Co-founder and CEO of quiver.trade. Interested in mechanism design and neuroscience. Hopes to contribute to AI alignment.
Another strong upvote for a great sequence. Social-instinct AGIs seems to me a very promising and very much overlooked approach to AGI safety. There seem to be many "tricks" that are "used by the genome" to build social instincts from ground values, and reverse engineering these tricks seem particularly valuable for us. I am eagerly waiting to read the next posts.
In a previous post I shared a success model that relies on your idea of reverse engineering the steering subsystem to build agents with motivations compatible with a safe Oracle design, including the class of reversely aligned motivations. What is your opinion on them? Do you think the set of "social instincts" we would want to incorporate into an AGI changes much if we are optimizing for reverse vs direct intent alignment?
While I am sure that you have the best intentions, I believe the framing of the conversation was very ill-conceived, in a way that makes it harmful, even if one agrees with the arguments contained in the post.
For example, here is the very first negative consequence you mentioned:
(bad external relations) People on your team will have a low trust and/or adversarial stance towards neighboring institutions and collaborators, and will have a hard time forming good-faith collaboration. This will alienate other institutions and make them not want to work with you or be supportive of you.
I think one can argue that, this argument being correct, the post itself will exacerbate the problem by bringing greater awareness to these "intentions" in a very negative light.
It is important for this community to be able to have some difficult discussions without attracting backlash from outsiders, and having specific neutral/untainted terminology serves precisely for that purpose.
As others have mentioned, your preferred 'Idea A' has many complications and you have not convincingly addressed them. As a result, good members of our community may well find 'Idea B' to be worth exploring despite the problems you mention. Even if you don't think their efforts are helpful, you should be careful to portrait them in a good light.
I think you are right! Maybe I should have actually written different posts about each of these two plans.
And yes, I agree with you that maybe the most likely way of doing what I propose is getting someone ultra rich to back it. That idea has the advantage that it can be done immediately, without waiting for a Math AI to be available.
To me it still seems important to think of what kind of strategical advantages we can obtain with a Math AI. Maybe it is possible to gain a lot more than money (I gave the example of zero-day exploits, but we can most likely get a lot of other valuable technology as well).
In my model the Oracle would stay securely held in something like a Faraday cage with no internet connection and so on.
So yes, some people might want to steal it, but if we have some security I think they would be unlikely to succeed, unless it is a state-level effort.
I think it is an interesting idea, and it may be worthwhile even if Dagon is right and it results in regulatory capture.
The reason is, regulatory capture is likely to benefit a few select companies to promote an oligopoly. That sounds bad, and it usually is, but in this case it also reduces the AI race dynamic. If there are only a few serious competitors for AGI, it is easier for them to coordinate. It is also easier for us to influence them towards best safety practices.
Having read Steven's post on why humans will not create AGI through a process analogous to evolution, his metaphor of the gene trying to do something felt appropriate to me.
If the "genome = code" analogy is the better one for thinking about the relationship of AGIs and brains, then the fact that the genome can steer the neocortex towards such proxy goals as salt homeostasis is very noteworthy, as a similar mechanism may give us some tools, even if limited, to steer a brain-like AGI toward goals that we would like it to have.
I think Eliezer's comment is also important in that it explains quite eloquently how complex these goals really are, even though they seem simple to us. In particular the positive motivational valence that such brain-like systems attribute to internal mental states makes them very different from other types of world-optimizing agents that may only care about themselves for instrumental reasons.
Also the fact that we don't have genetic fitness as a direct goal is evidence not only that evolution-like algorithms don't do inner alignment well, but also that simple but abstract goals such as inclusive genetic fitness may be hard to install in a brain-like system. This is especially so if you agree, in the case of humans, that having genetic fitness as a direct goal, at least alongside the proxies, would probably help fitness, even in the ancestral environment.
I don't really know how big of a problem this is. Given that our own goals are very complex and that outer alignment is hard, maybe we shouldn't be trying to put a simple goal into an AGI to begin with.
Maybe there is a path for using these brain-like mechanisms (including positive motivational valence for imagined states and so on) to create a secure aligned AGI. Getting this answer right seems extremely important to me, and if I understand correctly, this is a key part of Steven's research.
Of course, it is also possible that this is fundamentally unsafe and we shouldn't do that, but somehow I think that is unlikely. It should be possible to build such systems in a smaller scale (therefore not superintelligent) so that we can investigate their motivations to see what the internal goals are, and whether the system is treacherous or looking for proxies. If it turns out that such a path is indeed fundamentally unsafe, I would expect this to be related to ontological crises or profound motivational changes that are expected to occur as capability increases.
That is, that we shouldn't worry so much about what to tell the genie in the lamp, because we probably won't even have a say to begin with.
I think you summarized it quite well, thanks! The idea written like that is more clear than what I wrote, so I'll probably try to edit the article to include this claim explicitly like that. This really is what motivated me to write this post to begin with.
Personally I (also?) think that the right "values" and the right training is more important.
You can put the also, I agree with you.
At the current state of confusion regarding this matter I think we should focus on how values might be shaped by the architecture and training regimes, and try to make progress on that even if we don't know exactly what the human values are or what utility functions they represent.
I agree my conception is unusual, I am ready to abandon it in favor of some better definition. At the same time I feel like an utility function having way too many components makes it useless as a concept.
Because here I'm trying to derive the utility from the actions, I feel like we can understand the being better the less information is required to encode its utility function, in a Kolmogorov complexity sense, and that if its too complex then there is no good explanation to the actions and we conclude the agent is acting somewhat randomly.
Maybe trying to derive the utility as a 'compression' of the actions is where the problem is, and I should distinguish more what the agent does from what the agent wants. An agent is then going to be irrational only if the wants are inconsistent with each other; if the actions are inconsistent with what it wants then it is merely incompetent, which is something else.
One thing that appears to be missing on the filial imprinting story is a mechanism allowing the "mommy" thought assessor to improve or at least not degrade over time.
The critical window is quite short, so many characteristics of mommy that may be very useful will not be perceived by the thought assessor in time. I would expect that after it recognizes something as mommy it is still malleable to learn more about what properties mommy has.
For example, after it recognizes mommy based on the vision, it may learn more about what sounds mommy makes, and what smell mommy has. Because these sounds/smalls are present when the vision-based mommy signal is present, the thought assessor should update to recognize sound/smell as indicative of mommy as well. This will help the duckling avoid mistaking some other ducks for mommy, and also help the ducklings find their mommy though other non-visual cues (even if the visual cues are what triggers the imprinting to begin with).
I suspect such a mechanism will be present even after the critical period is over. For example, humans sometimes feel emotionally attracted to objects that remind them or have become associated with loved ones. The attachment may be really strong (e.g. when the loved one is dead and only the object is left).
Also, your loved ones change over time, but you keep loving them! In "parental" imprinting for example, the initial imprinting is on the baby-like figure, generating a "my kid" thought assessor associated with the baby-like cues, but these need to change over time as the baby grows. So the "my kid" thought assessor has to continuously learn new properties.
Even more importantly, the learning subsystem is constantly changing, maybe even more than the external cues. If the learned representations change over time as the agent learns, the thought assessors have to keep up and do the same, otherwise their accuracy will slowly degrade over time.
This last part seems quite important for a rapidly learning/improving AGI, as we want the prosocial assessors to be robust to ontological drift. So we both want the AGI to do the initial "symbol-grounding" of desirable proto-traits close to kindness/submissiveness, and also for its steering subsystem to learn more about these concepts over time, so that they "converge" to favoring sensible concepts in an ontologically advanced world-model.