Whether in business or biology, complex systems repeatedly exhibit platform-application dynamics, with separation between broadly useful, hard-to-implement components in the platform, and highly adaptive, easy-to-implement systems on top. Why is this the case? It’s bad for every use case to reinvent the wheel, and it’s also bad if a slow development process prevents the discovery of new niches. This post explores this dynamic in both nature and technology.
Octopus heavily rely on post-transcriptional RNA modification to adapt to their environment, an approach that's more error-prone than DNA editing but leads to faster iteration times. An intermediate data point between evolution of genes and the adaptation of brains!
Thanks Evelyn! I think you're pointing to an interesting distinction, where for human-produced app-platform systems, there is marked cost difference between the two, and this makes it harder to produce new hardware. For living systems, all genetic changes are just as "costly", but the more platform-y systems are just in very deep and wide local minima, requiring many genetic changes for improvement that are progressively improbable. I do look forward to humans having a way to evolve the whole stack, but that would likely require full programmability of matter itself (and, I suppose, some smart way of navigating this extremely large search space - see AI Drug discovery via search algorithms for some cool inspiration there, maybe fodder for a new post!).
This is so interesting! If we have the comparison of app vs. platform, or app vs. hardware, where would be the limit to expanding the app to the maximum capacity of the platform/hardware? It would be cool if over time, the selection of app-programming in code could generationally modify the platform or the hardware, as does natural selection, with genetic code.
Also, another cool example: https://www.sciencealert.com/octopus-and-squid-evolution-is-weirder-than-we-could-have-ever-imagined
Octopus heavily rely on post-transcriptional RNA modification to adapt to their environment, an approach that's more error-prone than DNA editing but leads to faster iteration times. An intermediate data point between evolution of genes and the adaptation of brains!
Thanks Evelyn! I think you're pointing to an interesting distinction, where for human-produced app-platform systems, there is marked cost difference between the two, and this makes it harder to produce new hardware. For living systems, all genetic changes are just as "costly", but the more platform-y systems are just in very deep and wide local minima, requiring many genetic changes for improvement that are progressively improbable. I do look forward to humans having a way to evolve the whole stack, but that would likely require full programmability of matter itself (and, I suppose, some smart way of navigating this extremely large search space - see AI Drug discovery via search algorithms for some cool inspiration there, maybe fodder for a new post!).
This is so interesting! If we have the comparison of app vs. platform, or app vs. hardware, where would be the limit to expanding the app to the maximum capacity of the platform/hardware? It would be cool if over time, the selection of app-programming in code could generationally modify the platform or the hardware, as does natural selection, with genetic code.