AI development feels more similar to biology than to chemistry. Bright 11th graders shouldn’t be doing experiments on culturing some previously unculturabke pathogen which would be a good bioweapon target and discussing their results, since the field is wide and shallow and it’s not entirely impossible that their experiments are novel. On the other hand, if they’re running basic experiments on culturing some specific common bacterium (e.g. e coli) better, they probably don’t need to worry about accelerating bioweapon development even if there is a chance of them making a slight advancement to the field of biology as a whole.
The nanogpt speedrun feels more like developing better methods to culture e coli at a hobbyist level, and quite unlikely to lead to any substantial advancement applicable to the operational efficiency of well-funded companies at the frontier. Still, it probably is worth keeping track of when the work you’re doing approaches the “this is actually something novel the frontier labs might use” mark, particularly if it’s something more substantial than “here’s how to use the hardware more efficiently to train this particular model”.
AI development feels more similar to biology than to chemistry. Bright 11th graders shouldn’t be doing experiments on culturing some previously unculturabke pathogen which would be a good bioweapon target and discussing their results, since the field is wide and shallow and it’s not entirely impossible that their experiments are novel. On the other hand, if they’re running basic experiments on culturing some specific common bacterium (e.g. e coli) better, they probably don’t need to worry about accelerating bioweapon development even if there is a chance of them making a slight advancement to the field of biology as a whole.
The nanogpt speedrun feels more like developing better methods to culture e coli at a hobbyist level, and quite unlikely to lead to any substantial advancement applicable to the operational efficiency of well-funded companies at the frontier. Still, it probably is worth keeping track of when the work you’re doing approaches the “this is actually something novel the frontier labs might use” mark, particularly if it’s something more substantial than “here’s how to use the hardware more efficiently to train this particular model”.