Using Artificial Intelligence In Academic Research

There are over 8 million active researchers who collectively spend over $1.5 trillion on academic research, with the promise of advancing the world’s combined knowledge and intellect and Artificial Intelligence (AI) has already reached a point where it can significantly improve our intelligence and help us achieve better outputs at a faster pace.

In the past decade, AI and machine learning have transformed several industries and the disruptive technology of AI is making it easier and faster to automate several processes. 

In the academic publishing industry, AI-based technologies are being developed and implemented to assist both authors and publishers in tackling issues related to peer review, searching published content, detecting plagiarism, and identifying data fabrication. AI can not only help expedite scientific communication but also reduce human bias.

Similarly, the application of AI in research has grown tremendously with a focus on automation of research techniques from generating a hypothesis to conducting experiments. In fact, researchers are now being able to address complex problems in biomedical sciences, drug combinations, and predicting diseases using AI.

The right AI powered tools and techniques can make a significant difference in how research is conducted and how fast results are obtained.

But because of the recent COVID-19 situation, many people are now recognising and feeling the pace of research in the race to find an antiviral drug or a vaccine. On average, researchers spend 4 hours every week searching through research and 5 hours reading articles, with only 50% of the articles being useful. AI can come in to help researchers discover the right articles to read.

There are many tools out there that are powered by natural language processing and search based on machine–learned concepts, which help researchers narrow down their reading and discover the relevant research much faster. 

The next stage is the actual research, which consists of gathering data; running experiments based on various hypotheses; collecting, analysing, and representing the research outputs; and arriving at the conclusions. Many AI open–source tools, such as Python, R, Pandas, Scikit, and Spark, as well as proprietary AI tools like Mathematica, Matlab, and SAS can be very useful, especially when directed toward statistical machine learning.

Many research labs are making use of advanced AI streams such as computer vision, robotic arms, IOT, and speech and audio to assist them in the research process.  

Finally, the most important stage for researchers is the publication and dissemination of their research, the tedious and time–consuming albeit critical final step of the process. Pub-sure.com is an online suite of assistive tools that helps researchers make their manuscripts publication ready. Google has launched a hieroglyphics translator that uses machine learning to decode ancient Egyptian language.

Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input. The program replicated decades of AI research in a matter of days, and its designers think that one day, it could discover new approaches to AI.

DQ India:         ENAGO:      BBC:        Science Mag: 

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