![]() We can’t let fear of perpetrating human biases limit our willingness to explore how AI can help us democratize the expertise of our human specialists, who are unfortunately unscalable.HAL 9000 first appeared in the 1968 film as a member of the ship’s crew. But AI is less of a black box than popular imagination suggests the human decision-making we depend on today, as I’ve noted previously, is arguably more opaque. ![]() The most complex and uncertain aspects of addressing health and medicine simply don’t exist fully in the world of bits.Įxposing these specialist AIs to the perspective of a diverse range of top practitioners will be a must to avoid replicating dangerous biases. We should be equipping our most skilled human specialists with wearables to gather nuanced, real-world interactions for AI to learn from, just as our up-and-coming academic and industry stars do. In parallel, we must rip AI from its online moorings and plunge it into the world of atoms. I anticipate the creation of not a single specialist AI but many, with a diversity of approaches in coding, data, and testing, such that these models could provide a second (or third, or fourth) opinion when necessary. And, in fact, we need specialist AIs in specific domains more than an overarching AI that can do anything an average human can do. Ironically, creating an AI that specializes in a particular domain such as health care may be easier to create than something more akin to HAL 9000, with typical human-level knowledge across fields. But, whereas humans have a visual cortex and a motor cortex, AI could have a biology cortex and a drug design cortex-in both cases, neural architectures specialized for specific tasks. These stacked models could develop in ways analogous to cortexes in the human brain. I believe this may initially parallel human education and educational paradigms, but will likely in time specialize to develop new types of expertise in AI learning. For example, estrogen and testosterone differ only slightly, but have dramatically different impacts on human health.ĭeveloping these stacked AI models with hierarchies of latent spaces-simplified maps of complex data to help AI models understand patterns and relationships-would reflect an understanding or predictive capability for each foundational element. ![]() This style of learning can help develop a sense for how to navigate decisions involving subtle differences, which, especially at the molecular scale, really matter. Similarly, a scientist who designs a new therapeutic undergoes years of studying chemistry and biology, followed by PhD studies, followed by working under the tutelage of expert drug designers. Without those foundational courses, their ability to one day provide high-quality health care would face significant limits. 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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