When you think of artificial intelligence, images of futuristic robots or memories of bad sci-fi films might come to mind. However, the reality of AI is actually a lot more tame: a friendly search engine, for instance.
But while we type our queries into Google and usually get fairly useful results, the same has not always been true for the information gleaned by scientific researchers.
Although existing resources like Google Scholar and PubMed provide scientists with resources much faster than the methods of old, they don’t always cover the nitty-gritty details that are needed.
Semantic Scholar has been labeled a game-changer for these professionals, who previously had no way of effectively combing through mountains of dense research. While Google Scholar has a huge database – it has indexed more than 200 million articles to date – it’s lacking in terms of providing access to metadata.
It can help scientists find studies, but it won’t tell them how often a paper or author has been cited. Essentially, it can make a scientist’s job even more difficult because the research tool they’re using isn’t comprehensive.
But Semantic Scholar is different. Developed by Microsoft co-founder Paul Allen in conjunction with his non-profit organization, the Allen Institute for Artificial Intelligence, Semantic Scholar first launched last November. Known as AI2, the non-profit built the engine in collaboration with Allen’s other research organization, the Allen Institute for Brain Science.
Originally launched as a research tool for computer science, Semantic Scholar’s real appeal is its AI-based design.
Instead of simply listing a study’s abstract and bibliographic data,this new search engine is actually able to think and analyzea study’s worth.GeekWirenotes that, “Semantic Scholar uses data mining, natural language processing, and computer vision to identify and present key elements from research papers.”
The engine is able to understand when a paper is referencing its own study or results from another source. Semantic Scholar can then identify important details, pull figures, and compare one study to thousands of other articles within one field.
As it stands now, scientists can use other search engine databases as a jumping-off point, but what they find often requires additional research.
The results don’t give the full picture of a study, its variables, or the overall impact.