In the case of DeepMind’s chess beauty program, researchers were able to reduce this by explicitly programming for more diversity. But even with vast training data, probabilistic output, and diversity filters, it’s not easy to mimic the variation and range of human thought.
To be sure, LLMs and AI more broadly are not the only technology to struggle to capture the diversity of the human experience. Take the algorithmic, winner-take-all dynamics of social media, in which conforming to what the average user wants gets you more clicks, attention, and money. To avoid falling into the pull of a mono-voice and monoculture, we must seek out diversity in our sources, prompts, and input. As Haruki Murakami wrote: “If you only read the books that everyone else is reading, you can only think what everyone else is thinking.”
Like chess engines, LLMs will only get better, and we have to prepare for that future. Chess has been wrestling with trying to keep the game fair despite superhuman AI for decades. Electronic devices have long been prohibited in chess competitions, but that has not stopped cheating from disrupting the field.
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