Researchers, scientists and academics around the world publish roughly 2.5 million scientific papers each year, on top of a backlog of more than 50 million papers dating back to 1665. Plus, the rate at which researchers publish these academic papers keeps rising, a la Moore's Law. It's impossible for scientists to read every paper published in their fields, and searching for a specific study can be a daunting task.
Enter: Paul Allen, Microsoft co-founder and leader of the non-profit Allen Institute for Artificial Intelligence. The Allen Institute's latest effort is Semantic Scholar, a scientific-paper search engine powered by machine learning and other artificial intelligence systems.
Semantic Scholar went live in November 2015 with a focus on computer science papers. Today, the service expanded to include neuroscience, bringing the search engine's database to more than 10 million papers. Semantic Scholar is pitched as a sophisticated alternative to Google Scholar, and it uses AI systems and natural language processing algorithms to help parse each paper.
This is just the beginning for Semantic Scholar. By the end of 2017, Allen and his team plan to incorporate the full library of medical research into the service.