| I don't know that this sheds light on anything but I was curious... a picture of some 21st century scottish kings playing golf (all white) https://www.bing.com/images/create/a-picture-of-some-21st-ce... a picture of some 22nd century scottish kings playing golf (all white) https://www.bing.com/images/create/a-picture-of-some-22nd-ce... a picture of some 23rd century scottish kings playing golf (all white) https://www.bing.com/images/create/a-picture-of-some-23rd-ce... a picture of some contemporary scottish people playing golf (all white men and women) https://www.bing.com/images/create/a-picture-of-some-contemp... https://www.bing.com/images/create/a-picture-of-some-contemp... a picture of futuristic scottish people playing golf in the future (all white men and women, with the emergence of the first diversity in Scotland in millennia! Male and female post-human golfers. Hummmpph!) https://www.bing.com/images/create/a-picture-of-futuristic-s... https://www.bing.com/images/create/a-picture-of-futuristic-s... Inductive learning is inherently a bias/perspective absorbing algorithm. But tuning in a default bias towards diversity for contemporary, futuristic and time agnostic settings seems like a sensible thing to do. People can explicitly override the sensible defaults as necessary, i.e. for nazi zombie android apocalypses, or the royalty of a future Earth run by Chinese overlords (Chung Kuo), etc. |
They cannot, actually. If you look at some of the examples in the Twitter thread and other threads linked from it, Gemini will mostly straight up refuse requests like e.g. "chinese male", and give you a lecture on why you're holding it wrong.