Pandemic has helped democratize AI, ML and data science, says Techvantage CEO

New era technologies such as artificial intelligence (AI) have aided in drug development, tracking the global spread of the Covid-19 virus and identifying control measures, says Deviprasad Thrivikraman, Founder and CEO from Techvantage Systems, a big data company based in Technopark, Thiruvananthapuram.

While the pandemic forced the company to switch to BCP mode (business continuity plan) with all employees teleworking, it never eased off. The Covid period saw its analysis activity increase by more than 75%. The company hopes to double its revenue and workforce next year and expand to Bengaluru, London and New Jersey. It plans to hire more than 200 data scientists and artificial intelligence engineers by 2025.

Thrivikraman said Activity area in an interview that while AI, ML, and data science have remained largely within the realm of the tech community until now, the pandemic has changed it. It’s safe to say that the pandemic has helped democratize new era technologies of AI, ML and data science, he pointed out. Excerpts:

What has been the impact of the pandemic on artificial intelligence (AI), machine learning (ML) and data science technologies?

At first, there was very little knowledge about Covid-19. It has driven humanity to harness every ounce of available technological innovation and ingenuity to combat it. AI and ML played a key role in understanding and solving the crisis. AI has truly become one of man’s best friends and has helped in the detection, prevention, response and cure of disease. The big challenge was that most healthcare systems couldn’t handle the unexpected patient load. There was a felt need to prioritize patients for treatment.

Techvantage saw business accretion to the extent of 75 percent? How did it happen?

We’ve helped a few clients quickly redesign their vulnerable human-intensive processes and replace them with automated processes powered by AI and ML – e.g., self-dispatching customer service ticketing system, prospecting algorithms to prioritize end customers with a high chance of conversion, contactless customer service with chatbots, ML-enabled chatbots for contactless Covid symptom screening, and automatic transmission of insurance claims with video.

The clinical implications of Covid-19 prognosis for people with underlying conditions – diabetes, heart disease, etc. – are they an area that interests you?

Now is a good time to work on it. There are several studies, some anecdotal, that suggest pre-existing medical conditions can potentially influence prognosis and quality of life post-Covid. Data availability and quality still pose challenges. There are certainly more public Covid-related datasets available than before, but they are spread across heterogeneous sources and, for the most part, unstructured. Data on pre-existing medical conditions is more difficult to access. In order to build something that will be useful for “patient-level prediction”, AI and healthcare professionals and others should collaborate to create partnerships and build an ecosystem from which reliable data can be extracted and created models tested and improved.

Does the advanced virology lab offered by Kerala offer work opportunities on epidemic containment strategies, drug design and reuse?

Setting up an institute of this size and scope in full compliance with regulatory guidelines is by no means an easy task. It will likely take time before we have enough data to create mitigation strategies. A confluence of various autonomous databases (for example: health care data with immigration data) can potentially stop many transmissions early on. Researchers have leveraged AI to discover a treatment that could stop the outbreak. There are deep learning models that can predict old and new drugs or treatments. Having data and the ability to build an AI platform to use it to facilitate drug design and reuse is just the first step. Partnering with capable research and drug manufacturing companies that offer subject matter expertise is essential.

How can we deal with a situation where the lack of large datasets/accuracy hampers efforts to go beyond simple algorithms and improve results?

No data is usually available ready to be consumed by an AI platform. The reason for this is that many of these data capture systems were not created with the expectation that the data will one day be consumed by AI platforms. It is usually the responsibility of the data analyst or data scientist to prepare it for consumption. In India, data relating to Covid-19 comes from heterogeneous sources. Vaccine compliance data resides on the CO-WIN platform while testing data resides on the ICMR. It is entirely possible that these source systems also do not contain the highest quality data. They can also be in different formats. It is imperative that we analyze the available data in order to prepare the country for a possible future epidemic.

Are privacy concerns arising from the continuous movement of personal data through cyberspace limiting the scope of AI?

We must promote responsible use of personal data. It is likely that there will be a growing trend towards the use of invasive collection, processing and sharing of personal health and behavioral data for targeted tracking of individuals. While these measures are essential, governments must ensure that these tools are implemented responsibly. There should also be a willingness to cease or reverse exceptional uses of data after the crisis. Policy makers should ensure alignment with strong principles (example: OECD AI Principles) that respect human rights and privacy. Systems must be transparent, secure, safe and developers must remain accountable.

Published on

April 26, 2022

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