I am a longtime LessWrong and SSC reader who finally got around to starting a blog. I would love to hear feedback from you! https://harsimony.wordpress.com/
harsimony
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Is it fair to say that this is similar to Richard Kennaway’s point? If so, see my response to their comment.
I agree with you and Richard that nanotechnology still presents a catastrophic risk, but I don’t believe nanotechnology presents an existential risk independent of AI (which I could have stated more clearly!).
These are good points.
The scenarios you present would certainly be catastrophic (and are cause for great concern/research about nanotechnology), but could all of humanity really be consumed by self replicators?
I would argue that even if they were maliciously designed, self replicators would have to outsmart us in some sense in order to become a true existential risk. Simply grabbing resources is not enough to completely eliminate a society which is actively defending a fraction of those resources, especially if they also have access to self-replicators/nanotechnology (blue goo) and other defense mechanisms.
If we assume that a very smart and malevolent human is designing this grey goo, I suspect they could make something world ending.
I agree, conditional on the grey goo having some sort of intelligence which can thwart our countermeasures.
A parallel scenario occurs when a smart and malevolent human designs an AI (which may or may not choose to self replicate). This post attempts to point out that these two situations are nearly identical, and that the existential risk does not come from self-replication or nanotechnology themselves, but rather from the intelligence of the grey goo. This would mean that we could prevent existential risks from replication by copying over the analogous solution in AI safety: to make sure that the replicators decision-making remains aligned with human interests, we can apply alignment techniques; to handle malicious replicators, we can apply the same plan we would use to handle malicious AI’s.
I agree. I think humanity should adopt some sort of grey-goo-resistant shelter and, if/when nanotechnology is advanced enough, create some sort of blue-goo defense system (perhaps it could be designed after-the-fact in the shelter).
The fact that these problems seem tractable, and that -in my estimation- we will achieve dangerous AI before dangerous nanotechnology suggests to me that preventing AI risk should have priority over preventing nanotechnology risks.
I appreciate your feedback!
On reflection, I agree that this post could benefit from a clearer example framed as a decision. Of course, I have also sidestepped the discussion of ethical premises.
More context in population ethics would be nice but I wanted to avoid focusing on the Repugnant Conclusion because the goal was to examine a more neglected question: how do we trade off utility between existing lives and potentially-existing lives?
This is no different than any other resource allocation question, is it?
Sort of! It is mostly a resource allocation question if you are willing to trade utility between existing lives and potentially-existing lives (as I am). But many people disagree with this instinctively. Additionally, there are network effects to consider, in scenario 2 there are a lot more possible activities which are not possible in scenario 1.
I don’t have concrete answers on all of these questions, but most people seem to have a strong presumption for a world more like scenario 1, which seems unjustified.
I completely agree and I support further research on radical life extension (we might as well have the option and decide on the ethics later).
This is not a near-term issue in any sense, rather, it is more of an “ethical brainteaser” to identify different intuitions people have.
Yes I definitely agree that this is not an argument against research on life extension.
Merging is very interesting, it seems that it would also prevent AI’s or humans who are self-copying too much. Agents could also merge in a temporal sense by say, alternating the days that they run, or by running more slowly (and thus using less resources).
Do you have a good system for saving and prioritizing the ideas you have?
Building a habit of noticing a new idea and writing it down (even if it’s silly) has increased my overall output dramatically.
Is it known whether predictive coding is easier to train than backprop? Local learning rules seem like they would be more parallelizable.
Every Model Learned by Gradient Descent Is Approximately a Kernel Machine seems relevant to this discussion:
We show, however, that deep networks learned by the standard gradient descent algorithm are in fact mathematically approximately equivalent to kernel machines, a learning method that simply memorizes the data and uses it directly for prediction via a similarity function (the kernel). This greatly enhances the interpretability of deep network weights, by elucidating that they are effectively a superposition of the training examples. The network architecture incorporates knowledge of the target function into the kernel.
Good point!
Do you know of any work that applies similar methods to study the equivalent kernel machine learned by predictive coding?
Good points. I would imagine that all of these scenarios are made possible by an intelligence advantage, but I did not make that explicit here.
Your point about multitasking (if I understood it correctly) is important too. We can imagine an unfriendly-AI pursuing all 3 paths to existential catastrophe simultaneously. The question becomes, are there prevention strategies for combinations of existential-risk-paths which work better than simply trying to prevent individual paths? I have to think on that more.
Is there a generalization of “sufficient statistic” that applies to summaries which grow as the log (or polynomial of the log) in the size of the data?
Despite the fact that your summary keeps growing, this seems like it might also be a useful definition. In the limit of infinite data, a log(n) summary is vanishingly small relative to the size of the data you have.
I see! To reiterate: for a fixed n, having a smaller summary dimension means that the distribution of those n points is part of the exponential family.
It seems like there are three cases here:
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The size of the summary remains constant as number n of samples grows. The distribution of these samples remains constant. Once enough data is collected, you can estimate the summary statistics and infer the overall distribution.
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The size of the summary grows as O(log(n)). The distribution of these samples changes for each n as the size of the summary grows. You converge on the correct distribution in the limit of infinite n. (This seems weird/incorrect, I might be missing something here. I am trying to think of a concrete example)
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degenerate case: the size of the required “summary” grows faster than O(n) so you are better off just using the data itself as the summary.
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I wrote about something similar that I call counterfactual contracts. I focused on what a person should do strategically and came to a similar conclusion, namely, that a person should both reward actions with good intent somewhat, while also punishing actions that end up being bad for you.
This resolution is weirder to the extent that the punishment is in the form of social disgrace and the like. It’s one thing to charge Bob money for his harms to cows, and another to go around saying ‘Bob made the best altruistic decisions he could, and I would do the same in his place. Also I do think he’s contemptible.’
I think the overall weirdness of gossip-based reputation is doing a lot of work here? Though community members can hear the whole story and make nuanced updates about Bob, I think this framework makes more sense in the context of monetary punishments (or an automated reputation system).
I agree, I want to see a lot more crowdfunding projects for global problems especially. I think things like crowdfunding are great, and if a public good can be provided this way, its usually easier/simpler than retroactive funding.
That being said, I still think there are a few cases where retroactive funding and prizes can be useful. For example, these mechanisms can allow people to finance the creation of music they like before hearing the song itself!
I talk about this in more depth here and it seems that Vitalik Buterin is working on an implementation of something similar.
I don’t expect retroactive funding/prizes to be as important as crowdfunding, but I think having a diverse set of goods-funding mechanisms is important.
I like the promotions example. It can be framed as a company “sticking their neck out” for a particular customer, and the customer can reciprocate by buying the item if they actually like it, and not buying it if the ad/promotion is a waste of their time.
Yeah framing the decision theory problems as counterfactual contracts is more of a stretch, not sure if its actually useful.
I generally agree. It seems unlikely to me that Cryosleep will be developed or in use for very long before brain emulations or AI become dominant. But like you point out, a lot of the benefits listed here would apply to brain emulations too.
Even if it won’t be useful for long, cryonics research seems like an important precursor to Em’s. Developing tools to preserve/image the brain, determining which brain structures are important to preserve, and finding ways to upload organic minds will all be important.
Cool project! I was thinking along similar lines when I recently went through a bunch of his posts to find interesting ideas and collected them:
https://harsimony.wordpress.com/2021/10/20/robin-hansons-other-ideas/
Whoever decides to work on this project up might be able to use this as a starting point.
Nice! Bryan Caplan did a “Theory and Practice of Oligarchical Collectivism Book Club” which I enjoyed as well:
I wrote about the literature around the evolution of virulence previously which people might find useful for finding references on the topic.
In short, it is complicated:
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zoonotic diseases (like COVID) don’t follow a consistent pattern early in their evolution because they are so far removed from being adapted to humans as a niche.
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As long as there is a correlation between deadly-ness and ability to reproduce, diseases will face evolutionary pressure to become more/less deadly depending on the circumstances. This is strongly influenced by transmission mode (but isn’t the only factor):
a. Disease transmitted by close person-to-person contact → disease can’t make host too sick, or host won’t come into contact with others → disease evolves to cause milder infection.
Example: HIV has relatively few effects on hosts for years before culminating in AIDS.
b. Disease transmission doesn’t require person-to-person contact → disease can optimize other features of transmission such as producing many copies and using host resources → disease evolves to cause severe infection.
Example: malaria is spread via mosquitos and doesn’t require person-to-person contact in order to spread, which might explain why it is such a debilitating disease.
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That’s true. I guess I should have clarified that the argument here doesn’t exclude nanotechnology from the category of catastrophic risks (by catastrophic, I mean things like hurricanes which could cause lots of damage but could not eliminate humanity) but rules out nanotechnology as an existential risk independent from AI.
Lots of simple replicators can use up the resources in a specific environment. But in order to present a true existential risk, nanotechnology would have to permanently out-compete humanity for vital resources, which would require outsmarting humanity in some sense.