IMAGE: Screenshot from d-cell.ucsd.edu, where researchers can use DCell, a new virtual yeast cell developed at UC San Diego School of Medicine. view more
Credit: UC San Diego Health
“It seems like every time you turn around, someone is talking about the importance of artificial intelligence and machine learning,” said Trey Ideker, PhD, University of California San Diego School of Medicine and Moores Cancer Center professor. “But all of these systems are so-called ‘black boxes.’ They can be very predictive, but we don’t actually know all that much about how they work.”
Ideker gives an example: machine learning systems can analyze the online behaviors of millions of people to flag an individual as a potential “terrorist” or “suicide risk.” “Yet we have no idea how the machine reached that conclusion,” he said.
For machine learning to be useful and trustworthy in health care, Ideker said, practitioners need to open up the black box and understand how a system arrives at a decision.
Machine learning systems are built on layers of artificial neurons, known as a neural network. The layers are tied together by seemingly random connections between neurons. The systems “learn” by fine-tuning those connections.
Ideker’s research team recently
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