12:05 - 12:40
“The upheavals [of artificial intelligence] can escalate quickly and become scarier and
even cataclysmic. Imagine how a medical robot, originally programmed to rid cancer,
could conclude that the best way to obliterate cancer is to exterminate humans who are
genetically prone to the disease.” — Nick Bilton, tech columnist wrote in the New York
Data Science as a skill and Data Scientist as a role have been in the market for more
than a decade now. It is high time to ponder that what is required from a data scientist.
Since, Data Science is a highly disruptive industry which demands continuous upskilling
and reskilling from an industry perspective. The entire business has moved from just
building models and generating insights to build real time applications and platforms.
The likes of building smart cars, space cars, cancer prediction through AI and identifying
and catching criminals through AI devices are some of the recent developments in the
field of technology. Artificial Intelligence, Deep Learning, IoT totally fit as a robust
mechanism to provide such solutions for the businesses.
How do you build such skills?
The traditional model of reskilling seeks a theoretical approach to build AI and Deep
Leaning competency. Hence, it is important to bridge the gap through an amalgamation
of concepts, applications and flavor of businesses. Companies such as Intel, Google,
Microsoft, IBM, Tesla, NVDIA are the innovators in the space of building solutions in AI,
IoT and Deep Learning. Recently Gartner presented a detailed analysis of AI disruption
in the market. "AI promises to be the most disruptive class of technologies during the
next 10 years due to advances in computational power, volume, velocity and variety of
data, as well as advances in deep neural networks (DNNs)," said John-David Lovelock,
research vice president at Gartner. Business executives will drive investment in these
products, sourced from thousands of narrowly focused, specialist suppliers with specific
It is important to deploy experiential learning vehicles in collaboration with innovators to
reskill the existing pool of data scientists. It is important to design, learn and apply from
a 3-layered learning architecture.
Layer 1 – Inclusion of conceptual training through digital platforms
Layer 2- Engagement through in-person training workshops
Layer 3 – Capstone Project in partnership with industry innovators.
The talk is primarily emphasized on ways to reskill Data Scientists in newer and
Hall 1: Keynotes & Panels