Road to Scalable AI – Is this a Data, Science, or an Engineering Challenge?
Artificial intelligence (AI) is being seen as a game-changing technology in the modern era of science and engineering. It is believed that AI is going to play a very big role in shaping the future of the energy industry.
The use of such science and data-driven digital solution in the energy industry is pervasive, especially in seismic processing, predictive maintenance, operational optimization, high-frequency trading, reservoir simulation, and computational chemistry just to name a few. These areas reside at the intersection of physics/chemistry, data science, applied mathematics & computer science.
All the problems in those domains are very challenging to solve on their own and it needs attention from experts from cross-discipline and often can be viewed as a fusion of data and computing challenge! The most important question remains, how do we make use of modern-day digital technologies to solve such challenging industrial problems in a faster, smarter, and more robust way?
In this talk, I will paint the connection between gaining "insights from data", "the art of scientific computing" and "software engineering" with the intention to trigger some thought about - who is responsible to make the “road to scalable AI” smooth? Is this data or science, or is this an engineering challenge on its own?