AI and scientific literature work in sync

When scholars choose a topic to work on their research, they need more sources or materials to review literature and add more value to their findings. According to Canadian science publishing’s article from last year, 2.5 million research papers are published annually while another unidentified source suggests that new researches are published around the world; approximately 1 million each year! Which is equal to one every 30 seconds. With the overload of new papers in each field and more growing every year it is practically impossible for scholars to keep with the information that is put out in each paper. Christian Berger’s team from the University of Gothenburg in Sweden, found a staggering number of papers on the subject; more than 10,000 in the same subject. Fortunately, the team had the support of an AI system, a writing investigation tool called Iris.ai.

Iris.ai is an AI, a tool developed for scholars to make writing research papers easier. It is a Berlin-based company that claims to save 90% of time with 85% precision of data matching, has more than 70 m open access papers. Iris.ai is programmed to learn about the topic provided and perform an elaborate frequency analysis over the text. Then it read the words for which it needs to find results and additional material that could be helpful for the paper. It uses a 500-word description of the researcher’s issue, or the link of their paper and the AI restores a guide to thousands of coordinating reports. As the website suggests, it is a scientific writing assistant.

According to Berger, it was “a quick and nevertheless precise overview of what should be relevant to a certain research question”. Iris.ai is one among many of the new AI-based tools offering targeted results of the knowledge landscape. One such tool is called Semantic Scholar, produced by the Allen Institute for Artificial Intelligence in Seattle, Washington, and Microsoft Academic.

Although every instrument is different from each other and gives different output, they all provide researchers with a different look at the scientific literature than conventional tools such as PubMed and Google Scholar. Semantic Scholar is a browser-based search tool that mimics the engines like Google and it is free. But it is more informative than Google Scholar in terms of specific results required by researchers. Doug Raymond, Semantic Scholar’s general manager, says that one million individuals utilize their service every month. It uses natural language processing or NLP to extract data while building connections to determine if the information is relevant and reputable or not.

Artificial intelligence is saving a lot of time and making it easier and quicker to automate some procedures. In the academic publishing industry, the Al-based innovations are being produced and implemented to help both authors and publishers for peer reviews, searching published content, detecting plagiarism, and identifying data fabrication. AI could be costly, but it can accelerate a researchers’ access to new fields. More and more such AI tools are being developed to cater to various requisites of writing a paper, such as filtering topics for relevance, keyword search, etc.

Experts who need more assistance for their specific concerns might consider free Al­ tool such as Microsoft Academic or Semantic Scholar. While AI is easing so many burdens and saving time for a researcher, let’s not forget that it is still machine intelligence and may require human intervention here and there to make a paper more presentable and precise.


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