What happened this week in AI by Louie This week in AI, Gemini’s embarrassing and backfiring attempt to implement DEI (Diversity, Equity, and Inclusion) and counter LLM bias was the center of the debate. Both Gemini’s image and text generation examples were widely ridiculed, including its inability to generate accurate images of historical people — such as images of the U.S. Founding Fathers depicted as American Indian, Black, or Asian. There was also much criticism of examples of Gemini’s inability to make what should be clear-cut moral judgments and examples of political or ideological bias in its responses. Google confirmed that it’s temporarily suspended Gemini’s ability to generate images of people. At the same time, it will work on updating the technology to improve the historical accuracy of outputs involving depictions of humans. We expect part of the issue to come from poorly thought-through system prompts, which should be quick to fix (but slower to test); however, bias built into RLHF fine-tuning datasets is more challenging.
This AI newsletter is all you need #88
This AI newsletter is all you need #88
This AI newsletter is all you need #88
What happened this week in AI by Louie This week in AI, Gemini’s embarrassing and backfiring attempt to implement DEI (Diversity, Equity, and Inclusion) and counter LLM bias was the center of the debate. Both Gemini’s image and text generation examples were widely ridiculed, including its inability to generate accurate images of historical people — such as images of the U.S. Founding Fathers depicted as American Indian, Black, or Asian. There was also much criticism of examples of Gemini’s inability to make what should be clear-cut moral judgments and examples of political or ideological bias in its responses. Google confirmed that it’s temporarily suspended Gemini’s ability to generate images of people. At the same time, it will work on updating the technology to improve the historical accuracy of outputs involving depictions of humans. We expect part of the issue to come from poorly thought-through system prompts, which should be quick to fix (but slower to test); however, bias built into RLHF fine-tuning datasets is more challenging.