Using AI to advance scientific research
25 June 2021
Artificial Intelligence (AI) is revolutionising many areas of human endeavour, and scientific research is no exception. While efforts toward building scientific research tend to focus on developing scientists’ technical competencies, it is also important to facilitate knowledge creation by building infrastructure powered by new technologies.
Here Nishchay Shah, Chief Technology Officer at Cactus Communications, a technology company accelerating scientific advancement, explains the benefits of using AI for scientific research.
AI can benefit all stages of the research process – from browsing through thousands of academic papers to building prototypes, there is no lack of applications for AI-based tools. The right AI-powered tools can effectively support researchers and make a significant difference in how their work is conducted and how quickly results are obtained.
The need for faster results
It took the COVID-19 pandemic for government organisations, NGOs, and the public to understand the need for speed and accuracy in research. When millions of lives are at stake, the slow pace of research in the race to find an antiviral drug or vaccine hits especially hard.
However, in academic research, only select landmark achievements are celebrated. As a result, the wider public might easily forget that these achievements are the result of steps and processes that take months of painstaking work. Powered by natural language processing and machine learning algorithms, AI-based tools could contribute to speeding up these steps and freeing researchers from time-consuming tasks that could be easily automated.
AI to the rescue
One area where AI could help is bibliographical research. Researchers currently spend close to four hours a week searching through hundreds of articles. About five hours are then invested in reading these articles, half of which turn out to be irrelevant to the specific project on which they are working. AI can come to the rescue, helping researchers narrow down their reading and discover relevant papers much faster.
AI can also benefit later stages of the research process. For example, open-source tools like Python, R, Pandas and Spark, as well as proprietary tools like Mathematica, MATLAB, and SAS can be very useful in statistical machine learning. Several research labs are also using advanced AI-based technologies such as computer vision, robotic arms, and IoT-connected devices to assist in gathering data and running experiments based on various hypotheses; collecting, analysing and illustrating research output; and drawing conclusions.
Finally, for researchers, the publication and dissemination of their research is the final and most important stage of scientific endeavour. This step can be time-consuming and tedious, but it is critical in determining how and to what extent research will reach the right audience. AI tools can help speed up this process without compromising on quality by assisting researchers to write manuscripts, correct grammar and syntax, and format papers according to target journal standards, as well as encompassing automated solutions for styling figures, tables, captions, and citations.
Cactus has entered the AI and deep-learning space to develop innovative products for researchers as well as publishers and other stakeholders in the research landscape. With this in mind, Cactus has rolled out a powerful initiative called R (researcher.life), an ecosystem integrating six tools and platforms to cater to the needs of academic researchers, from literature search to collaboration.
As AI grows in popularity, advanced tools are already becoming available to scientific researchers to enhance their work and make the next landmark breakthrough.
To find out more about R (researcher.life) and other AI-based tools for scientific research, visit www.cactusglobal.com.