I have some related discussion in Section 2.1 here. I think if I were writing the list, I would remove the assumption of bad faith from 4, i.e. my 4 choices would be:
They are similar because those answers are convergent.
They are similar because we’re stealing.
They are similar because we’re romantics.
They are not in fact similar. Maybe it’s just marketing, or maybe biology was a source of inspiration during the brainstorming process, or maybe somebody was trying to copy how they thought biology worked but their beliefs about how biology worked were incorrect, or whatever.
And then I think your particular examples are a mix of 1 & 2 & (my now-more-broad) 4.
I feel like it’s 4 ~ 1 > 2 > 3. The example of CNNs seems like this, where the artificial neural networks and actual brains face similar constraints and wind up with superficially similar solutions, but when you look at all the tricks that CNNs use (especially weight-sharing, but also architecture choices, choice of optimizer, etc.) they’re not actually very biology-like, and were developed based on abstract considerations more than biological ones.
I have some related discussion in Section 2.1 here. I think if I were writing the list, I would remove the assumption of bad faith from 4, i.e. my 4 choices would be:
They are similar because those answers are convergent.
They are similar because we’re stealing.
They are similar because we’re romantics.
They are not in fact similar. Maybe it’s just marketing, or maybe biology was a source of inspiration during the brainstorming process, or maybe somebody was trying to copy how they thought biology worked but their beliefs about how biology worked were incorrect, or whatever.
And then I think your particular examples are a mix of 1 & 2 & (my now-more-broad) 4.
I feel like it’s 4 ~ 1 > 2 > 3. The example of CNNs seems like this, where the artificial neural networks and actual brains face similar constraints and wind up with superficially similar solutions, but when you look at all the tricks that CNNs use (especially weight-sharing, but also architecture choices, choice of optimizer, etc.) they’re not actually very biology-like, and were developed based on abstract considerations more than biological ones.
Do you have a granular take about which ones are relatively more explained by each point?