![]() ![]() Pre-med students aim to become doctors, but their coursework starts with the basics of chemistry and biology rather than the finer points of diagnosing disease. Rather than learning solely from massive amounts of data and expecting a single generative model to solve all problems, we should train AI by using models that stack on top of each other-first biology, then chemistry, then layer on top of those foundations data points specific to health care or drug design, for example. By studying thousands to millions of labeled data points-examples of “right” and “wrong”-current advanced neural network architectures are able to figure out what makes one choice better than another. This is true for artificial intelligence and people alike, but for AI, the issue is exacerbated by the way it currently learns and how technologists are currently approaching the opportunity and challenge. ![]() It’s particularly challenging to gain the intuition, often acquired through schooling and experience, that helps determine the best answer in a complex situation. It’s a nearly irreplaceable process: Most of the information a medical resident gleans by listening and watching a high-performing surgeon, for example, isn’t spelled out in any textbook. Getting to the top of a field typically begins with years of intensive information upload, often via formal schooling, followed by some form of apprenticeship years devoted to learning, mostly in person, from the field’s most accomplished practitioners. ![]()
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