I can say this with confidence: In 2014, I was the least popular manager at CACTUS.

I joined CACTUS in 2014 as head of the technology department. Back then, we were a small team of 10 engineers, pretty much a start-up. As I started working with and getting to know each team member, I came to the conclusion that they did not have the right skills for the job.

My mandate was to scale up CACTUS’s capabilities in technology. CACTUS was gunning for a foothold in the technology landscape, and we had to build our competencies quickly. I didn’t see that happening—not with this team, at least. The scope of their solutions was limited. They were fixing bugs daily and showed a tendency to resort to standard protocol instead of demonstrating out-of-the-box problem solving with an eye on long-term solutions.

As I spent more time with the team, it became clear that the issue had little to do with attitude. They were a talented bunch, but they had no motivation for upskilling. Skills can be built easily, but building a culture of learning is far more challenging.

I love engineering and tech solutioning. It’s an obsession. I would be happy to spend all my time doing this and keep getting better at it. I doubt I would ever want to give up coding even as I take on the senior-most role in tech. This is how I have built my skills by myself.

I realized that some team members had been assigned managerial roles too early in their career; as a result, they no longer rolled up their sleeves and got down to brass tacks. They failed to explore new code, new technologies, new standards, and new ways of looking at things. They had stopped learning and growing because they didn’t explore solutions themselves.

This wasn’t right, I thought. This wasn’t the way to grow a business. This is not how a technology team should function.

A few months later, I called everyone in for a team meeting and dropped the bomb. I stripped them of their titles. “From today, each one of you is just an engineer,” I announced. I told them they had to start from scratch, as junior engineers, and work their way up the ladder. They had to learn, experiment, and think beyond their limitations. They had to prove that they deserved their titles.

Not surprisingly, the reaction was unfavorable. They complained that the decision was unfair.

I stuck to my guns. I knew what I was doing. Deep down, I felt they also knew I was right. I knew they weren’t satisfied with who they were. I was confident they would take up this challenge. They wanted to be better, and here was the opportunity.

Today, Technology is a large team of 200+ product developers, engineers, project managers, and R&D specialists. But everyone from the original ragtag team of 10 is still with us today, with all of them playing much larger and more critical roles, overseeing different functions. I have immense trust in them and full faith in their capabilities, and it gives me great pride to see them push their teams to do more, do better, and grow each day.

This writeup is part of a series called The CACTUS Way Leader Stories. The CACTUS Way is what we collectively call our 8 guiding principles. We asked the senior cadre at CACTUS to share personal experiences where they practiced or identified with these guiding principles. These stories of struggle and reinvention, despair and hope, conflict and collaboration, and self-doubt and self-discovery make for an interesting read.

Learn more about The CACTUS Way here: https://cactusglobal.com/careers/about-us/culture-and-values/

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We have reached a point where Artificial Intelligence (AI) can significantly augment our intelligence and help us achieve better outputs at a faster pace. There is no area where AI has not been proven useful. With an eye on enhancing scientific research and upgrading technological capabilities, research capacity building has been adopted as a tool for development, with close to $1.5 trillion spent on academic research by over 8 million active researchers.To supplement these efforts, the right AI-powered tools can make a significant difference in how research is conducted and how quickly results are obtained.

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 strikes especially hard.

However, in research, where only a few landmark achievements in academic research are celebrated, many steps and processes are easily forgotten. Sometimes, these efforts take months before any results are achieved; more often than not, the results aren’t revolutionary or don’t directly contribute to the betterment of society.

Governments and corporations are waking up to the need for faster research. Researchers currently spend close to four hours a week searching through hundreds of articles. About five hours are invested in reading these articles, with half of the articles turning out to be irrelevant to the specific field of research. Technology, especially AI, can come to the rescue. There are many tools that help researchers narrow down their reading and discover the relevant research much faster. These are powered by natural language processing and search based on machine learning.

During the actual research stage, AI open-source tools like Python, R, Pandas, and Spark, as well as proprietary AI tools like Mathematica, Matlab, and SAS, can be very useful in statistical machine learning. A number of research labs are making use of advanced AI concepts such as computer vision, robotic arms, IOT, and speech and audio to assist in gathering data and running experiments based on various hypotheses; collecting, analysing, and illustrating research output; and arriving at conclusions.

Finally, for researchers, the publication and dissemination of their research is the final—and most important—stage. This stage is time-consuming and tedious, yet critical. It determines how and to what extent the research will reach the right audience. There are many AI tools that can be used by researchers to write manuscripts, correct grammar and syntax, and format as per target journal standards, in addition to automated solutions for styling figures, tables, captions, and citations.

Since its inception, CACTUS has been partnering with researchers to assist them in their research journey. Enabling researchers and innovators to find analogous concepts and novel ideas from different industries and fields has been our constant endeavour. The organisation has also entered the AI and deep-learning space to develop innovative products for publishers as well as business and tech solutions for stakeholders in the research landscape. With the aim of putting the researcher at the centre of research, we have rolled out a powerful initiative called R. Having developed several AI-powered tools that simplify the life of the researcher and helps them focus on actual research, we believe it won’t be long before AI is fully integrated in the researcher ecosystem.

The author is Nishchay Shah, Chief Technology Officer, Cactus Communications.

Article originally published by TechGig on https://content.techgig.com/implementation-of-technology-in-advancing-scientific-research/articleshow/77958300.cms (September 7, 2020)

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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.

Pub-sure.com 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 researcher.life, 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 https://www.dqindia.com/counting-on-ai-the-right-time-for-researchers-to-embrace-artificial-intelligence/ (July 21, 2020)

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