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Neither artificial intelligence designed to play Turing's imitation game, noraugmented intelligence built to maximize the human manipulation of informationare tuned to accelerate innovation and improve humanity's collective advanceagainst its greatest challenges. We reconceptualize and pilot beneficial AI toradically augment human understanding by complementing rather than competingwith human cognitive capacity. Our approach to complementary intelligencebuilds on insights underlying the wisdom of crowds, which hinges on theindependence and diversity of crowd members' information and approach. Byprogrammatically incorporating information on the evolving distribution ofscientific expertise from research papers, our approach follows thedistribution of content in the literature while avoiding the scientific crowdand the hypotheses cognitively available to it. We use this approach togenerate valuable predictions for what materials possess valuableenergy-related properties (e.g., thermoelectricity), and what compounds possessvaluable medical properties (e.g., asthma) that complement the human scientificcrowd. We demonstrate that our complementary predictions, if identified byhuman scientists and inventors at all, are only discovered years further intothe future. When we evaluate the promise of our predictions withfirst-principles equations, we demonstrate that increased complementarity ofour predictions does not decrease and in some cases increases the probabilitythat the predictions possess the targeted properties. In summary, by tuning AIto avoid the crowd, we can generate hypotheses unlikely to be imagined orpursued until the distant future and promise to punctuate scientific advance.By identifying and correcting for collective human bias, these models alsosuggest opportunities to improve human prediction by reformulating scienceeducation for discovery.

Complementary artificial intelligence designed to augment human discovery