Pharma
In the ever-evolving landscape of artificial intelligence (AI), one remarkable phenomenon has caught the attention of researchers and industry experts alike: the convergence of scalable generative AI. This potent combination of cutting-edge computational tools, creative frameworks, and problem-solving strategies has opened the way for ground-breaking developments across a range of fields. Scalable generative AI has revolutionized industries such as architecture, healthcare, entertainment, and more. In architecture, algorithms generate innovative building designs that optimize space utilization, energy efficiency, and aesthetics. In healthcare, AI-generated models assist in diagnosing complex medical conditions and enable efficient drug development. The entertainment industry leverages AI algorithms to create life like virtual characters, music, and artwork, pushing the boundaries of creative expression. From finance to cybersecurity, scalable generative AI has transformative applications across diverse sectors, unlocking new possibilities and reshaping traditional processes.
To give us more insights on this we had an exclusive roundtable discussion with all the senior leaders in the industry. The panelists for the session were Rajendar RB, Managing Director - Analytics and Information Management, Head of Citi Solutions Center, at Citi, Padmashree Shagrithaya, EVP & MD - Insights & Data GBL at Capgemini, Anupam Gupta, Vice President - Enterprise Data and Analytics at Optum, Ankush Sabharwal, Founder & CEO at CoRover and Purnesh Gali, Cofounder and CEO at Actalyst.
The Emergence of Scalable Generative AI
The few factors which are driving growth are mostly access to data across different industries. Along with the processing power of the machines to a large extent. As the data grows, the quality of the data helps a lot. Accessibility of this data helps a lot. But there are a lot of other things where the demand from the industry will drive the growth to a large extent. The internal constraints might be the bottleneck for growth sometimes because the privacy of data is something we need to focus on, and anything which can change that. Most importantly how do we govern the data and how do we make sure that we are abiding by the regulations. Because in a very regulated area, privacy is a large issue. And if we can come across those, there is no stopping for growth. Any bias which is introduced because of AI in the decision making process, needs to be focused on. So if we can come up with things where these are taken care of, we can actually grow much, much faster.
