Waking up to reality. No, not that one. We’re still dreaming.
The world is full of scale-free regularities that pop up across topics not unlike 2+2=4 does. Ever since I learned how common and useful this is, I’ve been in the habit of tracking cross-domain generalizations. That bit you read about biology, or psychology, or economics, just to name a few, is likely to apply to the others in some fashion.
ETA: I think I’m also tracking the meta of which domains seem to cross-generalize well. Translation is not always obvious but it’s a learnable skill.
Did you write this reply using a different method? It has a different feel than the original post.Partway through reading your post, I noticed that reading it felt similar to reading GPT-3-generated text. That quality seems shared by the replies using the technique. This isn’t blinded so I can’t rule out confirmation bias.ETA: If the effect is real, it may have something to do with word choice or other statistical features of the text. It takes a paragraph or two to build and shorter texts feel harder to judge.
If AI alignment were downstream of civilization alignment, how could we tell? How would the world look different if it were/were not?If AI alignment is downstream of civilization alignment, how would we pivot? I’d expect at least some generalizability between AI and non-AI alignment work and it would certainly be easier to learn from experience.
Yeah, there were important changes. I’m suggesting that most of their long-term impact came from enabling the bootstrapping process. Consider the (admittedly disputed) time lag between anatomical and behavioral modernity and the further accelerations that have happened since.ETA: If you could raise an ape as a child, that variety of ape would’ve taken off.
Upgrading a primate didn’t make it strongly superintelligent relative to other primates. The upgrades made us capable of recursively improving our social networking; that was what made the difference.If you raised a child as an ape, you’d get an ape. That we seem so different now is due to the network effects looping back and upgrading our software.
Are you ontologically real or distinct from the sum of your parts? Do you “care” about things only because your constituents do?I’m suggesting precisely that the group-network levels may be useful in the same sense that the human level or the multicellular-organism level can be useful. Granted, there’s more transfer and overlap when the scale difference is small but that in itself doesn’t necessarily mean that the more customary frame is equally-or-more useful for any given purpose.Appreciate the caring-about-money point, got me thinking about how concepts and motivations/drives translate across levels. I don’t think there’s a clean joint to carve between sophisticated agents and networks-of-said-agents.Side note: I don’t know of a widely shared paradigm of thought or language that would be well-suited for thinking or talking about tall towers of self-similar scale-free layers that have as much causal spillover between levels as living systems like to have.
The network results are no different from the sum of behaviors of the components (in the same sense as they work out the same in the brain). I was surprised to realize just how simple and general the principle was.ETA: On closer reading, I may have answered somewhat past your question. Yes, changes in connectivity between nearby nodes affects the operation of those nodes, and therefore the whole. This is equally true in both cases as the abstract network dynamic is the same.
You seem to be focused on the individual level? I was talking about learning on the level of interpersonal relationships and up. As I explain here, I believe any network of agents does Hebbian learning on the network level by default. Sorry about the confusion.Looking at the large scale, my impression is that the observable dysfunctions correspond pretty well with pressures (or lack thereof) organizations face, which fits the group-level-network-learning view. It seems likely that the individual failings, at least in positions where they matter most, are downstream of that. Call it the institution alignment problem if you will.I don’t think we have a handle on how to effectively influence existing networks. Forming informal networks of reasonably aligned individuals around relatively object-level purposes seems like a good idea by default.
Edit: On reflection, in many situations insulation from financial pressures may be a good thing, all else being equal. That still leaves the question of how to keep networks in proper contact with reality. As our power increases, it becomes ever easier to insulate ourselves and spiral into self-referential loops.If civilization really is powered by network learning on the organizational level, then we’ve been doing it exactly wrong. Top-down funding that was supposed to free institutions and companies to pursue their core competencies has the effect of removing reality-based external pressures from the organization’s network structure. It certainly seems as if our institutions have become more detached from reality over time.Have organizations been insulated from contact with reality in other ways?
If existing intelligence works the way I think it does, “small and secret” could be a very poor approach to solving an unreasonably difficult problem. You’d want a large, relatively informal network of researchers working on the problem. The first challenge, then, would be working out how to begin to align the network in a way that lets it learn on the problem.There’s a curious self-reflective recursivity here. Intuitively, I suspect the task of aligning the reseach network would turn out isomorphic to the AI alignment problem it was trying to solve.
If we’re in a world where EY is right, we’re already dead. Most of the expected value will be in the worlds where alignment is neither guaranteed nor extremely difficult.By observation, entities with present access to centralized power, such as governments, corporations, and humans selected for prominent roles in them, seem relatively poorly aligned. The theory that we’re in a civilizational epoch dominated by Molochian dynamics seems like a good fit for observed evidence: the incentive landscapes are such that most transferable resources have landed in Moloch-aligned hands.First impression: distributing the AI among Moloch-unaligned actors seems like the best actionable plan to escape the Molochian attractor. We’ll spin up the parts of our personal collaborative networks that we trust and can rouse on short notice and spend a few precious hours trying to come up with a safer plan before proceeding.***ETA: That’s what I would say on the initial phone call before I have time to think deeply and consider contextual information not included in the prompt. For example, the leak, as it became public, could trigger potentially destabilizing reactions from various actors. The scenario could diverge quickly as more minds got on the problem and more context became available.
My main read is that the situation is hard to read in this regard. On one hand, the baseline public signaling seems to have intensified and decisionmaking seems to have degraded further. On the other hand (based largely on intuitions with too many inputs to list), my sense is that most likely explanations involve evaporative cooling of group beliefs and public-impression based preference cascades. I’d expect a tipping point of some kind but its exact nature and timing are harder to predict.
Sure, I’ll be careful. I only need it for my expedition to the Platonic Realm anyway.
walks into the magic shopHello, I’d like to commission a Sword of Carving at the Joints.
Yeah, I don’t see much reason to disagree with that use of “egregore”.I’m noticing I’ve updated away from using references to any particular layer until I have more understanding of the causal patterning. Life, up to the planetary and down to the molecular, seems to be a messy, recursive nesting of learning networks with feedbacks and feedforwards all over the place. Too much separation/focus on any given layer seems like a good way to miss the big picture.
Kinda valid but I personally prefer to avoid “egregore” as a term. Too many meanings that narrow it too much in the wrong places.Eg. some use it specifically to refer to parasitic memeplexes that damage the agency of the host. That cuts directly against the learning-network interpretation IMO because independent agency seems necessary for the network to learn optimally.
I think we have an elephant in the room. As I outlined in a recent post, networks of agents may do Hebbian learning as inevitably as two and two makes four. If this is the case, there are some implications.If a significant fraction of human optimization power comes from Hebbian learning in social networks, then the optimal organizational structure is one that permits such learning. Institutional arrangements with rigid formal structure are doomed to incompetence.If the learning-network nature of civilization is a major contributor to human progress, we may need to revise our models of human intelligence and strategies for getting the most out of it.Given the existence of previously understudied large-scale learning networks, it’s possible that there already exist agentic entities of unknown capability and alignment status. This may have implications for the tactical context of alignment research and priorities for research direction.If agents naturally form learning networks, the creation and proliferation of AIs whose capabilities don’t seem dangerous in isolation may have disproportionate higher-order effects due to the creation of novel large-scale networks or modification of existing ones.It seems to me that the above may constitute reason to raise an alarm at least locally. Does it? If so, what steps should be taken?
This caught my eye:
There is a large gap between the accomplishments of humans and chimpanzees, which Yudkowsky attributes this to a small architectural improvement
Based on my recent thinking, the big amplifier may have been improvements in communication capacity, making human groups more flexible and effective learning networks than had previously existed. The capacity of individual brains may not have mattered as much as is usually thought.