Violet Hour's Quick takes

Here's a dynamic that I've seen pop up more than once.

Person A says that an outcome they judge to be bad will occur with high probability, while making a claim of the form "but I don't want (e.g.) alignment to be doomed — it would be a huge relief if I'm wrong!"

It seems uncontroversial that Person A would like to be shown that they're wrong in a way that vindicates their initial forecast as ex ante reasonable.

It seems more controversial whether Person A would like to be shown that their prediction was wrong, in a way that also shows their initial prediction to have been ex ante unreasonable. 

In my experience, it's much easier to acknowledge that you were wrong about some specific belief (or the probability of some outcome), than it is to step back and acknowledge that the reasoning process which led you to your initial statement was misfiring. Even pessimistic beliefs can be (in Ozzie’s language) "convenient beliefs" to hold. 

If we identify ourselves with our ability to think carefully, coming to believe that there are errors in our reasoning process can hit us much more personally than updates about errors in our conclusions. Optimistic updates might be an update towards me thinking that my projects have been less worthwhile than I thought, that my local community is less effective than I thought, or that my background framework or worldview was in error. I think these updates can be especially painful for people who are more liable to identify with their ability to reason well, or identify with the unusual merits of their chosen community. 

To clarify: I'm not claiming that people with more pessimistic conclusions are, in general, more likely to be making reasoning errors. Obviously there are plenty of incentives towards believing rosier conclusions. I'm simply claiming that: if someone arrives at a pessimistic conclusion based on faulty reasoning, then you shouldn't necessarily expect optimistic pushback to be uniformly welcomed— for all of the standard reasons that updates of the form "I could've done better on a task I care about" can be hard to accept.

A working attempt to sketch a simple three-premise argument for the claim: ‘TAI will result in human extinction’, and offer objections. Made mostly for my own benefit while working on another project, but I thought it might be useful to post here.

The structure of my preferred argument is similar to an earlier framing suggested by Katja Grace.

1. Goal-directed superhuman AI systems will be built (let’s say conditioned on TAI).
2. If goal-directed superhuman AI systems are built, their values will result in human extinction if realized.
3. If goal-directed superhuman AI systems are built, they’ll be able to realize their values — even if their values would result in human extinction if realized.
4. Thus: Humanity will go extinct.

I’ll offer some rough probabilities, but the probabilities I’m offering shouldn’t be taken seriously. I don’t think probabilities are the best way to adjudicate disputes of this kind, but I thought offering a more quantitative sense of my uncertainty (based on my immediate impressions) might be helpful in this case. For the (respective) premises, I might go for 98%, 7%, 83%, resulting in a ~6% chance of human extinction given TAI.  

Some more specific objections:

• Obviously Premise 2 is doing a lot of the work here. I think that one of the main arguments for believing in Premise 2 is a view like Rob’s, which holds that current ML is on track to produce systems which are, “in the ways that matter”, more like ‘randomly sample (simplicity-weighted) plans’ than anything recognizably human. If future systems are sampling from simplicity-weighted plans to achieve arbitrary goals, then Premise 2 does start to look very plausible.
  • This basically just seems like an extremely strong claim about the inductive biases of ML systems, and my (likely unsatisfying) response boils down to: (1) I don’t see any strong argument for believing it, and (2) I see some arguments for the alternative conclusion.
  • I find myself really confused when trying to think about this debate. In a discussion of Rob’s post, Daniel Kokotajlo says: “IMO the burden of proof is firmly on the side of whoever wants to say that therefore things will probably be fine.”
  • I think I just don’t get the intuition behind his argument (tagging @kokotajlod in case he wants to correct any misunderstandings). I don’t really like ‘burden of proof’ talk, but my instinct is to say “look, LLMs distill human cognition, much of this cognition implicitly contains plans, human-like value judgements, etc.” I start from a place where I currently believe “future systems have human-like inductive biases” will be a better predictive abstraction than “randomly sample from the space of simplicity-weighted plans”. And … I just don’t currently see the argument for rejecting my current view?
    • Perhaps there are near-term predictions which would help weigh on the dispute between the two hypotheses? I currently interpret the disagreement here as a disagreement about the relevant outcome space over which we should be uncertain, which feels hard to adjudicate. But, right now, I struggle to see the argument for the more doomy outcome space.
• More on Premise 2: Paul Christiano offers various considerations which count against doom which appear to go through without having “solved alignment”. These considerations feel less forceful to me than the points in the bullet point above, but they still serve to make Premise 2 seem less likely.
  • “Given how small the [resource costs of keeping humans around are], even tiny preferences one way or the other will dominate incidental effects from grabbing more resources”.
  • “There are just a lot of plausible ways to care a little bit (one way or the other!) about a civilization that created you, that you've interacted with, and which was prima facie plausibly an important actor in the world”
  • “Most humans and human societies would be willing to spend much more than 1 trillionth of their resources (= $100/year for all of humanity) for a ton of random different goals”
  • Paul also mentions “decision-theoretic arguments for cooperation”, including a passing reference to ECL.

I also think the story by Katja Grace below is plausible, in which superhuman AI systems are “goal-directed”, but don’t lead to human extinction.

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. 

Perhaps the story above is unlikely because the AI systems in Grace’s story would (in the absence of strong preventative efforts) be dangerous maximizers. I think that this is most plausible on something like Eliezer’s model of agency, and if my views change my best bet is that I’ll have updated towards his view. 

• I believe: as you develop gradually more capable agentic systems, there are dynamic pressures towards a certain kind of coherency. I don’t think that claim alone establishes the existence of dynamic pressures towards ‘dangerous maximizing cognition’.
• I think that AGI cognition (like our own) may well involve schemas, like (say) being loyal, or virtuous. We don’t argmax(virtue). Rather, the virtue schema also applies to the process by which we search over plans.
  • So I don’t see why ‘having superhuman AIs run Walmart’ necessarily leads to doom, because they might just be implementing schemas like “be a good business professional”, rather than “find the function f(.) which is most ‘business-professional-like’, then maximize f(.) — regardless of whether any human would consider f(.) to represent anything corresponding to ‘good business professional.” 
    • Alex Turner has a related comment here. 
• On Premise 3: I feel unsatisfied, so far, by accounts of AI takeover scenarios. Admittedly, it seems kinda mad for me to say “I’m confident that an AI with greater cognitive power than all of humanity couldn’t kill us if it wanted to”, which is one reason that I’m only at ~⅙ chance that we’d survive in that situation. 
  • But I also don’t know how much my conclusion is swayed by a sense of wanting to avoid the hubris of “man, it would be Really Dumb if you said a highly capable AI couldn’t kill us if it wanted to, and then we end up dead”, rather than a more obviously virtuous form of cognition.    
  • A system being ‘cognitively efficient wrt humanity’ doesn’t automatically entail ‘whatever goals the system has – and whatever constraints the system might otherwise face – the cognitively efficient system gets what it wants’. The arguments attempting to move from ‘AI with superhuman cognitive abilities’ to ‘human extinction’ feel fuzzier than I’d like.  

• If superhuman systems don’t foom, we might have marginally superhuman systems, who are able to be thwarted before they kill literally everyone (while still doing a lot of damage). Constraints like ‘accessing the relevant physical infrastructure’ might dominate the gains from greater cognitive efficiency.
  • I also feel pretty confused about how much actual real-world power would be afforded to AIs in light of their highly advanced cognition (a relevant recent discussion), which further brings down my confidence in Premise 3. 
  • I’m also assuming that: conditioned on an AI instrumentally desiring to kill all humans, deceptive alignment is likely. I haven’t read posts like this one which might challenge that assumption. If I came to believe that deceptive alignment was highly unlikely, this could lower the probability of either Premise 2 or Premise 3. 

Finally, I sometimes feel confused by the concept of ‘capabilities’ as it’s used in discussions about AGI. From Jenner and Treutlein’s response to Grace’s counterarguments:

Assuming it is feasible, the question becomes: why will there be incentives to build increasingly capable AI systems? We think there is a straightforward argument that is essentially correct: some of the things we care about are very difficult to achieve, and we will want to build AI systems that can achieve them. At some point, the objectives we want AI systems to achieve will be more difficult than disempowering humanity, which is why we will build AI systems that are sufficiently capable to be dangerous if unaligned.”

Maybe one thing I’m thinking here is that “more difficult” is hard to parse. The AI systems might be able to achieve some narrower outcome that we desire, without being “capable” of destroying humanity. I think this is compatible with having systems which are superhumanly capable of pursuing some broadly-scoped goals, without being capable of pursuing all broadly-scoped goals. 

• Psychologists talk of ‘g’ bc there’s correlation between performance on tasks we intuitively think of as cognitive, and correlations with some important life outcomes. I don’t know how well the unidimensional notion of intelligence will transfer to advanced AI systems. The fact that some AIs perform decently on IQ tests without being good at much else is at least some weak evidence against the generality of the more unidimensional ‘intelligence’ concept.
  • However, I agree that there’s a well-defined sense in which we can say that AIs are more cognitively capable than all of humanity combined. I also think that my earlier point about expecting future systems to exhibit human-like inductive biases makes the argument in the bullet point above substantially weaker.
    • I still remain uneasy about the extent to which unidimensional notion of ‘capabilities’ can feed into claims about takeoffs and takeover scenarios, and I’m currently unclear on whether this makes a practical difference.   

(Also, I’m no doubt missing a bunch of relevant information here. But this is probably true for most people, and I think it’s good for people to share objections even if they’re missing important details)