Independent alignment researcher
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I don't think the conclusion follows from the premises. People often learn new concepts after studying stuff, and it seems likely (to me) that when studying human cognition, we'd first be confused because our previous concepts weren't sufficient to understand it, and then slowly stop being confused as we built & understood concepts related to the subject. If an AI's thoughts are like human thoughts, given a lot of time to understand them, what you describe doesn't rule out that the AI's thoughts would be comprehensible.
The mere existence of concepts we don't know about in a subject doesn't mean that we can't learn those concepts. Most subjects have new concepts.
Counterintuitively, it may be easier for an organization (e.g. Redwood Research) to get a $1 million grant from Open Phil than it is for an individual to get a $10k grant from LTFF. The reason why is that both grants probably require a similar amount of administrative effort and a well-known organization is probably more likely to be trusted to use the money well than an individual so the decision is easier to make. This example illustrates how decision-making and grant-making processes are probably just as important as the total amount of money available.
A priori, and talking with some grant-makers, I'd think the split would be around people & orgs who are well-known by the grant-makers, and those who are not well-known by the grant-makers. Why do you think the split is around people vs orgs?
This seems like an underestimate because you don’t consider whether the first “AGI” will indeed make it so we only get one chance. If it can only self improve by more gradient steps, then humanity has a greater chance than if it self improves by prompt engineering or direct modification of its weights or latent states. Shard theory seems to have nonzero opinions on the fruitfulness of the non-data methods.
I think this type of criticism is applicable in an even wider range of fields than even you immediately imagine (though in varying degrees, and with greater or lesser obviousness or direct correspondence to the SGD case). Some examples:
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Despite the economists, the economy doesn't try to maximize welfare, or even net dollar-equivalent wealth. It rewards firms which are able to make a profit in proportion to how much they're able to make a profit, and dis-rewards firms which aren't able to make a profit. Firms which are technically profitable, but have no local profit incentive gradient pointing towards them (factoring in the existence of rich people and lenders, neither of which are perfect expected profit maximizers) generally will not happen.
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Individual firms also don't (only) try to maximize profit. Some parts of them may maximize profit, but most are just structures of people built from local social capital and economic capital incentive gradients.
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Politicians don't try to (only) maximize win-probability.
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Democracies don't try to (only) maximize voter approval.
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Evolution doesn't try to maximize inclusive genetic fitness.
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Memes don't try to maximize inclusive memetic fitness.
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Academics don't try to (only) maximize status.
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China doesn't maximize allegiance to the CCP.
I think there's a general tendency for people to look at local updates in a system (when the system has humans as decision nodes, the local updates are called incentive gradients), somehow perform some integration-analogue for a function which would produce those local updates, then find a local minimum of that "integrated" function and claim the system is at that minimum or can be approximated well by the system at that minimum. Generally, this seems constrained in empirical systems by common sense learned by experience with the system, but in less and less empirical systems (like the economy or SGD), people get more and more crazy because they have less learned common sense to guide them when making the analysis.
This is true, but indicates a radically different stage in training in which we should find deception compared to deception being an intrinsic value. It also possibly expands the kinds of reinforcement schedules we may want to use compared to the worlds where deception crops up at the earliest opportunity (though pseudo-deception may occur, where behaviors correlated with successful deception are reinforced possibly?).
John usually does not make his plans with an eye toward making things easier. His plan previously involved values because he thought they were strictly harder than corrigibility. If you solve values, you solve corrigibility. Similarly, if you solve abstraction, you solve interpretability, shard theory, value alignment, corrigibility, etc.
I don’t know all the details of John’s model here, but it may go something like this: If you solve corrigibility, and then find out corrigibility isn’t sufficient for alignment, you may expect your corrigible agent to help you build your value aligned agent.
I think the pointer “the thing I would do if I wanted to make a second AI that would be the best one I could make at my given intelligence” is what is being updated in favor of, since this does feel like a natural abstraction, given how many agents would think this (also seems very similar to the golden rule. “I will do what I would want a successor AI to do if the successor AI was actually the human’s successor AI”. or “treat others (the human) how I’d like to be treated (by a successor AI), (and abstracting one meta-level upwards)”). Whether this turns out to be value learning or something else 🤷. This seems a different question from whether or not it is indeed a natural abstraction.
Seems possibly relevant & optimistic when seeing deception as a value. It has the form ‘if about to tell human statement with properties x, y, z, don’t’ too.
Re: agents terminalizing instrumental values.
I anticipate there will be a hill-of-common-computations, where the x-axis is the frequency[1] of the instrumental subgoal, and the y-axis is the extent to which the instrumental goal has been terminalized.
This is because for goals which are very high in frequency, there will be little incentive for the computations responsible for achieving that goal to have self-preserving structures. It will not make sense for them to devote optimization power towards ensuring future states still require them, because future states are basically guaranteed to require them.[2]
An example of this for humans may be the act of balancing while standing up. If someone offered to export this kind of cognition to a machine which did it just as good as I, I wouldn't particularly mind. If someone also wanted to change physics in such a way that the only effect is that magic invisible fairies made sure everyone stayed balancing while trying to stand up, I don't think I'd mind that either[3].
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I'm assuming this is frequency of the goal assuming the agent isn't optimizing to get into a state that requires that goal.
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This argument also assumes the overseer isn't otherwise selecting for self-preserving cognition, or that self-preserving cognition is the best way of achieving the relevant goal.
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Except for the part where there's magic invisible fairies in the world now. That would be cool!
Interpretability seems pretty useful for alignment, but it also seems pretty dangerous for capabilities. Overall the field seems net-bad. Using an oversimplified model, my general reason for thinking this is because for any given interpretability advance, it can either be used for the purposes of capabilities or the purposes of alignment. Alignment is both harder, and has fewer people working on it than improving model capabilities. Even if the marginal interpretability advance would be net good for alignment if alignment and capabilities were similar in size and difficulty, we should still expect that it will get used for the purposes of capabilities.
Lots of people like pointing to how better interpretability almost never makes long-term improvements to model capabilities, but it leads to just as few improvements to model alignment! And the number & quality of papers or posts using interpretability methods for capabilities vastly exceeds the number & quality using interpretability methods for alignment.
The only example of interpretability leading to novel alignment methods I know of is shard theory's recent activation additions work (notably work that is not so useful if Nate & Eliezer are right about AGI coherence). In contrast, it seems like all the papers using interpretability to advance capabilities rely on Anthropic's transformer circuits work.
These are two interesting case-studies, and more work should probably be done comparing their relative merits. But in lieu of that, my explanation for the difference in outcomes is this:
Anthropic's work was highly explorational, while Team Shard's was highly targeted. Anthropic tried to understand the transformer architecture and training process in general, while shard theory tried to understand values and only values. If capabilities is easier than alignment, it should not be too surprising if an unfocused approach makes capabilities relatively easier, while a focused-on-values approach makes alignment relatively easier. The unfocused approach will gather a wide range of low-hanging fruit, but little low-hanging fruit is alignment related, so most fruit gathered will be capabilities related.
This is why I'm pessimistic about most interpretability work. It just isn't focused enough! And its why I'm optimistic about interpretability (and interpretability adjacent) work focused on understanding explicitly the value systems of our ML systems, and how those can be influenced.
So a recommendation for those hoping to work on interpretability and have it be net-positive: Focus on understanding the values of models! Or at least other directly alignment relevant parts of models.
For example, I mostly expect a solution to superposition to be net-negative, in the same way that transoformer circuits is net-negative. Though at the same time I also expect superposition to have lots of alignment benefits in the short-term. If AGI is further off, superposition ends up being net-negative, the closer AGI is to now, the more positive a superposition solution becomes.
Another sort of interpretability advance I'm worried about: locating the optimization algorithms operating inside neural networks. I admit these have large alignment boosts, but that seems inconsequential compared to their large potential for large boosts to capabilities. Such advances may be necessary for alignment though, so I'm more happy in a world where these are not so widely publicized, and given only to the superintelligence alignment wings of AGI labs [EDIT: and a group of researchers outside the labs, all in a way such that nobody shares it with people who may use the knowledge to advance capabilities].