Tuesday, November 19, 2019

What Does Interoperability Mean for the Future of Machine Learning


Interoperability in action: Healthcare

Let’s use healthcare as an example of how interoperable machine learning technology can enhance our lives. Consider high-tech medical procedures like CT scans that automatically generate large volumes of sensor data for a single patient as opposed to health information your doctor manually enters into a proprietary database during a routine check-up. Without a way to quickly and automatically integrate these disparate data types for analysis, there is lost the potential for fast diagnosis of critical illnesses. This has created a demand for optimization across different information models. Current methods and legacy systems simply aren’t friendly in terms of interoperability — but recent developments in machine learning are opening the door for the possibility of stronger, faster translation between information platforms. The result could be vastly improved medical care and optimized research practices.


The role of neural networks

Modeled after the human brain, neural networks are comprised of a set of algorithms that are designed to recognize patterns. They interpret sensory data through a sort of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. According to a 2017 article in MIT News, neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department. Since that time, the approach has fallen in and out of favor, but today it’s making a serious comeback.

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