Counting on AI: The right time for researchers to embrace Artificial Intelligence

Nishchay Shah

Chief Technology Officer and Head, Emerging Products

July 22, 2020

While Artificial General Intelligence, or “singularity” as they call it, may be decades away, we have already reached a point where AI can significantly augment our intelligence and help us achieve better outputs at a faster pace.

As of today, there is no area where AI has not been proven useful. From playing games to flying airplanes and from detecting cancers to automatically cleaning up selfie-portraits, AI has made its presence felt in all domains.

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.

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

Until a year ago, the general public may not have understood or even paid much heed to the need for speed and accuracy when it comes to research. But because of the recent COVID-19 situation, many are recognizing and feeling the pain of the pace of research in the race to find an antiviral drug or a vaccine.

While some of the results of academic research are celebrated, it is easy to forget the countless steps and processes behind the scenes, which last many months before any results are achieved; more often than not, the results of research aren’t always revolutionary or directly useful.

One of the early stages of the research lifecycle is discovery. On average, researchers spend 4 hours every week searching through research and 5 hours reading articles, with only 50% of the articles being useful. Here, AI can come in to help researchers discover the right articles to read.

There are many tools out therethat 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, analyzing, and representing the research outputs; and arriving at the conclusions.

For the above steps, 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.

While there are editing services that exist to help with manuscript preparation, formatting, and language correction, there are many AI tools out there that can be used by researchers, which help with writing manuscripts, correcting grammar and language, and formatting them as per target journal standards, in addition to automated solutions for styling figures, tables, captions, and citations. is an online suite of assistive tools that helps researchers make their manuscripts publication–ready.

Since its inception, Cactus has been partnering with researchers to assist them in their research journey. It has been our constant endeavor to enable researchers and innovators to find analogous concepts and novel ideas from different industries and fields.

We are excited to have entered the AI and deep–learning space as well, as the need of the hour is to develop innovative products for publishers as well as business and tech solutions for stakeholders in the research landscape.

With powerful initiatives like, our aim is to put the researcher at the center of research. We have already developedseveral AI–powered tools that help researchers focus on their main work, the research. As a community, however, we still have a long way to go before AI is fully integrated in the researcher’s ecosystem.

The author of the article is Nishchay Shah, Chief Technology Officer, Cactus Communications


Article originally published by DataQuest on (July 21, 2020)

By Nishchay Shah


Chief Technology Officer and Head, Emerging Products

Nishchay oversees technology and innovation across products and brands at CACTUS. Experienced in handling tech-budgeting, outsourcing, and global tech recruitment, Nishchay manages a large department with over 250 experts working in product management, software development, UX, DevOps, Digital innovation, and Machine Learning. He focuses on creating, translating, and mobilizing big-picture visions downstream. Nishchay has over 15 years of experience in software development and technology and strives to stay on the bleeding edge of innovation. He has worked in the US for over a decade, and has handled diverse teams in the US, Belarus, Bulgaria, India, and the UK. Having successfully led both B2C and B2B product teams in the past, Nishchay has a thorough understanding of the end-to-end product and technology life cycles. He has a master’s degree from the University of Bridgeport, Connecticut, specializing in Computer Networks and Database Systems.

Connect with CACTUS

Contact us for all media inquiries

Contact Us