I think you misunderstood my initial comment, the point I was trying to make is that it's the amplification of the abilities of bad actors that should be of concern, not AI going rogue and deciding to exterminate the human race.
If one were to actually try to do such a thing you wouldn't need a LLM. For a very crude pipeline, you would need a good sequence to structure method such as Alphafold 2 (or maybe you can use a homology model), some thermodynamically rigorous protein-protein binding affinity prediction method (this is the hardest part) and an RL process like a policy gradient with an action space over possible single point sequence mutations in the for-example spike protein of SARS to maximize binding affinity (or potentially minimize immunogenicity, but that's far harder).
But I digress, the technology isn't there yet, neither for an LLM to write that sort of code or the in-silico methods of modeling aspects of the viral genome. But we should consider one day it may be and that it could result in the amplification of the abilities of a single bad actor or enable altogether what was not possible before due to a lack of technology.
I probably misunderstood the details of where you think AI will accelerate things. You are worried about AI predicting things like protein structure, binding affinity, and immunogenicity. And using that info to do RL and find a sequence, basically doing evolution in silico. Is this a better representation? That it reduces the search space, requiring less real experiments?
I am basically just skeptical these kinda of reductive predictions will eliminate all of the rate limiting steps of synthetic virology. The assumptions of the natural language input are numerous and would need to be tested in a real lab.
Also, we can already do serial passaging where we just manipulate the organism/environment interaction to make a virus more dangerous. We dont need AI; evolution can do all the hard stuff for you.
If one were to actually try to do such a thing you wouldn't need a LLM. For a very crude pipeline, you would need a good sequence to structure method such as Alphafold 2 (or maybe you can use a homology model), some thermodynamically rigorous protein-protein binding affinity prediction method (this is the hardest part) and an RL process like a policy gradient with an action space over possible single point sequence mutations in the for-example spike protein of SARS to maximize binding affinity (or potentially minimize immunogenicity, but that's far harder).
But I digress, the technology isn't there yet, neither for an LLM to write that sort of code or the in-silico methods of modeling aspects of the viral genome. But we should consider one day it may be and that it could result in the amplification of the abilities of a single bad actor or enable altogether what was not possible before due to a lack of technology.