ARTIFICIAL INTELLIGENCE
Why a Little-Known Mathematical Formula is Driving Many AI Systems BY KEITH DARLINGTON
When I first started working in AI about 35 years ago, I was intellectually dazzled with the application of one technique. It was called Bayesian inference –based upon a mathematical formula, called Bayes theorem, which was conceived by a British clergyman named Thomas Bayes in the 18th Century. It was being used very successfully in expert systems –a successful branch of AI in the 1980s. What struck me about this technique was the way that a mathematical formula could mimic –and improve upon in many cases –human experts in their decision-making processes. As a graduate mathematician, I was familiar with and had used Bayes in other types of problem-solving. But its use in emulating human expertise was new to me. I found it impressive because human experts would be unlikely to have the knowledge of the mathematics underlying Bayesian inference.
42 | iQ March 2022
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et nowadays it is ubiquitous in the world of AI. It is being used to predict the spread of the Omicron pandemic, diagnose health problems, and help with various applications on our smartphones without us even realizing it. In machine learning systems today, Bayesian inference is more prominent than ever because, given the right conditions, it can mimic the way that human decision-making involving uncertainty exceptionally well. How could that be? The reason is that experts subconsciously learn to assign reasonably good weightings to evidence that enable them to do intuitive calculations in problemsolving. But Bayesian inference can do such calculations –very precisely.
