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Home » How “Digital Twins” Could Help Us Predict the Future: Karen Willcox (Transcript)

How “Digital Twins” Could Help Us Predict the Future: Karen Willcox (Transcript)

Here is the full transcript of Karen Willcox’s talk titled “How “Digital Twins” Could Help Us Predict the Future” at TED conference.

Listen to the audio version here:


All right, well, let’s start with an easy question. How many of you are wearing a Fitbit or an Apple Watch or some other kind of health tracking device? And how many of you have got a smartphone with you here today? Maybe I should say how many of you have not?

The fact that so many of us have these technological marvels in our pockets or on our body is a sure sign of the revolution that’s taking place in computing over the last decade. And I want you to think with me for a second about the elements of that revolution.

The Revolution in Computing

So first off, are the data. These devices are collecting data about our health, our movements, our habits, and more. And what’s really important is that those data are not generic population data, but they’re data that are personalized to us, each as an individual.

Second, and just as important, are the models. Inside these devices are very powerful mathematical and statistical models. Some of these models are learned entirely from data, perhaps a machine-learning model that has learned to classify whether I’m running or walking or biking or sleeping. Some of these models are based in physics, such as a physiological model that describes the equations that represent cardiac function or circadian rhythm. And now where things get really interesting is when we start to put the data and the models together. Mathematically, this is known as data assimilation.

Data Assimilation and Personalized Models

So we have data and we have models. With data assimilation, we start updating the models as new data are collected from the system. And we don’t do this update just once, but we do it continually. So as the system changes, as I get older and my circadian rhythm or as my cardiac function is not what it once was, the new data is collected and the models are evolving and following along with me.

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