Still wanted to say:

I appreciate the spirit of this comment.

There are trade-offs here.

If it’s simple or concrete like a toy model, then it is not fully specified. If it is fully specified, then the inferential distance of going through the reasoning steps is large (people get overwhelmed and they opt out).

If it’s formalised, then people need to understand the formal language. Look back at Gödel’s incompleteness theorems, which involved creating a new language and describing a mathematical world people were not familiar with. Actually reading through the original paper would have been a toil for most mathematicians.

There are further bottlenecks, which I won’t get into.

For now, I suggest people who care to understand (because everything we care about is at stake) to read this summary post: https://www.lesswrong.com/posts/xp6n2MG5vQkPpFEBH/the-control-problem-unsolved-or-unsolvable [LW · GW]

Anders Sandberg also had an insightful conversation with my research mentor about fundamental controllability limits. I guess the transcript will be posted on this forum somewhere next month.

Again, it’s not simple.

For the effort it takes you to read through and understand parts, please recognise it took a couple of orders of magnitude more effort for me and my collaborators to convey the arguments in a more intuitive digestible form.

• An approach could be to say under what conditions natural selection will and will not sneak in. 

Yes!

• Natural selection requires variation. Information theory tells us that all information is subject to noise and therefore variation across time. However, we can reduce error rates to arbitrarily low probabilities using coding schemes. Essentially this means that it is possible to propagate information across finite timescales with arbitrary precision. If there is no variation then there is no natural selection. 

Yes! The big question to me is if we can reduced error rates enough. And "error rates" here is not just hardware signal error, but also randomness that comes from interacting with the environment.

• In abstract terms, evolutionary dynamics require either a smooth adaptive landscape such that incremental changes drive organisms towards adaptive peaks and/or unlikely leaps away from local optima into attraction basins of other optima. In principle AI systems could exist that stay in safe local optima and/or have very low probabilities of jumps to unsafe attraction basins. 

It has to be smooth relative to the jumps the jumps that can be achieved what ever is generating the variation. Natural mutation don't typically do large jumps. But if you have a smal change in motivation for an intelligent system, this may cause a large shift in behaviour. 

• I believe that natural selection requires a population of "agents" competing for resources. If we only had a single AI system then there is no competition and no immediate adaptive pressure.

I though so too to start with. I still don't know what is the right conclusion, but I think that substrate-needs convergence it at least still a risk even with a singleton. Something that is smart enough to be a general intelligence, is probably complex enough to have internal parts that operate semi independently, and therefore these parts can compete with each other. 

I think the singleton scenario is the most interesting, since I think that if we have several competing AI's, then we are just super doomed. 

And by singleton I don't necessarily mean a single entity. It could also be a single alliance. The boundaries between group and individual is might not be as clear with AIs as with humans. 

• Other dynamics will be at play which may drown out natural selection. There may be dynamics that occur at much faster timescales that this kind of natural selection, such that adaptive pressure towards resource accumulation cannot get a foothold. 

This will probably be correct for a time. But will it be true forever? One of the possible end goals for Alignment research is to build the aligned super intelligence that saves us all. If substrate convergence is true, then this end goal is of the table. Because even if we reach this goal, it will inevitable start to either value drift towards self replication, or get eaten from the inside by parts that has mutated towards self replication (AI cancer), or something like that.

• Other dynamics may be at play that can act against natural selection. We see existence-proofs of this in immune responses against tumours and cancers. Although these don't work perfectly in the biological world, perhaps an advanced AI could build a type of immune system that effectively prevents individual parts from undergoing runaway self-replication. 

Cancer is an excellent analogy. Humans defeat it in a few ways that works together

1. We have evolved to have cells that mostly don't defect
2. We have an evolved immune system that attracts cancer when it does happen
3. We have developed technology to help us find and fight cancer when it happens
4. When someone gets cancer anyway and it can't be defeated, only they die, it don't spread to other individuals. 

Point 4 is very important. If there is only one agent, this agent needs perfect cancer fighting ability to avoid being eaten by natural selection. The big question to me is: Is this possible?

If you on the other hand have several agents, they you defiantly don't escape natural selection, because these entities will compete with each other. 
 

Thanks for the thoughts! Some critical questions:

Natural selection requires variation. Information theory tells us that all information is subject to noise and therefore variation across time.

Are you considering variations introduced during learning (as essentially changes to code, that can then be copied). Are you consider variations introduced by microscopic changes to the chemical/structural configurations of the maintained/produced hardware?

However, we can reduce error rates to arbitrarily low probabilities using coding schemes.

Claude Shannon showed this to be the case for a single channel of communication. How about when you have many possible routing channels through which physical signals can leak to and back from the environment?

If you look at existing networked system architectures, does the near-zero error rates you can correct toward at the binary level (eg. with use of CRC code) also apply at higher layers of abstraction (eg. in detecting possible trojan horse adversarial attacks)?

If there is no variation then there is no natural selection.

This is true. Can there be no variation introduced into AGI, when they are self-learning code and self-maintaining hardware in ways that continue to be adaptive to changes within a more complex environment?

In abstract terms, evolutionary dynamics require either a smooth adaptive landscape such that incremental changes drive organisms towards adaptive peaks…

Besides point-change mutations, are you taking into account exaptation, as the natural selection for shifts in the expression of previous (learned) functionality? Must exaptation, as involving the reuse of functionality in new ways, involve smooth changes in phenotypic expression?

…and/or unlikely leaps away from local optima into attraction basins of other optima.

Are the other attraction basins instantiated at higher layers of abstraction? Are any other optima approached through selection across the same fine-grained super-dimensional landscape that natural selection is selective across? If not, would natural selection “leak” around those abstraction layers, as not completely being pulled into the attraction basins that are in fact pulling across a greatly reduced set of dimensions? Put a different way, can natural selection pull side-ways on the dimensional pulls of those other attraction basins?

I believe that natural selection requires a population of "agents" competing for resources. If we only had a single AI system then there is no competition and no immediate adaptive pressure.

I get how you would represent it this way, because that’s often how natural selection gets discussed as applying to biological organisms.

It is not quite thorough in terms of describing what can get naturally selected for. For example, within a human body (as an “agent”) there can be natural selection across junk DNA that copies itself across strands, or virus particles, or cancer cells. At that microscopic level though, the term “agent” would lose its meaning if used to describe some molecular strands.

At the macroscopic level of “AGI”, the single vs. multiple agents distinction would break down, for reasons I described here [LW(p) · GW(p)].

Therefore, to thoroughly model this, I would try describe natural selection as occurring across a population of components. Those components would be connected and co-evolving, and can replicate individually (eg. as with viruses replacing other code) or as part of larger packages or symbiotic processes of replication (eg. code with hardware). For AGI, they would all rely on somewhat similar infrastructure (eg. for electricity and material replacement) and also need somewhat similar environmental conditions to operate and reproduce.

Other dynamics will be at play which may drown out natural selection…Other dynamics may be at play that can act against natural selection.

Can the dynamic drown out all possible natural selection over x shortest-length reproduction cycles? Assuming the “AGI” continues to exist, could any dynamics you have in mind drown out any and all interactions between components and surrounding physical contexts that could feed back into their continued/increased existence?

We see existence-proofs of this in immune responses against tumours and cancers. Although these don't work perfectly in the biological world, perhaps an advanced AI could build a type of immune system that effectively prevents individual parts from undergoing runaway self-replication.

Immune system responses were naturally selected for amongst organisms that survived.

Would such responses also be naturally selected for in “advanced AI” such that not the AI but the outside humans survive more? Given that bottom-up natural selection by nature selects for designs across the greatest number of possible physical interactions (is the most comprehensive), can alternate designs built through faster but more narrow top-down engineering actually match or exceed that fine-grained extent of error detection and correction? Even if humans could get “advanced AI” to build in internal error detection and correction mechanisms that are kind of like an immune system, would that outside-imposed immune system withstand natural selection while reducing the host’s rates of survival and reproduction [LW · GW]?

~ ~ ~

Curious how you think about those questions. I also passed on your comment to my mentor (Forrest) in case he has any thoughts.