The application of technology in scientific research

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Efforts towards building research capacity focus on developing the technical skills of scientists through training. Therefore, to help advance scientific research, it is important to facilitate knowledge building by building infrastructure driven by new technologies.

We’ve reached a point where Artificial Intelligence (AI) can significantly increase our intelligence and help us achieve better results at a faster pace. There is no area where AI has not proved useful. With a focus on enhancing scientific research and upgrading technological capabilities, research capacity building has been adopted as a means of development, with more than 8 million active researchers spending approximately 1.5 trillion on academic research. To complement these efforts, the right tools driven by AI can make a significant difference in how research is performed and how results are obtained quickly.

It required the covid-19 epidemic to make government organizations, NGOs and the public understand the need for speed and accuracy of research. With millions of lives at stake, the slow pace of research in the race to find an antiviral drug or vaccine is particularly difficult.

However, in research, where only significant achievements are celebrated in academic research, many steps and procedures are easily forgotten. Sometimes, these efforts take months before many results are achieved; Often, the results are not revolutionary or do not directly contribute to the betterment of society.

Governments and companies are awakening to the requirement for quicker exploration. Researchers currently spend about four hours a week searching for hundreds of articles. About five hours are spent reading these articles, half of which are not related to a particular area of   research. Technology, especially AI, can come to the rescue.There are a number of tools that can help researchers reduce their reading and search for relevant research faster. These are powered by natural language processing and research based on machine learning.

During the actual research phase, AI open-source tools such as Python, R, Panda and Spark, as well as proprietary AI tools like proprietary, matlab, and SAS can be very useful in machine learning. Many research laboratories are using advanced AI concepts such as computer vision, robotic weapons, IoT, and speech and audio to help collect and run experiments based on different hypotheses; Collect, analyze and demonstrate search output; And arrive at a conclusion.

Finally, for researchers, the publication and dissemination of their research is the final and most important step. This stage is time consuming and difficult, but serious. It determines how and to what extent the search will reach the right audience. There are many AI tools that can be used by researchers to format handwriting, correct grammar and syntax, and target journal standards, as well as automated solution of styling statistics, tables, headlines and references.

Since its inception, CACTUS has been partnering with researchers to support their search journey. It is our constant endeavor to enable researchers and innovators to find suitable concepts and new ideas for different industries and sectors. The organization has entered the space of AI and in-depth training for publishers to develop innovative products as well as business and technology solutions for stakeholders in the research landscape.With the goal of keeping researchers at the center of research, we have launched a powerful initiative called R, which has developed a number of AI-powered tools that simplify the lives of researchers and focus them on actual research. We believe that it won’t be long before researchers fully integrate into the ecosystem.