Specific vs. General Artificial Intelligence

The most recent episode of the Ezra Klein podcast includes an interview with Google’s head of DeepMind, Demis Hassabis, whose AlphaFold project was able to use artificial intelligence to predict the shape of proteins essential for addressing numerous genetic diseases, drug development, and vaccines.

Before the AlphaFold project, human scientists, after decades of work, had solved around 150,000 proteins. Once AlphaFold got rolling, it solved 200 million protein shapes, nearly all proteins known, in about a year.

I enjoyed the interview because it focused on Artificial Intelligence to solve specific problems (like protein folds) instead of one all-knowing AI that can do anything. At some point in the future, a more generic AI will be useful, but for now, these smaller specific AI projects seem the best path. They can help us solve complex problems while at the same time being constrained to just those problems while we humans figure out the big-picture implications of artificial intelligence.