When “human-level” is the wrong threshold for AI

Ben Millwood🔸22 Jun 2024 14:34 UTC
38 points
3 comments7 min readEA link

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This article should be accessible to AI non-experts, and it may turn out the AI experts already think like this, in which case it’s mostly for non-experts. I’m not much of an “AI insider” as such, and as usual for me, I have weaknesses in literature search and familiarity with existing work. I appreciate comments about what in the below has already been discussed, and especially what has already been refuted :)

Thanks to Egg Syntax, Nina Panickssery, and David Mears for some comments on the draft. Thanks Dane Sherburn for sending me a link to When discussing AI risks, talk about capabilities, not intelligence, which discusses similar themes.

I plan to post this to the EA forum first, wait and see if people like it, and then if they do, cross-post it to LW and/​or the alignment forum.

Link preview image is this by Possessed Photography on Unsplash.

OK let’s get to the point

I think people treat “when does an AI become better than humans at all relevant tasks” as a key moment in their minds. I suggest this threshold is only a proxy for the capability thresholds that actually matter, and moreover that the closer we get to these thresholds, the worse a proxy it is. I suggest we move towards explicitly identifying and examining these thresholds, and regard “human-level” as an increasingly irrelevant label for safety. This is particularly relevant for forecasting when these thresholds will arrive (or even noticing when they do arrive), and informs what sorts of protective measures may be necessary then.

After making some general points, I’ll focus on these not-necessarily-exhaustive examples of important AI thresholds which I’ll argue are meaningfully distinct from “human-level”:

  • Able to accelerate further AI research, potentially bringing about a massive, compounding transformation,

    • to a lesser extent, able to accelerate other kinds of research, e.g. biotech,

  • Smart enough to be a takeover threat,

  • Able to develop its own goals and agency about them,

  • Capable enough to be economically transformative,

  • Potentially worthy of moral consideration.

For some of these, human-level AI is clearly sufficient but not clearly necessary. For others it seems to me possible that it’s neither. (Being universally human-level seems to me essentially never necessary.)

Rephrased, AI being unable to do human capability X is only reassuring if X is either directly necessary for or at least somehow connected with the AI being a threat.

For example, AI not being capable of true semantic understanding, of emotion, or of some other deep nuance of the human experience, doesn’t matter to X-risk if those things are not essential to winning a power struggle[1]. We already know they’re not essential to winning a game of Starcraft.

(To clarify, whenever I say “below-human AI” or equivalent, assume that I mean below human level in some meaningful or significant way, on some capability that is at least apparently useful and relevant, rather than just barely below human, or below human only in particular idiosyncratic niches.)

General arguments

AI is already massively superhuman in many domains

For example, today’s AI tends to be much faster and cheaper to run than humans are, on tasks that both are capable of. They also typically seem to be able to retrieve a broader range of general knowledge than any individual human can.

This is mostly obvious, but it’s worth pointing out its implications here. Some problems turn out to be easy for AI, others turn out to be hard. If they all continue to improve at their own rates, we’ll generally see AIs with a mix of superhuman, human-level, and below-human traits. The first time an AI reaches human level on some particular set of prerequisite skills for a task, we expect it to already be significantly superhuman at some of them, and to still be below human level at some reasonable fraction of things not in the set.

Humans have specific capabilities, as well as general ones

The easiest (and most facile) argument against “human-level” as a benchmark is that lots of our capabilities are niche, too, and not directly relevant to the thing we do that makes us so powerful. Humans are much better at recognising human faces than other kinds of object. We are probably genetically optimised for language adoption, and the languages we have adopted are probably (with bidirectional causality) the kinds of languages we are best at adopting. We have a lot of “hard-coded” behaviours, and I’d bet some of these are involved in our mechanisms for performing so-called “general” reasoning.

The most useful form of this point is that it may be possible to get to AGI without outperforming humans at things that humans are narrowly optimised for, and so poor AI performance on those things may give us false confidence (or false despair, depending on your perspective) in their lack of general intelligence.

OK, so what about AGI as a threshold, instead of human-level AI?

(Sometimes AGI is defined by reference to human-level generalisation capabilities. Here I’m supposing that you have some more first-principles attempt to define cognitive generalisation.)

I think AGI as a threshold is more defensible than “human-level”, but I think there’s still a case to make that:

  • many AI capabilities we care about are achievable with general reasoning ability, but not only with general reasoning ability, so AGI is not a prerequisite,

  • in any case, general reasoning ability is not (or at least, there’s room to doubt that it is) an either/​or proposition, and AIs may be able to do enough generalisation before they can do all generalisation. (As an aside, we might even doubt whether we ourselves are capable of all generalisation, or what it would feel like to be missing some.)

Substituting instead of replicating

When the first superhuman chess bot was created, it was very likely the case that the world’s greatest grandmasters still had better intuition or better “game sense”, or some other better skills, perhaps even in a way that could have been empirically verified. It won anyway (I suggest) because its advantage in game tree search was strong enough to overcome that weakness.

Similarly, Go bots that were able to beat the best humans in a normal game had relatively glaring weaknesses that, once known, could be exploited by intermediate-level players to win (see: Man beats machine at Go in human victory over AI). I forget the details, but it was something akin to being able to count the number of stones needed to complete a loop. In retrospect, knowing the exploit, this seems like it would have been a cognitive prerequisite to beat the best humans at Go, but as it turned out, it wasn’t.

The lesson in both cases is that just because we achieve performance on some end goal using some set of skills, it doesn’t mean that AI needs those skills at that level to replicate that performance. AIs incapable of effectively replicating our solution to a problem may use superhuman abilities in other areas to be able to come up with their own.

Specific cases

Accelerating research

Most people imagine that at least very dramatic research accelerations will come exactly when AIs are able to entirely replace human researchers, which will coincide with AIs being (approximately) human-level AGIs. But:

Can meaningfully below-human /​ non-general AIs do fully automatic research?

What is really necessary in order to do meaningful, novel research? Are any of the hard-to-replicate ingredients of the human research process substitutable? E.g. can we replace good taste in choosing research directions with ability to rapidly scale, filter, and synthesise the results of trying every promising direction at once?

Can AIs dramatically accelerate research before they are able to fully automate it?

In the long term, probably not. Whatever part of the research process that still requires human input will become a bottleneck until human input can be eliminated entirely (a sort of Amdahl’s law for research). However, there’s still a question of how much and for how long those bottlenecks will slow progress down, and whether we’ll be able to use below-human AI assistants to very rapidly progress to the point where AIs can run the whole process autonomously. Then the threshold to watch out for isn’t really “when can AIs automate research?”, but “when can AIs put research automation within reach?”

Would human-level or above-human-level AI researchers necessarily create transformative progress?

I think a common (and not entirely unreasonable) assumption is that once we have AIs able to improve themselves in a compounding way, they can improve their rate of improvement in a way that allows them to exponentially or even super-exponentially accelerate their capabilities.

However, there are ways that this could fail to happen. For example, we need superintelligence to not only be possible, but attainable with existing resources (e.g. hardware, electricity, time, data). We might imagine that resource efficiency could be dramatically improved, but remember that we need do it before the intelligence gains that come from it. An AI that is truly only human-level would accelerate progress no more than the next human, so if anything miraculous happens here, it surely happens because AI gets close enough to being a researcher to be able to exploit one of the ways in which it is already much better than humans – e.g. breadth of knowledge, or being cheap to run, rerun, and duplicate, or being able to integrate more efficiently with conventional computers – and that needs to be able to unlock a lot of research potential that was inaccessible to humans alone. (To be clear, it seems maybe more likely to me than not that there would be a lot of progress here, but I don’t know how much.)

We also need to know that there are no major roadblocks or bottlenecks in AI development that prevent going beyond intelligence level X, and are not (easily or quickly) solvable at intelligence level X. While it’s hard to make any conceptual argument on whether these roadblocks are likely to exist, it seems also hard (IMO, at least) to rule them out.

This is the example I was thinking of when I said that human-level AI may not be sufficient (in addition to perhaps not being necessary).

Agency, goals, deception and takeover

I imagine many people who equate “potential for takeover” with “human-level” do so via the above idea of equating both with “able to rapidly become superintelligent”, rather than thinking a human-level AI is itself capable of takeover. In this section, therefore, let’s examine only the cases where non-superintelligent AI is developing goals and agency, or attempting takeover.

My previous article, Can the AI afford to wait?, is partly focused on this question. I think it’s pretty non-obvious what kinds of cognitive ability are necessary to follow the argument that self-preservation is instrumentally convergent, and that seizing power or attempting to negotiate are actions available to do that. It seems possible that the main bottleneck here isn’t cognitive ability but some other resources like memory /​ persistence between runs and the ability to reflect, which may come from scaffolding alone. Another threshold is when an AI believes that it has enough influence on the material world to be able to negotiate with or coerce humanity, which could happen as soon as it’s able to autonomously execute cyber attacks, for example. All of these seem easier to me than AGI.

Economic transformation

I think the considerations here are pretty similar to the research question, though the specifics are different in ways that could be meaningful. If AI is able to trigger productivity increases of 10x but not 100x, what effect does that have on the economy? If it is able to automate 80% of jobs but not the other 20%, what effect does that have? If it’s able to automate knowledge work but not physical work, what then? It seems to me that any of these scenarios could potentially be very socially disruptive, while still missing meaningful fractions of what humans are currently able to do.

Worthiness of moral consideration

Of all the points equivocated with “human-level”, this one seems the weakest to me. Given that animals are usually considered worthy of human consideration, many cognitive tasks are clearly not prerequisites. On the other hand, consciousness and capacity for suffering seem so poorly understood that we are hardly able to guarantee that any particular level of cognitive ability would have any particular relevance for the question of suffering, especially as we are often specifically training AIs to mimic us, which seems likely to lead to them mimicking the appearance of suffering regardless of whether they experience it.

I’m open to the possibility that AIs can suffer, but I think advancing capabilities really doesn’t solve the question at all, at human or any other level.

Recap

I gave a bunch of suggestions for why common concerns with AIs don’t require that it can match humans in all aspects. If you’re on board with them, let’s start to distinguish when we talk about “advanced AI” which particular outcome we care about and which particular cognitive capabilities it requires, and let’s not be misled by unrelated inadequacies.

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  1. ^

    Egg Syntax highlights this passage from Gwern’s short story:

    We should pause to note that a Clippy2 still doesn’t really think or plan. It’s not really conscious. It is just an unfathomably vast pile of numbers produced by mindless optimization starting from a small seed program that could be written on a few pages. It has no qualia, no intentionality, no true self-awareness, no grounding in a rich multimodal real-world process of cognitive development yielding detailed representations and powerful causal models of reality; it cannot ‘want’ anything beyond maximizing a mechanical reward score, which does not come close to capturing the rich flexibility of human desires, or historical Eurocentric contingency of such conceptualizations, which are, at root, problematically Cartesian. When it ‘plans’, it would be more accurate to say it fake-plans; when it ‘learns’, it fake-learns; when it ‘thinks’, it is just interpolating between memorized data points in a high-dimensional space, and any interpretation of such fake-thoughts as real thoughts is highly misleading; when it takes ‘actions’, they are fake-actions optimizing a fake-learned fake-world, and are not real actions, any more than the people in a simulated rainstorm really get wet, rather than fake-wet. (The deaths, however, are real.)

Ben Millwood🔸22 Jun 2024 14:34 UTC
38 points
3 comments7 min readEA link