A radical new Neural Network could overcome big challenges in AI

Researchers borrowed equations from calculus to redesign the core machinery of deep learning so it can model a radical new Neural Network and continuous processes like changes in health.

David Duvenaud was collaborating on a project involving medical data when he ran up against a major shortcoming in AI.

An AI researcher at the University of Toronto, he wanted to build a deep-learning model that would predict a patient’s health over time. But data from medical records is kind of messy: throughout your life, you might visit the doctor at different times for different reasons, generating a smattering of measurements at arbitrary intervals. A traditional neural network struggles to handle this. Its design requires it to learn from data with clear stages of observation. Thus it is a poor tool for modeling continuous processes, especially ones that are measured irregularly over time.

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David Duvenaud was collaborating on a project involving medical data when he ran up against a major shortcoming in AI. An AI researcher at the University of Toronto, he wanted to build a deep-learning model that would predict a patient’s health over time. But data from medical records is kind of messy: throughout your life, you might visit the…

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