Marc Carauleanu

AI Safety Researcher @AEStudio 

Currently developing a novel AI Alignment agenda that focuses on scalably inducing self-other overlap in state-of-the-art machine learning models in order to facilitate the learning of empathetic and non-deceptive representations and elicit prosocial behaviors.

Wiki Contributions


I am slightly confused by your hypothetical. The hypothesis is rather that when the predicted reward from seeing my friend eating a cookie due to self-other overlap is lower than the obtained reward of me not eating a cookie, the self-other overlap might not be updated against because the increase in subjective reward from risking to get the obtained reward is higher than the prediction error in this low stakes scenario. I am fairly uncertain about this being what actually happens but I put it forward as a potential hypothesis. 

"So anyway, you seem to be assuming that the human brain has no special mechanisms to prevent the unlearning of self-other overlap. I would propose instead that the human brain does have such special mechanisms, and that we better go figure out what those mechanisms are. :)"

My intuition is that the brain does have special mechanisms to prevent the unlearning of self-other overlap so I agree with you that we should be looking into the literature to understand them, as one would expect mechanisms like that to evolve given incentives to unlearn self-other overlap and given the evolutionary benefits of self-other overlap. One such mechanism could be the brain being more risk tolerant when it comes to empathic responses and not updating against self-other overlap when the predicted reward is lower than the obtained reward but I don't have a model of how exactly this would be implemented. 

"I’m a bit confused by this. My “apocalypse stories” from the grandparent comment did not assume any competing incentives and mechanisms, right? They were all bad actions that I claim also flowed naturally from self-other-overlap-derived incentives."

What I meant by "competing incentives" is any incentives that compete with the good incentives described by me (other-preservation and sub-agent stability), which could include bad incentives that might also flow naturally from self-other overlap. 

Thanks for watching the talk and for the insightful comments! A couple of thoughts:

  • I agree that mirror neurons are problematic both theoretically and empirically so I avoided framing that data in terms of mirror neurons. I interpret the premotor cortex data and most other self-other overlap data under definition (A) described in your post.
  • Regarding the second point, I think that the issue you brought up correctly identifies an incentive for updating away from "self-other overlap" but this doesn't seem to fully realise in humans and I expect this to not fully realise in AI agents either due to stronger competing incentives that favour self-other overlap. One possible explanation is the attitude for risk incorporated into the subjective reward value. In this paper, in the section "Risky rewards, subjective value, and formal economic utility", it is mentioned that monkeys show nonlinear utility functions that are compatible with risk seeking at small juice amounts and risk avoiding at larger amounts. It is possible that if the reward predictor predicts that "I will be eating chocolate" given that the amount of reward expected is fairly low humans are risk-seeking in that regime and our subjective reward value is high, making us want to take that bet, and given that the reward prediction error might be fairly small, it could potentially be overridden by our subjective reward value influenced by our attitude to risk. Now this might explain how empathic responses from self-other overlap are kept for low-stakes scenarios but there are stronger incentives to update away from self-other overlap in higher-stakes scenarios. Humans seem to have learned to modulate their empathy which might prevent some reward-prediction error. Also it is possible that we have evolved to be more risk-seeking when it comes to empathic concern due to the evolutionary benefits of empathy increasing the subjective reward value of the empathic response due to self-other overlap in higher stakes scenarios but I am uncertain. I am curious what you think about this hypothesis. 
  • I agree that the theory of change that I presented is simplistic and I should have more explicitly stated the key uncertainties of this proposal although I did mention throughout the talk that I do not think that inducing self-other overlap is enough and that we still have to ensure that the set of incentives that shape the agent's behaviour favours good outcomes. What I was trying to communicate in the theory of change section is that self-other overlap sets incentives that favour AI not killing us (self-preservation as an instrumentally convergent goal given which self-other overlap provides an incentive for other-preservation) and sub-agent stability of self-other overlap (due to the AI expecting its self/other-preservation preferences to be frustrated if the agents that it creates, including improved versions of itself, don't have self/other-preservation preferences) but I failed to put enough emphasis on the fact that this will only happen if we find ways to ensure that these incentives dominate and are not overridden by competing incentives and mechanisms. I think that the competing incentives problem is tractable, which is one of the main reasons I believe that this research direction is promising. 

It feels like if the agent is generally intelligent enough hinge beliefs could be reasoned/fine-tuned against for the purposes of a better model of the world. This would mean that the priors from the hinge beliefs would still be present but the free parameters would update to try to account for them at least on a conceptual level. Examples would include general relativity, quantum mechanics and potentially even paraconsistent logic for which some humans have tried to update their free parameters to account as much as possible for their hinge beliefs for the purpose of better modelling the world (we should expect this in AGI as it is an instrumentally convergent goal). Moreover, a sufficiently capable agent could self-modify to get rid of the limiting hinge beliefs for the same reasons. This problem could be averted if the hinge beliefs/priors were defining the agent's goals but goals seem to be fairly specific and about concepts in a world model but hinge beliefs tend to be more general eg. how those concepts relate. Therefore, I'm uncertain how stable alignment solutions that rely on hinge beliefs would be.

Any n-bit hash function will produce collisions when the number of elements in the hash table gets large enough (after the number of possible hashes stored in n bits has been reached) so adding new elements will require rehashing to avoid collisions making GLUT have a logarithmic time complexity in the limit. Meta-learning can also have a constant time complexity for an arbitrarily large number of tasks, but not in the limit, assuming a finite neural network.