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.
“Immunology” and “well-understood” are two phrases I am not used to seeing in close proximity to each other. I think with an “increasingly” in between it’s technically true—the field has any model at all now, and that wasn’t true in the past, and by that token the well-understoodness is increasing.
But that sentence could also be iterpreted as saying that the field is well-understood now, and is becoming even better understood as time passes. And I think you’d probably struggle to find an immunologist who would describe their field as “well-understood”.
My experience has been that for most basic practical questions the answer is “it depends”, and, upon closet examination, “it depends on some stuff that nobody currently knows”. Now that was more than 10 years ago, so maybe the field has matured a lot since then. But concretely, I expect if you were to go up to an immunologist and say “I’m developing a novel peptide vaccine from the specifc abc surface protein of the specific xyz virus. Can you tell me whether this will trigger an autoimmune response due to cross-reactivity” the answer is going to be something more along the lines of “lol no, run in vitro tests followed by trials (you fool!)” and less along the lines of “sure, just plug it in to this off-the-shelf software”.
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 newerpredictivemodelswhich 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 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.
“Immunology” and “well-understood” are two phrases I am not used to seeing in close proximity to each other. I think with an “increasingly” in between it’s technically true—the field has any model at all now, and that wasn’t true in the past, and by that token the well-understoodness is increasing.
But that sentence could also be iterpreted as saying that the field is well-understood now, and is becoming even better understood as time passes. And I think you’d probably struggle to find an immunologist who would describe their field as “well-understood”.
My experience has been that for most basic practical questions the answer is “it depends”, and, upon closet examination, “it depends on some stuff that nobody currently knows”. Now that was more than 10 years ago, so maybe the field has matured a lot since then. But concretely, I expect if you were to go up to an immunologist and say “I’m developing a novel peptide vaccine from the specifc abc surface protein of the specific xyz virus. Can you tell me whether this will trigger an autoimmune response due to cross-reactivity” the answer is going to be something more along the lines of “lol no, run in vitro tests followed by trials (you fool!)” and less along the lines of “sure, just plug it in to this off-the-shelf software”.
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.