My apologies, I've only skimmed, but maybe this encapsulates the main idea of your post? """ How MLPs could be trained was an open problem at the time, I think

My apologies, I've only skimmed, but maybe this encapsulates the main idea of your post? """ How MLPs could be trained was an open problem at the time, I think, but the field could easily have chugged along and we’d have been where we are now, just 30 years earlier. """ The main idea here is that we got an AI winter because expectations were too high and the reality didn't meet them? I think this is fundamentally missing why we're making rapid progress now. In my opinion, the AI winter wasn't primarily because of a backlash against overhyped promises, it was because computation just wasn't there. We're just now starting to get access to the compute and storage needed, at a price point that's reasonable, to make progress in these areas. In other words, the AI winter came about because the ground wasn't fertile. Now that we have economic massive computational resources, we're in an AI spring. As a back of the envelope calculation, the human brain is estimated at around 2.5 Pb of storage [0]. At today's prices, that works out to about $50k. The cost of 2.5Pb of storage in 1969 was around $675B [1]. [0] https://www.scientificamerican.com/article/what-is-the-memory-capacity/ https://www.scientificamerican.com/article/what-is-the-memor... [1] https://ourworldindata.org/grapher/historical-cost-of-computer-memory-and-storage?country=~OWID_WRL https://ourworldindata.org/grapher/historical-cost-of-comput...