), like ‘if body is in hallway, move North’, ‘if hands are by legs and body is in kitchen, raise hands to waist’.., equivalent to a kind of vector field of motions, such that for every particular state, there are directions that all the parts of you should be moving. I could imagine this being designed to fairly consistently cause O to happen within some context. However since such behavior would not be produced by a process optimizing O, you shouldn’t expect it to find new and strange routes to O, or to seek O reliably in novel circumstances. There appears to be zero pressure for this thing to become more coherent, unless its design already involves reflexes to move its thoughts in certain ways that lead it to change itself. I expect you could build a system like this that reliably runs around and tidies your house say, or runs your social media presence, without it containing any impetus to become a more coherent agent (because it doesn’t have any reflexes that lead to pondering self-improvement in this way).
It is not clear that economic incentives generally favor the far end of this spectrum over weak pseudo-agency. There are incentives toward systems being more like utility maximizers, but also incentives against.
The reason any kind of ‘goal-directedness’ is incentivised in AI systems is that then the system can be given an objective by someone hoping to use their cognitive labor, and the system will make that objective happen. Whereas a similar non-agentic AI system might still do almost the same cognitive labor, but require an agent (such as a person) to look at the objective and decide what should be done to achieve it, then ask the system for that. Goal-directedness means automating this high-level strategizing.
Weak pseudo-agency fulfills this purpose to some extent, but not as well as utility maximization. However if we think that utility maximization is difficult to wield without great destruction, then that suggests a disincentive to creating systems with behavior closer to utility-maximization. Not just from the world being destroyed, but from the same dynamic causing more minor divergences from expectations, if the user can’t specify their own utility function well.
That is, if it is true that utility maximization tends to lead to very bad outcomes relative to any slightly different goals (in the absence of great advances in the field of AI alignment), then the most economically favored level of goal-directedness seems unlikely to be as far as possible toward utility maximization. More likely it is a level of pseudo-agency that achieves a lot of the users’ desires without bringing about sufficiently detrimental side effects to make it not worthwhile. (This is likely more agency than is socially optimal, since some of the side-effects will be harms to others, but there seems no reason to think that it is a very high degree of agency.)
Some minor but perhaps illustrative evidence: anecdotally, people prefer interacting with others who predictably carry out their roles or adhere to deontological constraints, rather than consequentialists in pursuit of broadly good but somewhat unknown goals. For instance, employers would often prefer employees who predictably follow rules than ones who try to forward company success in unforeseen ways.
The other arguments to expect goal-directed systems mentioned above seem more likely to suggest approximate utility-maximization rather than some other form of goal-directedness, but it isn’t that clear to me. I don’t know what kind of entity is most naturally produced by contemporary ML training. Perhaps someone else does. I would guess that it’s more like the reflex-based agent described above, at least at present. But present systems aren’t the concern.
Coherence arguments are arguments for being coherent a.k.a. maximizing a utility function, so one might think that they imply a force for utility maximization in particular. That seems broadly right. Though note that these are arguments that there is some pressure for the system to modify itself to become more coherent. What actually results from specific systems modifying themselves seems like it might have details not foreseen in an abstract argument merely suggesting that the status quo is suboptimal whenever it is not coherent. Starting from a state of arbitrary incoherence and moving iteratively in one of many pro-coherence directions produced by whatever whacky mind you currently have isn’t obviously guaranteed to increasingly approximate maximization of some sensical utility function. For instance, take an entity with a cycle of preferences, apples > bananas = oranges > pears > apples. The entity notices that it sometimes treats oranges as better than pears and sometimes worse. It tries to correct by adjusting the value of oranges to be the same as pears. The new utility function is exactly as incoherent as the old one. Probably moves like this are rarer than ones that make you more coherent in this situation, but I don’t know, and I also don’t know if this is a great model of the situation for incoherent systems that could become more coherent.
What it might look like if this gap matters: AI systems proliferate, and have various goals. Some AI systems try to make money in the stock market. Some make movies. Some try to direct traffic optimally. Some try to make the Democratic party win an election. Some try to make Walmart maximally profitable. These systems have no perceptible desire to optimize the universe for forwarding these goals because they aren’t maximizing a general utility function, they are more ‘behaving like someone who is trying to make Walmart profitable’. They make strategic plans and think about their comparative advantage and forecast business dynamics, but they don’t build nanotechnology to manipulate everybody’s brains, because that’s not the kind of behavior pattern they were designed to follow. The world looks kind of like the current world, in that it is fairly non-obvious what any entity’s ‘utility function’ is. It often looks like AI systems are ‘trying’ to do things, but there’s no reason to think that they are enacting a rational and consistent plan, and they rarely do anything shocking or galaxy-brained.
Ambiguously strong forces for goal-directedness need to meet an ambiguously high bar to cause a risk
The forces for goal-directedness mentioned in I are presumably of finite strength. For instance, if coherence arguments correspond to pressure for machines to become more like utility maximizers, there is an empirical answer to how fast that would happen with a given system. There is also an empirical answer to how ‘much’ goal directedness is needed to bring about disaster, supposing that utility maximization would bring about disaster and, say, being a rock wouldn’t. Without investigating these empirical details, it is unclear whether a particular qualitatively identified force for goal-directedness will cause disaster within a particular time.
What it might look like if this gap matters: There are not that many systems doing something like utility maximization in the new AI economy. Demand is mostly for systems more like GPT or DALL-E, which transform inputs in some known way without reference to the world, rather than ‘trying’ to bring about an outcome. Maybe the world was headed for more of the latter, but ethical and safety concerns reduced desire for it, and it wasn’t that hard to do something else. Companies setting out to make non-agentic AI systems have no trouble doing so. Incoherent AIs are never observed making themselves more coherent, and training has never produced an agent unexpectedly. There are lots of vaguely agentic things, but they don’t pose much of a problem. There are a few things at least as agentic as humans, but they are a small part of the economy.
B. Contra “goal-directed AI systems’ goals will be bad”
Small differences in utility functions may not be catastrophic
Arguably, humans are likely to have somewhat different values to one another even after arbitrary reflection. If so, there is some extended region of the space of possible values that the values of different humans fall within. That is, ‘human values’ is not a single point.
If the values of misaligned AI systems fall within that region, this would not appear to be worse in expectation than the situation where the long-run future was determined by the values of humans other than you. (This may still be a huge loss of value relative to the alternative, if a future determined by your own values is vastly better than that chosen by a different human, and if you also expected to get some small fraction of the future, and will now get much less. These conditions seem non-obvious however, and if they obtain you should worry about more general problems than AI.)
Plausibly even a single human, after reflecting, could on their own come to different places in a whole region of specific values, depending on somewhat arbitrary features of how the reflecting period went. In that case, even the values-on-reflection of a single human is an extended region of values space, and an AI which is only slightly misaligned could be the same as some version of you after reflecting.
There is a further larger region, ‘that which can be reliably enough aligned with typical human values via incentives in the environment’, which is arguably larger than the circle containing most human values. Human society makes use of this a lot: for instance, most of the time particularly evil humans don’t do anything too objectionable because it isn’t in their interests. This region is probably smaller for more capable creatures such as advanced AIs, but still it is some size.
Thus it seems that some amount4 of AI divergence from your own values is probably broadly fine, i.e. not worse than what you should otherwise expect without AI.
Thus in order to arrive at a conclusion of doom, it is not enough to argue that we cannot align AI perfectly. The question is a quantitative one of whether we can get it close enough. And how close is ‘close enough’ is not known.
What it might look like if this gap matters: there are many superintelligent goal-directed AI systems around. They are trained to have human-like goals, but we know that their training is imperfect and none of them has goals exactly like those presented in training. However if you just heard about a particular system’s intentions, you wouldn’t be able to guess if it was an AI or a human. Things happen much faster than they were, because superintelligent AI is superintelligent, but not obviously in a direction less broadly in line with human goals than when humans were in charge.
Differences between AI and human values may be small
AI trained to have human-like goals will have something close to human-like goals. How close? Call it d, for a particular occasion of training AI.
If d doesn’t have to be 0 for safety (from above), then there is a question of whether it is an acceptable size.
I know of two issues here, pushing d upward. One is that with a finite number of training examples, the fit between the true function and the learned function will be wrong. The other is that you might accidentally create a monster (‘misaligned mesaoptimizer’) who understands its situation and pretends to have the utility function you are aiming for so that it can be freed and go out and manifest its own utility function, which could be just about anything. If this problem is real, then the values of an AI system might be arbitrarily different from the training values, rather than ‘nearby’ in some sense, so d is probably unacceptably large. But if you avoid creating such mesaoptimizers, then it seems plausible to me that d is very small.
If humans also substantially learn their values via observing examples, then the variation in human values is arising from a similar process, so might be expected to be of a similar scale. If we care to make the ML training process more accurate than the human learning one, it seems likely that we could. For instance, d gets smaller with more data.
Another line of evidence is that for things that I have seen AI learn so far, the distance from the real thing is intuitively small. If AI learns my values as well as it learns what faces look like, it seems plausible that it carries them out better than I do.
As minor additional evidence here, I don’t know how to describe any slight differences in utility functions that are catastrophic. Talking concretely, what does a utility function look like that is so close to a human utility function that an AI system has it after a bunch of training, but which is an absolute disaster? Are we talking about the scenario where the AI values a slightly different concept of justice, or values satisfaction a smidgen more relative to joy than it should? And then that’s a moral disaster because it is wrought across the cosmos? Or is it that it looks at all of our inaction and thinks we want stuff to be maintained very similar to how it is now, so crushes any efforts to improve things?
What it might look like if this gap matters: when we try to train AI systems to care about what specific humans care about, they usually pretty much do, as far as we can tell. We basically get what we trained for. For instance, it is hard to distinguish them from the human in question. (It is still important to actually do this training, rather than making AI systems not trained to have human values.)
Maybe value isn’t fragile
Eliezer argued that value is fragile, via examples of ‘just one thing’ that you can leave out of a utility function, and end up with something very far away from what humans want. For instance, if you leave out ‘boredom’ then he thinks the preferred future might look like repeating the same otherwise perfect moment again and again. (His argument is perhaps longer—that post says there is a lot of important background, though the bits mentioned don’t sound relevant to my disagreement.) This sounds to me like ‘value is not resilient to having components of it moved to zero’, which is a weird usage of ‘fragile’, and in particular, doesn’t seem to imply much about smaller perturbations. And smaller perturbations seem like the relevant thing with AI systems trained on a bunch of data to mimic something.
You could very analogously say ‘human faces are fragile’ because if you just leave out the nose it suddenly doesn’t look like a typical human face at all. Sure, but is that the kind of error you get when you try to train ML systems to mimic human faces? Almost none of the faces on thispersondoesnotexist.com are blatantly morphologically unusual in any way, let alone noseless. Admittedly one time I saw someone whose face was neon green goo, but I’m guessing you can get the rate of that down pretty low if you care about it.
Eight examples, no cherry-picking:

Skipping the nose is the kind of mistake you make if you are a child drawing a face from memory. Skipping ‘boredom’ is the kind of mistake you make if you are a person trying to write down human values from memory. My guess is that this seemed closer to the plan in 2009 when that post was written, and that people cached the takeaway and haven’t updated it for deep learning which can learn what faces look like better than you can.
What it might look like if this gap matters: there is a large region ‘around’ my values in value space that is also pretty good according to me. AI easily lands within that space, and eventually creates some world that is about as good as the best possible utopia, according to me. There aren’t a lot of really crazy and terrible value systems adjacent to my values.
Short-term goals
Utility maximization really only incentivises drastically altering the universe if one’s utility function places a high enough value on very temporally distant outcomes relative to near ones. That is, long term goals are needed for danger. A person who cares most about winning the timed chess game in front of them should not spend time accruing resources to invest in better chess-playing.
AI systems could have long-term goals via people intentionally training them to do so, or via long-term goals naturally arising from systems not trained so.
Humans seem to discount the future a lot in their usual decision-making (they have goals years in advance but rarely a hundred years) so the economic incentive to train AI to have very long term goals might be limited.
It’s not clear that training for relatively short term goals naturally produces creatures with very long term goals, though it might.
Thus if AI systems fail to have value systems relatively similar to human values, it is not clear that many will have the long time horizons needed to motivate taking over the universe.
What it might look like if this gap matters: the world is full of agents who care about relatively near-term issues, and are helpful to that end, and have no incentive to make long-term large scale schemes. Reminiscent of the current world, but with cleverer short-termism.
C. Contra “superhuman AI would be sufficiently superior to humans to overpower humanity”
Human success isn’t from individual intelligence
The argument claims (or assumes) that surpassing ‘human-level’ intelligence (i.e. the mental capacities of an individual human) is the relevant bar for matching the power-gaining capacity of humans, such that passing this bar in individual intellect means outcompeting humans in general in terms of power (argument III.2), if not being able to immediately destroy them all outright (argument III.1.). In a similar vein, introductions to AI risk often start by saying that humanity has triumphed over the other species because it is more intelligent, as a lead in to saying that if we make something more intelligent still, it will inexorably triumph over humanity.
This hypothesis about the provenance of human triumph seems wrong. Intellect surely helps, but humans look to be powerful largely because they share their meager intellectual discoveries with one another and consequently save them up over time5. You can see this starkly by comparing the material situation of Alice, a genius living in the stone age, and Bob, an average person living in 21st Century America. Alice might struggle all day to get a pot of water, while Bob might be able to summon all manner of delicious drinks from across the oceans, along with furniture, electronics, information, etc. Much of Bob’s power probably did flow from the application of intelligence, but not Bob’s individual intelligence. Alice’s intelligence, and that of those who came between them.
Bob’s greater power isn’t directly just from the knowledge and artifacts Bob inherits from other humans. He also seems to be helped for instance by much better coordination: both from a larger number people coordinating together, and from better infrastructure for that coordination (e.g. for Alice the height of coordination might be an occasional big multi-tribe meeting with trade, and for Bob it includes global instant messaging and banking systems and the Internet). One might attribute all of this ultimately to innovation, and thus to intelligence and communication, or not. I think it’s not important to sort out here, as long as it’s clear that individual intelligence isn’t the source of power.
It could still be that with a given bounty of shared knowledge (e.g. within a given society), intelligence grants huge advantages. But even that doesn’t look true here: 21st Century geniuses live basically like 21st Century people of average intelligence, give or take6.
Why does this matter? Well for one thing, if you make AI which is merely as smart as a human, you shouldn’t then expect it to do that much better than a genius living in the stone age. That’s what human-level intelligence gets you: nearly nothing. A piece of rope after millions of lifetimes. Humans without their culture are much like other animals.
To wield the control-over-the-world of a genius living in the 21st Century, the human-level AI would seem to need something like the other benefits that the 21st century genius gets from their situation in connection with a society.
One such thing is access to humanity’s shared stock of hard-won information. AI systems plausibly do have this, if they can get most of what is relevant by reading the internet. This isn’t obvious: people also inherit information from society through copying habits and customs, learning directly from other people, and receiving artifacts with implicit information (for instance, a factory allows whoever owns the factory to make use of intellectual work that was done by the people who built the factory, but that information may not available explicitly even for the owner of the factory, let alone to readers on the internet). These sources of information seem likely to also be available to AI systems though, at least if they are afforded the same options as humans.
My best guess is that AI systems easily do better than humans on extracting information from humanity’s stockpile, and on coordinating, and so on this account are probably in an even better position to compete with humans than one might think on the individual intelligence model, but that is a guess. In that case perhaps this misunderstanding makes little difference to the outcomes of the argument. However it seems at least a bit more complicated.
Suppose that AI systems can have access to all information humans can have access to. The power the 21st century person gains from their society is modulated by their role in society, and relationships, and rights, and the affordances society allows them as a result. Their power will vary enormously depending on whether they are employed, or listened to, or paid, or a citizen, or the president. If AI systems’ power stems substantially from interacting with society, then their power will also depend on affordances granted, and humans may choose not to grant them many affordances (see section ‘Intelligence may not be an overwhelming advantage’ for more discussion).
However suppose that your new genius AI system is also treated with all privilege. The next way that this alternate model matters is that if most of what is good in a person’s life is determined by the society they are part of, and their own labor is just buying them a tiny piece of that inheritance, then if they are for instance twice as smart as any other human, they don’t get to use technology that it twice as good. They just get a larger piece of that same shared technological bounty purchasable by anyone. Because each individual person is adding essentially nothing in terms of technology, so twice that is still basically nothing.
In contrast, I think people are often imagining that a single entity somewhat smarter than a human will be able to quickly use technologies that are somewhat better than current human technologies. This seems to be mistaking the actions of a human and the actions of a human society. If a hundred thousand people sometimes get together for a few years and make fantastic new weapons, you should not expect an entity somewhat smarter than a person to make even better weapons. That’s off by a factor of about a hundred thousand.
There might be places you can get far ahead of humanity by being better than a single human—it depends how much accomplishments depend on the few most capable humans in the field, and how few people are working on the problem7. But for instance the Manhattan Project took a hundred thousand people several years, and von Neumann (a mythically smart scientist) joining the project did not reduce it to an afternoon. Plausibly to me, some specific people being on the project caused it to not take twice as many person-years, though the plausible candidates here seem to be more in the business of running things than doing science directly (though that also presumably involves intelligence). But even if you are an ambitious somewhat superhuman intelligence, the influence available to you seems to plausibly be limited to making a large dent in the effort required for some particular research endeavor, not single-handedly outmoding humans across many research endeavors.
This is all reason to doubt that a small number of superhuman intelligences will rapidly take over or destroy the world (as in III.i.). This doesn’t preclude a set of AI systems that are together more capable than a large number of people from making great progress. However some related issues seem to make that less likely.
Another implication of this model is that if most human power comes from buying access to society’s shared power, i.e. interacting with the economy, you should expect intellectual labor by AI systems to usually be sold, rather than for instance put toward a private stock of knowledge. This means the intellectual outputs are mostly going to society, and the main source of potential power to an AI system is the wages received (which may allow it to gain power in the long run). However it seems quite plausible that AI systems at this stage will generally not receive wages, since they presumably do not need them to be motivated to do the work they were trained for. It also seems plausible that they would be owned and run by humans. This would seem to not involve any transfer of power to that AI system, except insofar as its intellectual outputs benefit it (e.g. if it is writing advertising material, maybe it doesn’t get paid for that, but if it can write material that slightly furthers its own goals in the world while also fulfilling the advertising requirements, then it sneaked in some influence.)
If there is AI which is moderately more competent than humans, but not sufficiently more competent to take over the world, then it is likely to contribute to this stock of knowledge and affordances shared with humans. There is no reason to expect it to build a separate competing stock, any more than there is reason for a current human household to try to build a separate competing stock rather than sell their labor to others in the economy.
In summary:

Functional connection with a large community of other intelligences in the past and present is probably a much bigger factor in the success of humans as a species or individual humans than is individual intelligence.
Thus this also seems more likely to be important for AI success than individual intelligence. This is contrary to a usual argument for AI superiority, but probably leaves AI systems at least as likely to outperform humans, since superhuman AI is probably superhumanly good at taking in information and coordinating.
However it is not obvious that AI systems will have the same access to society’s accumulated information e.g. if there is information which humans learn from living in society, rather than from reading the internet.
And it seems an open question whether AI systems are given the same affordances in society as humans, which also seem important to making use of the accrued bounty of power over the world that humans have. For instance, if they are not granted the same legal rights as humans, they may be at a disadvantage in doing trade or engaging in politics or accruing power.
The fruits of greater intelligence for an entity will probably not look like society-level accomplishments unless it is a society-scale entity
The route to influence with smaller fruits probably by default looks like participating in the economy rather than trying to build a private stock of knowledge.
If the resources from participating in the economy accrue to the owners of AI systems, not to the systems themselves, then there is less reason to expect the systems to accrue power incrementally, and they are at a severe disadvantage relative to humans.

Overall these are reasons to expect AI systems with around human-level cognitive performance to not destroy the world immediately, and to not amass power as easily as one might imagine.
What it might look like if this gap matters: If AI systems are somewhat superhuman, then they do impressive cognitive work, and each contributes to technology more than the best human geniuses, but not more than the whole of society, and not enough to materially improve their own affordances. They don’t gain power rapidly because they are disadvantaged in other ways, e.g. by lack of information, lack of rights, lack of access to positions of power. Their work is sold and used by many actors, and the proceeds go to their human owners. AI systems do not generally end up with access to masses of technology that others do not have access to, and nor do they have private fortunes. In the long run, as they become more powerful, they might take power if other aspects of the situation don’t change.
AI agents may not be radically superior to combinations of humans and non-agentic machines
‘Human level capability’ is a moving target. For comparing the competence of advanced AI systems to humans, the relevant comparison is with humans who have state-of-the-art AI and other tools. For instance, the human capacity to make art quickly has recently been improved by a variety of AI art systems. If there were now an agentic AI system that made art, it would make art much faster than a human of 2015, but perhaps hardly faster than a human of late 2022. If humans continually have access to tool versions of AI capabilities, it is not clear that agentic AI systems must ever have an overwhelmingly large capability advantage for important tasks (though they might).
(This is not an argument that humans might be better than AI systems, but rather: if the gap in capability is smaller, then the pressure for AI systems to accrue power is less and thus loss of human control is slower and easier to mitigate entirely through other forces, such as subsidizing human involvement or disadvantaging AI systems in the economy.)
Some advantages of being an agentic AI system vs. a human with a tool AI system seem to be:

There might just not be an equivalent tool system, for instance if it is impossible to train systems without producing emergent agents.
When every part of a process takes into account the final goal, this should make the choices within the task more apt for the final goal (and agents know their final goal, whereas tools carrying out parts of a larger problem do not).
For humans, the interface for using a capability of one’s mind tends to be smoother than the interface for using a tool. For instance a person who can do fast mental multiplication can do this more smoothly and use it more often than a person who needs to get out a calculator. This seems likely to persist.

1 and 2 may or may not matter much. 3 matters more for brief, fast, unimportant tasks. For instance, consider again people who can do mental calculations better than others. My guess is that this advantages them at using Fermi estimates in their lives and buying cheaper groceries, but does not make them materially better at making large financial choices well. For a one-off large financial choice, the effort of getting out a calculator is worth it and the delay is very short compared to the length of the activity. The same seems likely true of humans with tools vs. agentic AI with the same capacities integrated into their minds. Conceivably the gap between humans with tools and goal-directed AI is small for large, important tasks.
What it might look like if this gap matters: agentic AI systems have substantial advantages over humans with tools at some tasks like rapid interaction with humans, and responding to rapidly evolving strategic situations.  One-off large important tasks such as advanced science are mostly done by tool AI.
Trust
If goal-directed AI systems are only mildly more competent than some combination of tool systems and humans (as suggested by considerations in the last two sections), we still might expect AI systems to out-compete humans, just more slowly. However AI systems have one serious disadvantage as employees of humans: they are intrinsically untrustworthy, while we don’t understand them well enough to be clear on what their values are or how they will behave in any given case. Even if they did perform as well as humans at some task, if humans can’t be certain of that, then there is reason to disprefer using them. This can be thought of as two problems: firstly, slightly misaligned systems are less valuable because they genuinely do the thing you want less well, and secondly, even if they were not misaligned, if humans can’t know that (because we have no good way to verify the alignment of AI systems) then it is costly in expectation to use them. (This is only a further force acting against the supremacy of AI systems—they might still be powerful enough that using them is enough of an advantage that it is worth taking the hit on trustworthiness.)
What it might look like if this gap matters: in places where goal-directed AI systems are not typically hugely better than some combination of less goal-directed systems and humans, the job is often given to the latter if trustworthiness matters.
Headroom
For AI to vastly surpass human performance at a task, there needs to be ample room for improvement above human level. For some tasks, there is not—tic-tac-toe is a classic example. It is not clear how close humans (or technologically aided humans) are from the limits to competence in the particular domains that will matter. It is to my knowledge an open question how much ‘headroom’ there is. My guess is a lot, but it isn’t obvious.
How much headroom there is varies by task. Categories of task for which there appears to be little headroom:

Tasks where we know what the best performance looks like, and humans can get close to it. For instance, machines cannot win more often than the best humans at Tic-tac-toe (playing within the rules) or solve Rubik’s cubes much more reliably, or extracting calories from fuel
Tasks where humans are already be reaping most of the value—for instance, perhaps most of the value of forks is in having a handle with prongs attached to the end, and while humans continue to design slightly better ones, and machines might be able to add marginal value to that project more than twice as fast as the human designers, they cannot perform twice as well in terms of the value of each fork, because forks are already 95% as good as they can be.
Better performance is quickly intractable. For instance, we know that for tasks in particular complexity classes, there are computational limits to how well one can perform across the board. Or for chaotic systems, there can be limits to predictability. (That is, tasks might lack headroom not because they are simple, but because they are complex. E.g. AI probably can’t predict the weather much further out than humans.)

Categories of task where a lot of headroom seems likely:

Competitive tasks where the value of a certain level of performance depends on whether one is better or worse than one’s opponent, so that the marginal value of more performance doesn’t hit diminishing returns, as long as your opponent keeps competing and taking back what you just won. Though in one way this is like having little headroom: there’s no more value to be had—the game is zero sum. And while there might often be a lot of value to be gained by doing a bit better on the margin, still if all sides can invest, then nobody will end up better off than they were. So whether this seems more like high or low headroom depends on what we are asking exactly. Here we are asking if AI systems can do much better than humans: in a zero sum contest like this, they likely can in the sense that they can beat humans, but not in the sense of reaping anything more from the situation than the humans ever got.
Tasks where it is twice as good to do the same task twice as fast, and where speed is bottlenecked on thinking time.
Tasks where there is reason to think that optimal performance is radically better than we have seen. For instance, perhaps we can estimate how high Chess Elo rankings must go before reaching perfection by reasoning theoretically about the game, and perhaps it is very high (I don’t know).
Tasks where humans appear to use very inefficient methods. For instance, it was perhaps predictable before calculators that they would be able to do mathematics much faster than humans, because humans can only keep a small number of digits in their heads, which doesn’t seem like an intrinsically hard problem. Similarly, I hear humans often use mental machinery designed for one mental activity for fairly different ones, through analogy.8 For instance, when I think about macroeconomics, I seem to be basically using my intuitions for dealing with water. When I do mathematics in general, I think I’m probably using my mental capacities for imagining physical objects.

What it might look like if this gap matters: many challenges in today’s world remain challenging for AI. Human behavior is not readily predictable or manipulable very far beyond what we have explored, only slightly more complicated schemes are feasible before the world’s uncertainties overwhelm planning; much better ads are soon met by much better immune responses; much better commercial decision-making ekes out some additional value across the board but most products were already fulfilling a lot of their potential; incredible virtual prosecutors meet incredible virtual defense attorneys and everything is as it was; there are a few rounds of attack-and-defense in various corporate strategies before a new equilibrium with broad recognition of those possibilities; conflicts and ‘social issues’ remain mostly intractable. There is a brief golden age of science before the newly low-hanging fruit are again plucked and it is only lightning fast in areas where thinking was the main bottleneck, e.g. not in medicine.
Intelligence may not be an overwhelming advantage
Intelligence is helpful for accruing power and resources, all things equal, but many other things are helpful too. For instance money, social standing, allies, evident trustworthiness, not being discriminated against (this was slightly discussed in section ‘Human success isn’t from individual intelligence’). AI systems are not guaranteed to have those in abundance. The argument assumes that any difference in intelligence in particular will eventually win out over any differences in other initial resources. I don’t know of reason to think that.
Empirical evidence does not seem to support the idea that cognitive ability is a large factor in success. Situations where one entity is much smarter or more broadly mentally competent than other entities regularly occur without the smarter one taking control over the other:

Species exist with all levels of intelligence. Elephants have not in any sense won over gnats; they do not rule gnats; they do not have obviously more control than gnats over the environment.
Competence does not seem to aggressively overwhelm other advantages in humans:

Looking at the world, intuitively the big discrepancies in power are not seemingly about intelligence.
IQ 130 humans are apparently expected to earn very roughly $6000-$18,500 per year more than average IQ humans.
Elected representatives are apparently smarter on average, but it is a slightly shifted curve, not a radically difference.
MENSA isn’t a major force in the world.
Many places where people see huge success through being cognitively able are ones where they show off their intelligence to impress people, rather than actually using it for decision-making. For instance, writers, actors, song-writers, comedians, all sometimes become very successful through cognitive skills. Whereas scientists, engineers and authors of software use cognitive skills to make choices about the world, and less often become extremely rich and famous, say. If intelligence were that useful for strategic action, it seems like using it for that would be at least as powerful as showing it off. But maybe this is just an accident of which fields have winner-takes-all type dynamics.
If we look at people who evidently have good cognitive abilities given their intellectual output, their personal lives are not obviously drastically more successful, anecdotally.
One might counter-counter-argue that humans are very similar to one another in capability, so even if intelligence matters much more than other traits, you won’t see that by looking at  the near-identical humans. This does not seem to be true. Often at least, the difference in performance between mediocre human performance and top level human performance is large, relative to the space below, iirc. For instance, in chess, the Elo difference between the best and worst players is about 2000, whereas the difference between the amateur play and random play is maybe 400-2800 (if you accept Chess StackExchange guesses as a reasonable proxy for the truth here). And in terms of AI progress, amateur human play was reached in the 50s, roughly when research began, and world champion level play was reached in 1997.

And theoretically I don’t know why one would expect greater intelligence to win out over other advantages over time.  There are actually two questionable theories here: 1) Charlotte having more overall control than David at time 0 means that Charlotte will tend to have an even greater share of control at time 1. And, 2) Charlotte having more intelligence than David at time 0 means that Charlotte will have a greater share of control at time 1 even if Bob has more overall control (i.e. more of other resources) at time 1.
What it might look like if this gap matters: there are many AI systems around, and they strive for various things. They don’t hold property, or vote, or get a weight in almost anyone’s decisions, or get paid, and are generally treated with suspicion. These things on net keep them from gaining very much power. They are very persuasive speakers however and we can’t stop them from communicating, so there is a constant risk of people willingly handing them power, in response to their moving claims that they are an oppressed minority who suffer. The main thing stopping them from winning is that their position as psychopaths bent on taking power for incredibly pointless ends is widely understood.
Unclear that many goals realistically incentivise taking over the universe
I have some goals. For instance, I want some good romance. My guess is that trying to take over the universe isn’t the best way to achieve this goal. The same goes for a lot of my goals, it seems to me. Possibly I’m in error, but I spend a lot of time pursuing goals, and very little of it trying to take over the universe. Whether a particular goal is best forwarded by trying to take over the universe as a substep seems like a quantitative empirical question, to which the answer is virtually always ‘not remotely’. Don’t get me wrong: all of these goals involve some interest in taking over the universe. All things equal, if I could take over the universe for free, I do think it would help in my romantic pursuits. But taking over the universe is not free. It’s actually super duper duper expensive and hard. So for most goals arising, it doesn’t bear considering. The idea of taking over the universe as a substep is entirely laughable for almost any human goal.
So why do we think that AI goals are different? I think the thought is that it’s radically easier for AI systems to take over the world, because all they have to do is to annihilate humanity, and they are way better positioned to do that than I am, and also better positioned to survive the death of human civilization than I am. I agree that it is likely easier, but how much easier? So much easier to take it from ‘laughably unhelpful’ to ‘obviously always the best move’? This is another quantitative empirical question.
What it might look like if this gap matters: Superintelligent AI systems pursue their goals. Often they achieve them fairly well. This is somewhat contrary to ideal human thriving, but not lethal. For instance, some AI systems are trying to maximize Amazon’s market share, within broad legality. Everyone buys truly incredible amounts of stuff from Amazon, and people often wonder if it is too much stuff. At no point does attempting to murder all humans seem like the best strategy for this.
Quantity of new cognitive labor is an empirical question, not addressed
Whether some set of AI systems can take over the world with their new intelligence probably depends how much total cognitive labor they represent. For instance, if they are in total slightly more capable than von Neumann, they probably can’t take over the world. If they are together as capable (in some sense) as a million 21st Century human civilizations, then they probably can (at least in the 21st Century).
It also matters how much of that is goal-directed at all, and highly intelligent, and how much of that is directed at achieving the AI systems’ own goals rather than those we intended them for, and how much of that is directed at taking over the world.
If we continued to build hardware, presumably at some point AI systems would account for most of the cognitive labor in the world. But if there is first an extended period of more minimal advanced AI presence, that would probably prevent an immediate death outcome, and improve humanity’s prospects for controlling a slow-moving AI power grab.
What it might look like if this gap matters: when advanced AI is developed, there is a lot of new cognitive labor in the world, but it is a minuscule fraction of all of the cognitive labor in the world. A large part of it is not goal-directed at all, and of that, most of the new AI thought is applied to tasks it was intended for. Thus what part of it is spent on scheming to grab power for AI systems is too small to grab much power quickly. The amount of AI cognitive labor grows fast over time, and in several decades it is most of the cognitive labor, but humanity has had extensive experience dealing with its power grabbing.
Speed of intelligence growth is ambiguous
The idea that a superhuman AI would be able to rapidly destroy the world seems prima facie unlikely, since no other entity has ever done that. Two common broad arguments for it:

There will be a feedback loop in which intelligent AI makes more intelligent AI repeatedly until AI is very intelligent.
Very small differences in brains seem to correspond to very large differences in performance, based on observing humans and other apes. Thus any movement past human-level will take us to unimaginably superhuman level.

These both seem questionable.

Feedback loops can happen at very different rates. Identifying a feedback loop empirically does not signify an explosion of whatever you are looking at. For instance, technology is already helping improve technology. To get to a confident conclusion of doom, you need evidence that the feedback loop is fast.
It does not seem clear that small improvements in brains lead to large changes in intelligence in general, or will do on the relevant margin. Small differences between humans and other primates might include those helpful for communication (see Section ‘Human success isn’t from individual intelligence’), which do not seem relevant here. If there were a particularly powerful cognitive development between chimps and humans, it is unclear that AI researchers find that same insight at the same point in the process (rather than at some other time).

A large number of other arguments have been posed for expecting very fast growth in intelligence at around human level. I previously made a list of them with counterarguments, though none seemed very compelling. Overall, I don’t know of strong reason to expect very fast growth in AI capabilities at around human-level AI performance, though I hear such arguments might exist.
What it would look like if this gap mattered: AI systems would at some point perform at around human level at various tasks, and would contribute to AI research, along with everything else. This would contribute to progress to an extent familiar from other technological progress feedback, and would not e.g. lead to a superintelligent AI system in minutes.
Key concepts are vague
Concepts such as ‘control’, ‘power’, and ‘alignment with human values’ all seem vague. ‘Control’ is not zero sum (as seemingly assumed) and is somewhat hard to pin down, I claim. What an ‘aligned’ entity is exactly seems to be contentious in the AI safety community, but I don’t know the details. My guess is that upon further probing, these conceptual issues are resolvable in a way that doesn’t endanger the argument, but I don’t know. I’m not going to go into this here.
What it might look like if this gap matters: upon thinking more, we realize that our concerns were confused. Things go fine with AI in ways that seem obvious in retrospect. This might look like it did for people concerned about the ‘population bomb’ or as it did for me in some of my youthful concerns about sustainability: there was a compelling abstract argument for a problem, and the reality didn’t fit the abstractions well enough to play out as predicted.
D. Contra the whole argument
The argument overall proves too much about corporations
Here is the argument again, but modified to be about corporations. A couple of pieces don’t carry over, but they don’t seem integral.
I. Any given corporation is likely to be ‘goal-directed’
Reasons to expect this:

Goal-directed behavior is likely to be valuable in corporations, e.g. economically
Goal-directed entities may tend to arise from machine learning training processes not intending to create them (at least via the methods that are likely to be used).
‘Coherence arguments’ may imply that systems with some goal-directedness will become more strongly goal-directed over time.

II. If goal-directed superhuman corporations are built, their desired outcomes will probably be about as bad as an empty universe by human lights
Reasons to expect this:

Finding useful goals that aren’t extinction-level bad appears to be hard: we don’t have a way to usefully point at human goals, and divergences from human goals seem likely to produce goals that are in intense conflict with human goals, due to a) most goals producing convergent incentives for controlling everything, and b) value being ‘fragile’, such that an entity with ‘similar’ values will generally create a future of virtually no value.
Finding goals that are extinction-level bad and temporarily useful appears to be easy: for example, corporations with the sole objective ‘maximize company revenue’ might profit for a time before gathering the influence and wherewithal to pursue the goal in ways that blatantly harm society.
Even if humanity found acceptable goals, giving a corporation any specific goals appears to be hard. We don’t know of any procedure to do it, and we have theoretical reasons to expect that AI systems produced through machine learning training will generally end up with goals other than those that they were trained according to. Randomly aberrant goals resulting are probably extinction-level bad, for reasons described in II.1 above.

III. If most goal-directed corporations have bad goals, the future will very likely be bad
That is, a set of ill-motivated goal-directed corporations, of a scale likely to occur, would be capable of taking control of the future from humans. This is supported by at least one of the following being true:

A corporation would destroy humanity rapidly. This may be via ultra-powerful capabilities at e.g. technology design and strategic scheming, or through gaining such powers in an ‘intelligence explosion‘ (self-improvement cycle). Either of those things may happen either through exceptional heights of intelligence being reached or through highly destructive ideas being available to minds only mildly beyond our own.
Superhuman AI would gradually come to control the future via accruing power and resources. Power and resources would be more available to the corporation than to humans on average, because of the corporation having far greater intelligence.

This argument does point at real issues with corporations, but we do not generally consider such issues existentially deadly.
One might argue that there are defeating reasons that corporations do not destroy the world: they are made of humans so can be somewhat reined in; they are not smart enough; they are not coherent enough. But in that case, the original argument needs to make reference to these things, so that they apply to one and not the other.
What it might look like if this counterargument matters: something like the current world. There are large and powerful systems doing things vastly beyond the ability of individual humans, and acting in a definitively goal-directed way. We have a vague understanding of their goals, and do not assume that they are coherent. Their goals are clearly not aligned with human goals, but they have enough overlap that many people are broadly in favor of their existence. They seek power. This all causes some problems, but problems within the power of humans and other organized human groups to keep under control, for some definition of ‘under control’.
Conclusion
I think there are quite a few gaps in the argument, as I understand it. My current guess (prior to reviewing other arguments and integrating things carefully) is that enough uncertainties might resolve in the dangerous directions that existential risk from AI is a reasonable concern. I don’t at present though see how one would come to think it was overwhelmingly likely.

Notes


Outcomes of inducement prizes

Published 29 August 2022; last updated 30 August 2022
This project is incomplete, contains information that may change over time, and may or may not be updated in the future
This is a dataset of prizes we could find for incentivizing progress toward a specific technical or intellectual goal.
Details
Inducement prizes are created to encourage effort toward solving a specific problem, usually by offering a large monetary award for accomplishing a goal according to pre-specified criteria. Inducement prizes are distinct from those recognizing past achievements, like the Fields Medal or Nobel Prize.1
Methodology
We spent approximately 10-12 hours researching prizes for technological and intellectual progress, such as crossing the ocean or proving a mathematical theorem. For inclusion in our list, a prize needed to be:
Offered for accomplishing a well-specified goal that is reached primarily through intellectual or technological progress.Announced before the goal had been achieved, with a specific prize amount.
For each prize, we tried to answer several questions:
What are the basic parameters, such as the prize amount, the conditions for winning, the year it was announced, and the financial sponsor?Was the prize collected, and if so, by whom?Was the prize’s goal achieved in the intended time frame and for the original prize amount?Were there other notable consequences of the prize, such as increased interest in relevant industries or a change in public perception of the party offering the prize?
We were unable to answer every question for every prize in the time allotted to the project, nor were we able to investigate every prize that appeared to match our criteria.
Dataset
This table contains a condensed version of our dataset. The full dataset with sources can be found in this Google Sheet.
Prize NameOrganization or Financial SponsorYearsPrize Amount (2022 $M)OutcomeProblem AreaWinnerXPrize (6 prizes)XPrize1996-20255 – 1005 ongoing 1 awardedVariousScaled CompositesNASA Green Flight ChallengeNASA2009 -20111.80AwardedAviationPipistrel-USADARPA Grand ChallengeDARPA2004-20053.10AwardedAutonomous vehiclesStanford Racing TeamMillenium ProblemsClay Mathematics Institute2000-20101.00 each(1.7 in 2000)6 ongoing 1 awardedMathematicsGrigoriy PerelmanKremer PrizeHenry Kremer1959-19770.52AwardedAviationDr. Paul MacCreadyOrteig PrizeRaymond Orteig1919-19270.44AwardedAviationCharles LindberghDaily Mail PrizesDaily Mail1906-1930.015 – 1.5OtherAviation(Various)Longitude PrizeBritish Government1714-17355.72AwardedNavigation at seaJohn Harrison
Primary author: Elizabeth SantosAdditional writing and research: Rick Korzekwa
Notes

Outcomes of inducement prizes


Will Superhuman AI be created?

Published 6 Aug 2022
This page may represent little of what is known on the topic. It is incomplete, under active work and may be updated soon.
Superhuman AI appears to be very likely to be created at some point.
Details
Let ‘superhuman’ AI be a set of AI systems that together achieve human-level performance across virtually all tasks (i.e. HLMI) and substantially surpass human-level performance on some tasks.
Arguments
A. Superhuman AI is very likely to be physically possible
1. Human brains prove that it is physically possible to create human-level intelligence
A single human brain is not an existence proof of human-level intelligence according to our definition, because no specific human brain is able to perform at the level of any human brain, on all tasks. Given only the observation of the existence of human brains, it could conceivably be impossible to build a machine that performs any task at the level of any chosen human brain at the total cost of a single human brain. For instance, if each brain could only hold the information required to specialize in one career, then a machine that could do any career would need to store much more information than a brain, and thus could be more expensive.
The entire population of human brains together could be said to perform at ‘human-level’, if the cost of doing a single task is considered to be the cost of the single person’s labor used for that task, rather than the cost of maintaining the entire human race. This seems like a reasonable accounting for the present purposes. Thus, the entire collection of human brains demonstrates that it is physically possible to have a system which can do any task as well as the most proficient human, and can do marginal tasks at the cost of human labor (even if the cost of maintaining the entire system would be much higher, were it not spread between many tasks).
2. We know of no reason to expect that human brains are near the limits of possible intelligence
Human brains do appear to be near the limits of performance for some specific tasks. For instance, humans can play tic-tac-toe perfectly. Also for many tasks, human performance reaps a lot of the value potentially available, so it is impossible to perform much better in terms of value (e.g. selecting lunch from a menu, making a booking, recording a phone number).
However, many tasks do not appear to be like this (e.g. winning at Go), and even for the above mentioned tasks, there is room to carry out the task substantially faster or more cheaply than a human does. Thus there appears to be room for substantially better-than-human performance on a wide range of tasks, though we have not seen a careful accounting of this.
3. Artificial minds appear to have some intrinsic advantages over human minds
a) Human brains developed under constraints that would not apply to artificial brains. In particular, energy use was a more important cost, and there were reproductive constraints to human head size.
b) Machines appear to have huge potential performance advantages over biological systems on some fronts. Carlsmith summarizes Bostrom1:
Thus, as Bostrom (2014, Chapter 3) discusses, where neurons can fire a maximum of hundreds of Hz, the clock speed of modern computers can reach some 2 Ghz — ~ten million times faster. Where action potentials travel at some hundreds of m/s, optical communication can take place at 300,000,000 m/s — ~ a million times faster. Where brain size and neuron count are limited by cranial volume, metabolic constraints, and other factors, supercomputers can be the size of warehouses. And artificial systems need not suffer, either, from the brain’s constraints with respect to memory and component reliability, input/output bandwidth, tiring after hours, degrading after decades, storing its own repair mechanisms and blueprints inside itself, and so forth. Artificial systems can also be edited and duplicated much more easily than brains, and information can be more easily transferred between them.
4. Superhuman AI is very likely to be physically possible (from 1-3)
The human species exists (1). There seems little reason to think that no system could perform tasks substantially better (2), and multiple moderately strong reasons to think that more capable systems are possible (3). Thus it seems very likely that more capable systems are possible.
5. The likely physical possibility of superhuman AI minds strongly suggests the physical feasibility of creating such minds
A superhuman mind is a physically possible object (4). However, that a physical configuration is possible does not imply that bringing about such a configuration intentionally can be feasible in practice. For an example of the difference, a waterfall whose water is in exactly the configuration as that of the Niagara Falls for the last ten minutes is physically possible (the Niagara Falls just did it), yet bringing this about again intentionally may remain intractable forever.
In fact we know that human brains specifically are not only physically possible, but feasible for humans to create. However this is in the form of biological reproduction, and does not appear to straightforwardly imply that humans can create arbitrary different systems with at least the intelligence of humans. That is, human creation of human brains does not obviously imply that it is possible for humans to intentionally create human-level intelligence that isn’t a human brain.
However it seems strongly suggestive. If a physical configuration is possible, natural reasons it might be intractable to bring about are a) that it is computationally difficult to transform the given reference to an actionable description of the physical state required (e.g. ‘Niagara Falls ten minutes ago’ points at a particular configuration of water, but not in a way that is easily convertible to a detailed specification of the locations of that water2), and b) the actionable description is hard to bring about. For instance, it might require minute manipulation of particles beyond what is feasible today, either to get the required degree of specificity, or to avoid chaotic dynamics taking the system away from the desired state even at a macroscopic level (e.g. even if your description of the starting state of the waterfall is fairly detailed, it will quickly diverge farther from the real waterfall.)
These issues don’t appear to apply to creating superhuman intelligence, so in the absence of other evident defeaters, its physical possibility seems to strongly suggest its physical feasibility.
6. Contemporary AI systems exhibit qualitatively similar capabilities to human minds, suggesting modified versions of similar processes would give rise to capabilities matching human minds.
That is, given that current AI techniques create systems that do a lot of human-like tasks at some level of performance, e.g. recognize images and write human-like language, it would be somewhat surprising if getting to human level performance on these tasks required such starkly different methods as to be impossible.
7. Superhuman AI systems will very likely be physically feasible to create (from 4-6)
Superhuman AI systems are probably physically possible (4), and this suggests that they are feasible to create (5). Separately, presently feasible AI systems exhibit qualitatively similar behavior to human minds (6), weakly suggesting that systems exhibiting similar behavior at a higher level of performance will also be  feasible to create.
B. If feasible, superhuman AI will very likely be created
8. Superhuman AI would appear to be very economically valuable, given its potential to do most human work better or more cheaply
Whenever such minds become feasible, by stipulation they will be superior in ways to existing sources of cognitive labor, or those sources would have already constituted superhuman AI. Unless the gap is quite small between human-level AI and the best feasible superhuman AI (which seems unlikely given the large potential room for improvement over human minds), the economic value from superhuman AI should be at least at the scale of the global labor market.
9. It appears there will be large incentives to create such systems (from 8)
That a situation would make large amounts of economic value available does not imply that any individual has an incentive to make that situation happen, because the value may not accrue to the possible decision maker. In this case however, substantial parts of the economic value created by superhuman AI systems appear likely to be captured by their creators, by analogy to other commercial software.
These economic incentives may not be the only substantial incentives in play. Creating such systems could incur social or legal consequences which could negate the positive incentives. Thus this step is relatively uncertain.
10. Superhuman AI seems likely to be created
Given that creation of a type of machine is physically feasible (7) and strongly incentivized (9), it seems likely that it will be created.
Notes

Will Superhuman AI be created?


List of sources arguing against existential risk from AI

Published 6 Aug 2022
This page is incomplete, under active work and may be updated soon.
This is a bibliography of pieces arguing against the idea that AI poses an existential risk.
List
Cegłowski, Maciej. “Superintelligence: The Idea That Eats Smart People.” Idle Words (blog). Accessed December 9, 2021. https://idlewords.com/talks/superintelligence.htm.
Garfinkel, Ben, and Lempel, Howie. “How Sure Are We about This AI Stuff?” 80,000 Hours. Accessed September 16, 2020. https://80000hours.org/podcast/episodes/ben-garfinkel-classic-ai-risk-arguments/.
Garfinkel, Ben. How Sure Are We about This AI Stuff? (talk) | EA Global: London 2018, 2019. https://www.youtube.com/watch?v=E8PGcoLDjVk. Also in blog form: https://ea.greaterwrong.com/posts/9sBAW3qKppnoG3QPq/ben-garfinkel-how-sure-are-we-about-this-ai-stuff
LeCun, Yann, and Anthony Zador. “Don’t Fear the Terminator.” Scientific American Blog Network. Accessed December 9, 2021. https://blogs.scientificamerican.com/observations/dont-fear-the-terminator/.
Yudkowsky, Eliezer, and Robin Hanson. “The Hanson-Yudkowsky AI-Foom Debate – LessWrong.” Accessed August 6, 2022. https://www.lesswrong.com/tag/the-hanson-yudkowsky-ai-foom-debate.

See also
List of sources arguing for existential risk from AIIs AI an existential threat to humanity?
Primary author: Katja Grace
Notes


List of sources arguing for existential risk from AI

Published 6 Aug 2022
This page is incomplete, under active work and may be updated soon.
This is a bibliography of pieces arguing that AI poses an existential risk.
List
Adamczewski, Tom. “A Shift in Arguments for AI Risk.” Fragile Credences. Accessed October 20, 2020. https://fragile-credences.github.io/prioritising-ai/.
Amodei, Dario, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. “Concrete Problems in AI Safety.” ArXiv:1606.06565 [Cs], July 25, 2016. http://arxiv.org/abs/1606.06565.
Bensinger, Rob, Eliezer Yudkowsky, Richard Ngo, So8res, Holden Karnofsky, Ajeya Cotra, Carl Shulman, and Rohin Shah. “2021 MIRI Conversations – LessWrong.” Accessed August 6, 2022. https://www.lesswrong.com/s/n945eovrA3oDueqtq.
Bostrom, N., Superintelligence, Oxford University Press, 2014.
Carlsmith, Joseph. “Is Power-Seeking AI an Existential Risk? [Draft].” Open Philanthropy Project, April 2021. https://docs.google.com/document/d/1smaI1lagHHcrhoi6ohdq3TYIZv0eNWWZMPEy8C8byYg/edit?usp=embed_facebook.
Christian, Brian. The Alignment Problem: Machine Learning and Human Values. W. W. Norton & Company, 2021.
Christiano, Paul. “What Failure Looks Like.” AI Alignment Forum (blog), March 17, 2019. https://www.alignmentforum.org/posts/HBxe6wdjxK239zajf/what-failure-looks-like.
Dai, Wei. “Comment on Disentangling Arguments for the Importance of AI Safety – LessWrong.” Accessed December 9, 2021. https://www.lesswrong.com/posts/JbcWQCxKWn3y49bNB/disentangling-arguments-for-the-importance-of-ai-safety.
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Ngo, Richard. “Thinking Complete: Disentangling Arguments for the Importance of AI Safety.” Thinking Complete (blog), January 21, 2019. http://thinkingcomplete.blogspot.com/2019/01/disentangling-arguments-for-importance.html. (Also LessWrong and the Alignment Forum, with relevant comment threads.)
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See also
List of sources arguing against existential risk from AIIs AI an existential threat to humanity?
Primary author: Katja Grace
Notes


Incentives to create AI systems known to pose extinction risks

Published 6 Aug 2022
Economic incentives to deploy AI systems seem unlikely to be reliably eliminated by knowledge that those AI systems pose an existential risk.
Details
Reasons for people with normal values to be incentivized to bring about human extinction
One might reason that if advanced AI systems had such malign preferences as to pose a substantial existential risk to humanity, then almost nobody would be motivated to deploy such systems. This reasoning fails because a) the personal cost of incurring such risks can be small relative to the benefits, even for a person who cares unusually much about the future of humanity, and b) because coordination problems reduce the counterfactual downside of taking such risks further still.
a) Externalities, or, personal costs of incurring extinction risks can be small
A person might strongly disprefer human extinction, and yet want to take an action which contributes to existential risk if:
the action only incurs a risk of extinction, or the risk is in the distant future, so that taking the risk does not negate other benefits accruing from the actionthe person does not value the survival of humanity (or a slightly higher chance of the survival of humanity) radically more than their other interestsUsing the AI system would materially benefit the person
A different way of describing this issue is that even if people disprefer causing human extinction, since most of the costs of human extinction fall on others, any particular person making a choice that risks humanity for private gain will take more risk than is socially optimal.
Examples:
A person faces the choice of using an AI lawyer system for $100, or a human lawyer for $10,000. They believe that the AI lawyer system is poorly motivated and agentic, and that movement of resources to such systems is gradually disempowering humanity, which they care about. Nonetheless, their action only contributes a small amount to this problem, and they are not willing to raise tens of thousands of dollars to avoid that harm.A person faces the choice of deploying the largest scale model to date, or trying to call off the project. They believe that at some scale, a model will become an existential threat to humanity. However they are very unsure at what scale, and estimate that the model in front of them only has a 1% chance of being the dangerous one. They value the future of humanity a lot, but not ten times more than their career, and calling off the project would be a huge hit, for only 1% of the future of humanity.
b) Coordination problems
Coordination problems can make the above situation more common: if a person believes that if they don’t take an action that incurs a cost to others, then the same action will be taken by others and the cost incurred anyway, then the real downside to incurring that cost is even smaller.
If many people are independently choosing whether to use dangerously misaligned AI systems, and all believe that they will anyway be used enough to destroy humanity in the long run, then even people who wouldn’t have wanted to deploy such systems if they were the sole decision-maker have reason to deploy them.
Practical plausibility
Situations where extinction risk is worth incurring for individuals seem likely to be common in a world where advanced AI systems in fact pose extinction risk. Reasons to expect this include:
In a large class of scenarios, AI x-risk is anticipated to take at least decades to transpire, after the decisions that brought about the riskPeople commonly weight benefits to strangers in the future substantially lower than benefits to themselves immediately1Many people claim to not intrinsically care about the long run existence of humanity.AI systems with objectives that roughly match people’s short term goals seem likely to be beneficial for those goals in the short term, while potentially costly to society at large in the long term. (e.g. an AI system which aggressively optimizes for mining coal may be economically useful to humans running a coal-mining company in the short term, but harmful to those and other humans in the long run, if it gains the resources and control to mine coal to a destructive degree.)
Primary author: Katja Grace
Notes


What do ML researchers think about AI in 2022?

Katja Grace, 4 August 2022
AI Impacts just finished collecting data from a new survey of ML researchers, as similar to the 2016 one as practical, aside from a couple of new questions that seemed too interesting not to add.
This page reports on it preliminarily, and we’ll be adding more details there. But so far, some things that might interest you:

37 years until a 50% chance of HLMI according to a complicated aggregate forecast (and biasedly not including data from questions about the conceptually similar Full Automation of Labor, which in 2016 prompted strikingly later estimates). This 2059 aggregate HLMI timeline has become about eight years shorter in the six years since 2016, when the aggregate prediction was 2061, or 45 years out. Note that all of these estimates are conditional on “human scientific activity continu[ing] without major negative disruption.”
P(extremely bad outcome)=5% The median respondent believes the probability that the long-run effect of advanced AI on humanity will be “extremely bad (e.g., human extinction)” is 5%. This is the same as it was in 2016 (though Zhang et al 2022 found 2% in a similar but non-identical question). Many respondents put the chance substantially higher: 48% of respondents gave at least 10% chance of an extremely bad outcome. Though another 25% put it at 0%.
Explicit P(doom)=5-10% The levels of badness involved in that last question seemed ambiguous in retrospect, so I added two new questions about human extinction explicitly. The median respondent’s probability of x-risk from humans failing to control AI1 was 10%, weirdly more than median chance of human extinction from AI in general2, at 5%. This might just be because different people got these questions and the median is quite near the divide between 5% and 10%. The most interesting thing here is probably that these are both very high—it seems the ‘extremely bad outcome’ numbers in the old question were not just catastrophizing merely disastrous AI outcomes.
Support for AI safety research is up: 69% of respondents believe society should prioritize AI safety research “more” or “much more” than it is currently prioritized, up from 49% in 2016.
The median respondent thinks there is an “about even chance” that an argument given for an intelligence explosion is broadly correct. The median respondent also believes machine intelligence will probably (60%) be “vastly better than humans at all professions” within 30 years of HLMI, and that the rate of global technological improvement will probably (80%) dramatically increase (e.g., by a factor of ten) as a result of machine intelligence within 30 years of HLMI.
Years/probabilities framing effect persists: if you ask people for probabilities of things occurring in a fixed number of years, you get later estimates than if you ask for the number of years until a fixed probability will obtain. This looked very robust in 2016, and shows up again in the 2022 HLMI data. Looking at just the people we asked for years, the aggregate forecast is 29 years, whereas it is 46 years for those asked for probabilities. (We haven’t checked in other data or for the bigger framing effect yet.)
Predictions vary a lot. Pictured below: the attempted reconstructions of people’s probabilities of HLMI over time, which feed into the aggregate number above. There are few times and probabilities that someone doesn’t basically endorse the combination of.
You can download the data here (slightly cleaned and anonymized) and do your own analysis. (If you do, I encourage you to share it!)

Individual inferred gamma distributions
The survey had a lot of questions (randomized between participants to make it a reasonable length for any given person), so this blog post doesn’t cover much of it. A bit more is on the page and more will be added.
Thanks to many people for help and support with this project! (Many but probably not all listed on the survey page.)

Cover image: Probably a bootstrap confidence interval around an aggregate of the above forest of inferred gamma distributions, but honestly everyone who can be sure about that sort of thing went to bed a while ago. So, one for a future update. I have more confidently held views on whether one should let uncertainty be the enemy of putting things up.