I said:
disanalogies listed here aren’t in and of themselves reasons that similar strategies cannot sometimes be useful, once the limitations are understood. For that reason, disanalogies should be a reminder and a caution against analogizing, not a reason on its own to reject parallel approaches in the different domains.
You seem to be simultaneously claiming that I had plenty of room to make a more nuanced argument, and then saying you think I'm saying something which exactly the nuance I included seems to address. Yes, people could cite the title of the blog post to make a misleading claim, assuming others won't read it - and if that's your concern, perhaps it would be enough to change the title to "Biorisk is Often an Unhelpful Analogy for AI Risk," or "Biorisk is Misleading as a General Analogy for AI Risk"?
I agree that we do not have an exact model for anything in immunology, unlike physics, and there is a huge amount of uncertainty. But that's different than saying it's not well-understood; we have clear gold-standard methods for determining answers, even if they are very expensive. This stands in stark contrast to AI, where we don't have the ability verify that something works or is safe at all without deploying it, and even that isn't much of a check on its later potential for misuse.
But aside from that, I think your position is agreeing with mine much more than you imply. My understanding is that we have newer predictive models which can give uncertain but fairly accurate answers to many narrow questions. (Older, non-ML methods also exist, but I'm less familiar with them.) In your hypothetical case, I expect that the right experts can absolutely give indicative answers about whether a novel vaccine peptide is likely or unlikely to have cross-reactivity with various immune targets, and the biggest problem is that it's socially unacceptable to assert confidence in anything short of tested and verified case. But the models can get, in the case of the Zhang et al paper above, 70% accurate answers, which can help narrow the problem for drug or vaccine discovery, then they do need to be followed with in vitro tests and trials.
I'm arguing exactly the opposite; experts want to make comparisons carefully, and those trying to transmit the case to the general public should, at this point, stop using these rhetorical shortcuts that imply wrong and misleading things.
On net, the analogies being used to try to explain are bad and misleading.
I agree that I could have tried to convey a different message, but I don't think it's the right one. Anyone who wants to dig in can decide for themselves, but you're arguing that ideal reasoners won't conflate different things and can disentangle the similarities and differences, and I agree, but I'm noting that people aren't doing that, and others seem to agree.
I don't understand why you disagree. Sure, pathogens can have many hosts, but hosts generally follow the same logic as for humans in terms of their attack surface being static and well adapted, and are similarly increasingly understood.
That doesn't seem like "consistently and catastrophically," it seems like "far too often, but with thankfully fairly limited local consequences."
BSL isn't the thing that defines "appropriate units of risk", that's pathogen risk-group levels, and I agree that those are are problem because they focus on pathogen lists rather than actual risks. I actually think BSL are good at what they do, and the problem is regulation and oversight, which is patchy, as well as transparency, of which there is far too little. But those are issues with oversight, not with the types of biosecurity measure that are available.
If you're appealing to OpenPhil, it might be useful to ask one of the people who was working with them on this as well.
And you've now equivocated between "they've induced an EA cause area" and a list of the range of risks covered by biosecurity - not what their primary concerns are - and citing this as "one of them." I certainly agree that biosecurity levels are one of the things biosecurity is about, and that "the possibility of accidental deployment of biological agents" is a key issue, but that's incredibly far removed from the original claim that the failure of BSL levels induced the cause area!
I mean, I'm sure something more restrictive is possible.
But what? Should we insist that the entire time someone's inside a BSL-4 lab, we have a second person who is an expert in biosafety visually monitoring them to ensure they don't make mistakes? Or should their air supply not use filters and completely safe PAPRs, and feed them outside air though a tube that restricts their ability to move around instead? (Edit to Add: These are already both requires in BSL-4 labs. When I said I don't know of anything more restrictive they could do, I was being essentially literal - they do everything including quite a number of unreasonable things to prevent human infection, short of just not doing the research.)
Or do you have some new idea that isn't just a ban with more words?
"lists of restrictions" are a poor way of managing risk when the attack surface is enormous
Sure, list-based approaches are insufficient, but they have relatively little to do with biosafety levels of labs, they have to do with risk groups, which are distinct, but often conflated. (So Ebola or Smallpox isn't a "BSL-4" pathogen, because there is no such thing. )
I just meant "gain of function" in the standard, common-use sense—e.g., that used in the 2014 ban on federal funding for such research.
That ban didn't go far enough, since it only applied to 3 pathogen types, and wouldn't have banned what Wuhan was doing with novel viruses, since that wasn't working with SARS or MERS, it was working with other species of virus. So sure, we could enforce a broader version of that ban, but getting a good definition that's both extensive enough to prevent dangerous work and that doesn't ban obviously useful research is very hard.
I agree with you that analogies are needed, but they are also inevitably limited. So I'm fine with saying "AI is concerning because its progress is exponential, and we have seen from COVID-19 that we need to intervene early," or "AI is concerning because it can proliferate as a technology like nuclear weapons," or "AI is like biological weapons in that countries will pursue and use these because they seem powerful, without appreciating the dangers they create if they escape control." But what I am concerned that you are suggesting is that we should make the general claim "AI poses uncontrollable risks like pathogens do," or "AI needs to be regulated the way biological pathogens are," and that's something I strongly oppose. By ignoring all of the specifics, the analogy fails.
In other words, "while I think the disanalogies are compelling, comparison can still be useful as an analytic tool - while keeping in mind that the ability to directly learn lessons from biorisk to apply to AI is limited by the vast array of other disanalogies."