![]() ![]() For instance, this might be the database of historical “Jeopardy!” questions, as well as the correct answers. #BLOCK WORLD PROBLEM IN AI EXAMPLE HOW TO#To learn how to do anything, AI needs tons of prior data with known outcomes. The set of algorithms that conquered Go, a strategy board game, and “Jeopardy!” have accomplishing impressive feats, but they are still just (very complex) pattern recognition. ![]() Journalists that breathlessly cover the “AI that predicted coronavirus” and the quants on Twitter creating their first-ever models of pandemics should take heed: There is no value in AI without subject-matter expertise. These approaches are very, very different. In contrast to AI models that only learn patterns from historical data, epidemiologists are building statistical models that explicitly incorporate a century of scientific discovery. However, this should not be confused for AI predicting the spread of COVID-19 on its own. It is certainly the case that some of the epidemiological models employ AI. “There is no value in AI without subject-matter expertise.” The field has developed extensive knowledge of its particular problems, such as how to consider community factors in the rate of disease transmission, that most computer scientists, statisticians, and machine learning engineers will not have. Simple mathematical models of smallpox mortality date all the way back to 1766, and modern mathematical epidemiology started in the early 1900s. In the case of predicting the spread of COVID-19, look to the epidemiologists, who have been using statistical models to examine pandemics for a long time. Likewise, it always requires subject matter expertise to know if models will continue to work in the future, be accurate on different populations, and enable meaningful interventions. Effectively predicting a badly defined problem is worse than doing nothing at all. Despite all the talk of algorithms and big data, deciding what to predict and how to frame those predictions is frequently the most challenging aspect of applying AI. No matter what the topic, AI is only helpful when applied judiciously by subject-matter experts-people with long-standing experience with the problem that they are trying to solve. And so, framed around examples from the COVID-19 outbreak, here are eight considerations for a skeptic’s approach to AI claims. ![]() In fact, the COVID-19 AI-hype has been diverse enough to cover the greatest hits of exaggerated claims around AI. Twitter various news articles have dramatized the role AI is playing in the pandemic by overstating what tasks it can perform, inflating its effectiveness and scale, neglecting the level of human involvement, and being careless in consideration of related risks. ![]()
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