Well done! I read the original paper (twice now), and the approach is a better demonstration one of the evolutionary algorithms in continuously self-updating ML models.
Two comments/questions:
1. Why are so few GPTs and GANs designed to use continuously updated training? High fitness in a specific (niche) environment has been known to lead to the extinction of species for over 100 years. The fossil record is replete with examples of this.
Homo Sapiens was but one of 17 species in the genus Homo. Now there are but one: Us. Why? The impact of evolutionary pressures on maximizing fitness at the species level rather than the individual level. This of course is highly analogous to the pre-training vs. meta learning strategies discussed in you (excellent) post.
Graphics and memes on point as always, Devansh
Devansh -
Well done! I read the original paper (twice now), and the approach is a better demonstration one of the evolutionary algorithms in continuously self-updating ML models.
Two comments/questions:
1. Why are so few GPTs and GANs designed to use continuously updated training? High fitness in a specific (niche) environment has been known to lead to the extinction of species for over 100 years. The fossil record is replete with examples of this.
2. I suggest anyone who wants to know how humans came to use novel evolutionary algorithms (speaking teleologically, of course) read the terrific book “Sapiens: A Brief History of Humankind” by Yuval Noah Harari (see: https://www.amazon.com/Sapiens-Humankind-Yuval-Noah-Harari-ebook/dp/B00ICN066A/ref=sr_1_1?crid=1BYQJAPW4MQUD&keywords=sapiens+a+brief+history+of+mankind&qid=1701171557&s=digital-text&sprefix=Sapie%2Cdigital-text%2C91&sr=1-1
Homo Sapiens was but one of 17 species in the genus Homo. Now there are but one: Us. Why? The impact of evolutionary pressures on maximizing fitness at the species level rather than the individual level. This of course is highly analogous to the pre-training vs. meta learning strategies discussed in you (excellent) post.
Bill Lambos