  Data-driven artificial intelligence models fed with published scientific
findings have been used to create powerful prediction engines for scientific
and technological advance, such as the discovery of novel materials with
desired properties and the targeted invention of new therapies and vaccines.
These AI approaches typically ignore the distribution of human prediction
engines -- scientists and inventor -- who continuously alter the landscape of
discovery and invention. As a result, AI hypotheses are designed to substitute
for human experts, failing to complement them for punctuated collective
advance. Here we show that incorporating the distribution of human expertise
into self-supervised models by training on inferences cognitively available to
experts dramatically improves AI prediction of future human discoveries and
inventions. Including expert-awareness into models that propose (a) valuable
energy-relevant materials increases the precision of materials predictions by
~100%, (b) repurposing thousands of drugs to treat new diseases increases
precision by 43%, and (c) COVID-19 vaccine candidates examined in clinical
trials by 260%. These models succeed by predicting human predictions and the
scientists who will make them. By tuning AI to avoid the crowd, however, it
generates scientifically promising "alien" hypotheses unlikely to be imagined
or pursued without intervention, not only accelerating but punctuating
scientific advance. By identifying and correcting for collective human bias,
these models also suggest opportunities to improve human prediction by
reformulating science education for discovery.
