The big difference that makes this analogy kinda fall flat for me, is that evolution is a slow, blind, undirected process. It works by trying lots of dumb stuff, most of which a deliberate designer wouldn’t try because it would be obviously bad. Sometimes it gets lucky.
Machine learning algorithms, which are the relevant machines here, aren’t progressing in this pattern of dumb experiments which occasionally get lucky. The scientific and engineering frontiers of machine learning are being expanded in extremely deliberate ways by a large number of agents optimizing for various goals. The patterns which emerge from these two processes are very different. The constraints of DNA and heredity don’t apply to memetic advances like algorithmic improvements. Memetic advances spread far more rapidly through the human population.
Talking about the evolution of organizations makes slightly more sense, since they do tend to get trapped into certain patterns by bureaucratic inertia, and then unsuccessful companies die out, and new ones are more likely to be built in the pattern of successful companies. Still though, a pretty inexact comparison.
I think your observation that biological evolution is a slow, blind, and undirected process is fair. We try to make this point explicit in our section on natural selection (as a main evolutionary selection pressure for biological evolution) where we say “The natural processes for succeeding or failing in survival and reproduction – natural and sexual selection – are both blind and slow.”
For our contribution here we are not trying to dispute this. Instead we’re seeking to find analogies to the ways in which machine evolution, which we define as “the process by which machines change over successive generations,” may have some underlying similar mechanisms that we can apply to understand how machines change over successive generations.
To your point that, “Machine learning algorithms, which are the relevant machines here, aren’t progressing in this pattern of dumb experiments which occasionally get lucky,” I agree. To understand this process better and as distinct from biological evolution and natural selection, we propose the notion of artificial selection. The idea of artificial selection is that machines are responding in part to natural selection pressures but that the evolutionary pressures are different here, which is why we give them a different name. We describe artificial selection in a way that I think corresponds closely to your concern. We say:
“For an analogy to natural selection we have chosen the term artificial selection which is driven in large part by human culture, human artifacts, and individual humans.… Artificial selection also highlights the ways in which this selection pressure applies more generally to human artifacts. Human intention and human design have shifted the pace of evolution of artifacts, including machines, rocketing forward by comparison to biological evolution.”
All of this to say, I agree that the comparison is pretty inexact. We were not going for an exact comparison. We were attempting to make it clear that machines and machine learning are influenced by a very different evolutionary selection process, which should lead to different expectations about the process by which machines change over successive generation. Our hope was not for the analogy to be exact to biological evolution, but rather to use components of biological evolution such as natural selection, inheritance, mutation, and recombination as familiar biological processes to explore potential parallels to machine evolution.
The big difference that makes this analogy kinda fall flat for me, is that evolution is a slow, blind, undirected process. It works by trying lots of dumb stuff, most of which a deliberate designer wouldn’t try because it would be obviously bad. Sometimes it gets lucky.
Machine learning algorithms, which are the relevant machines here, aren’t progressing in this pattern of dumb experiments which occasionally get lucky. The scientific and engineering frontiers of machine learning are being expanded in extremely deliberate ways by a large number of agents optimizing for various goals. The patterns which emerge from these two processes are very different. The constraints of DNA and heredity don’t apply to memetic advances like algorithmic improvements. Memetic advances spread far more rapidly through the human population.
Talking about the evolution of organizations makes slightly more sense, since they do tend to get trapped into certain patterns by bureaucratic inertia, and then unsuccessful companies die out, and new ones are more likely to be built in the pattern of successful companies. Still though, a pretty inexact comparison.
Thanks for the comment!
I think your observation that biological evolution is a slow, blind, and undirected process is fair. We try to make this point explicit in our section on natural selection (as a main evolutionary selection pressure for biological evolution) where we say “The natural processes for succeeding or failing in survival and reproduction – natural and sexual selection – are both blind and slow.”
For our contribution here we are not trying to dispute this. Instead we’re seeking to find analogies to the ways in which machine evolution, which we define as “the process by which machines change over successive generations,” may have some underlying similar mechanisms that we can apply to understand how machines change over successive generations.
To your point that, “Machine learning algorithms, which are the relevant machines here, aren’t progressing in this pattern of dumb experiments which occasionally get lucky,” I agree. To understand this process better and as distinct from biological evolution and natural selection, we propose the notion of artificial selection. The idea of artificial selection is that machines are responding in part to natural selection pressures but that the evolutionary pressures are different here, which is why we give them a different name. We describe artificial selection in a way that I think corresponds closely to your concern. We say:
All of this to say, I agree that the comparison is pretty inexact. We were not going for an exact comparison. We were attempting to make it clear that machines and machine learning are influenced by a very different evolutionary selection process, which should lead to different expectations about the process by which machines change over successive generation. Our hope was not for the analogy to be exact to biological evolution, but rather to use components of biological evolution such as natural selection, inheritance, mutation, and recombination as familiar biological processes to explore potential parallels to machine evolution.