<![CDATA[Machine Learning’s Next Trick Will Transform How Research Is Done]]>
Machine Learning’s Next Trick Will Transform How Research Is Done

“Though research is a slow moving and rigid process, one study shows that the rate of scientific study has exploded in the last 50 years. According to the paper, humanity’s scientific output now doubles every nine years. Considering the rigors of science — that’s pretty fast. And it’s just the average rate. In specific areas like healthcare, the doubling rate is even faster — as much as every 3 years currently with an expected increase to every 73 days by the early 2020s.

For overwhelmed researchers navigating the growing stack of science literature — the value isn’t in having so much new information, but finding relevant insights when they need them.

Jacobo Elosua and the team at Iris hope recent advances in machine learning AI might be one way through the noise. Machine learning is powerful because it allows programmers to assign a task to an algorithm — in this case, combing through scientific literature — and then let the code teach itself to improve its model as it is fed more data over time.

Iris works by reading scientific papers and learning to determine what’s being discussed in the text. The goal is to augment the discovery process by leading researchers to relevant papers and new discoveries as they are published. By identifying emerging trends and concepts within the areas of science that may impact a researcher’s domain of interest, AIs can shoulder some of the burden of constantly scanning new literature…”

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