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Steven Niederer, a biomedical engineer at the Alan Turing Institute and Imperial College London, has a cardboard box filled with 3D printed hearts. Each is modeled after the real heart of a person with heart failure, but Niederer is more interested in creating detailed replicas of human hearts using computers.
These “digital twins” are the same size and shape as the real thing. They work the same way. But they exist only virtually. Scientists can perform virtual surgery on these virtual hearts, figuring out the best course of action for the patient’s condition.
After decades of research, models like these are now entering clinical trials and beginning to be used in patient care. The ultimate goal is to create digital versions of our bodies — computer copies that could help researchers and doctors understand our risk of developing various diseases and determine which treatments might work best.
But the new technology will have to be developed very carefully. Read the full story to find out why.
— Jessica Hamzelow
This story is from the upcoming issue of MIT Technology Review, out January 6—it’s all about the exciting discoveries happening in the world right now. If you haven’t already, subscribe to receive future copies.
From there comes the data for building artificial intelligence
AI is all about data. Piles and piles of data are needed to train algorithms to do what we want them to do, and what goes into AI models determines what comes out. But here’s the problem: AI developers and researchers don’t really know much about the data sources they use.
The Data Origins Initiative, a group of over 50 researchers from academia and industry, wanted to fix that. They wanted to know, very simply: where does the data come from to build the AI?
Their findings, which they shared exclusively with MIT Technology Review, show a troubling trend: AI data practices risk a large concentration of power in the hands of a few dominant tech companies. Read the full story.
-Melissa Heikkilä
