If humans don’t know how to solve a problem, no artificial intelligence can magically create the missing knowledge to solve it for us. We have driverless cars because we know how to drive cars. Computers are world chess champions because we know how to play chess. We have factory robots because we know how to do their jobs.
If only we could say the same about treating diabetes.
No doctor can give us a formula to calculate exactly how much insulin to take in any given situation. Ditto how much food to eat, how much activity to do, and so on. Each of us figures it out as we go, and for most of us, it’s different every day. I think we’d all like a robot to take this job, but unfortunately until human beings know more about how to treat diabetes, we will not get one. I wrote about this in my post about the artificial pancreas few months ago.
Investors often challenge me: “surely big data like WatsonHealth will eventually solve diabetes with AI?” I so wish that were true. But since no one understands diabetes, no one really knows what data would be needed to create and teach AI to solve diabetes. And the likelihood that the answer just happens to be in that data — no matter how big — is nil. In fact, I have a sizeable set of my own diabetes data from the past 23 years, and even as a specialist in AI and robotics, it’s no help to me in figuring how much insulin I need to take.
But the good news is there’s a massive body of diabetes knowledge and data that humans (and therefore AI) have yet to explore: the expertise of everyone living with diabetes. All the little tips and tricks and rules of thumb you come up with everyday to keep yourself going (your heuristics). Capturing the data to formalise this knowledge gives us a very good shot at creating and teaching AI to solve diabetes. It can be done. We just have to start with the right understanding.