I think that there are a lot of recent cases where implementation of clearly useful innovations were ridicoulusly slow. For example, cryonics was well known from 1960s (and was first suggested even in 18 century) - but still is mostly ignored. Even I knew the idea it 20 year before Michael Darwin personaly persued me that it is right.
And it gives me another explanation of slowing acceptance of the new ideas. Basically, it is based on the oversimplification that humans are neural nets mostly repating what they learned on their training dataset. If the dataset is not including many instances of cryonics, they will ignore the idea. Humans could be trained to make innovations, like combining words “uber”, “crypto” and “blockchain” and call it “my startup”, but it is limited type of innovations.
Some people are more able to think not based on their training dataset, but to understand the nature of the human intelligence we should study evolution of the whole human dataset.
I think that there are a lot of recent cases where implementation of clearly useful innovations were ridicoulusly slow. For example, cryonics was well known from 1960s (and was first suggested even in 18 century) - but still is mostly ignored. Even I knew the idea it 20 year before Michael Darwin personaly persued me that it is right.
And it gives me another explanation of slowing acceptance of the new ideas. Basically, it is based on the oversimplification that humans are neural nets mostly repating what they learned on their training dataset. If the dataset is not including many instances of cryonics, they will ignore the idea. Humans could be trained to make innovations, like combining words “uber”, “crypto” and “blockchain” and call it “my startup”, but it is limited type of innovations.
Some people are more able to think not based on their training dataset, but to understand the nature of the human intelligence we should study evolution of the whole human dataset.