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For machine learning experts, it’s an exciting time to be in the field. As the technology matures and becomes more affordable and more accessible, its number of uses and possible applications are growing exponentially.

At Aspire Ventures, we’ve been anticipating this moment, as we’ve been developing and deploying our own machine learning tools to solve problems in healthcare, finance, and other impact industries. 

Previously in healthcare, the potential uses for machine learning have been inadequately explored. But now we’re finally beginning to see studies that validate what we’ve been saying all along: machine learning has the power to make healthcare more efficient, improve diagnostics and outcomes, and transform the way we deliver care.

In a study from Indiana University that was released today, researchers concluded that machine learning could outperform humans in cancer surveillance. The study found that existing algorithms and open-sourced machine learning tools were as good as, or better than, humans for reviewing and detecting cancer cases using data from free-text pathology reports. Using a computing approach to detect cancer cases was also found to be faster and less resource intensive.

That’s just one way that computers can help healthcare professionals save time and money, researchers say. In a healthcare system that is “awash in data”, nearly every part of the care continuum could benefit from machine learning and artificial intelligence.

The power of machine learning to disrupt just about every industry is reaching a critical moment. In an insightful article by Sanjit Dang in readwrite, Dang explores how it is “making the transition from specialized, expensive-to-develop code to a general-purpose technology.”

Dang fully anticipates that machine learning will become the next great commodity. As the use of cloud computing, mobile phones, and IoT devices explodes, machine learning tools will become indispensable for making use of the resulting deluge of data. 

“Until now, the hot topic of conversation has been how to analyze information and take action based on the results,” Dang says. “But the volume of data has become so great, and its trajectory so steep, that we need to automate many of those actions. Now.” 

Indeed, the growing use of machine learning to handle more and more complex tasks is inevitable. It's an exciting time for the field, and we’re proud to be part of the next big movement in computing.