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Google apologizes for ‘missing the mark’ after Gemini generated racially diverse Nazis (www.theverge.com)

submitted 8 months ago by [email protected] to c/[email protected]

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Google apologizes for ‘missing the mark’ after Gemini generated racially diverse Nazis::Google says it’s aware of historically inaccurate results for its Gemini AI image generator, following criticism that it depicted historically white groups as people of color.

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[–] [email protected] 16 points 8 months ago (2 children)

Ultimately this is futile though, because you can do that for these two specific prompts until the AI appears to "get it", but it'll still screw up a prompt like "1800s Supreme Court justice" or something because it hasn't been trained on that. Real intelligence requires agency to seek out new information to fill in its own gaps; and a framework to be aware of what the gaps are. Through exploration of its environment, a real intelligence connects things together, and is able to form new connections as needed. When we say "AI doesn't know anything" that's what we mean--understanding is having a huge range of connections and the ability to infer new ones.

[–] [email protected] 9 points 8 months ago

That's why I hate that they started to call them artificial intelligence. There is nothing intelligent in them at all. They work on probability based on a shit ton of data, that's all. That's not intelligence, that's basically brute force. But there is no going back at this point, I know.

[–] [email protected] 1 points 8 months ago* (last edited 8 months ago)

Oh really? Here's Gemini's response to "What would the variety of genders and skin tones of the supreme court in the 1800s have been?"

The Supreme Court of the United States in the 1800s was far from diverse in terms of gender and skin tone. Throughout the entire 19th century, all the justices were white men. Women were not even granted the right to vote until 1920, and there wasn't a single person of color on the Supreme Court until Thurgood Marshall was appointed in 1967.

Putting the burden of contextualization on the LLM would have avoided this issue.