Chetan Gupta provides an Industrial AI perspective to Generative AI.
Subscribe: Apple • Spotify • Overcast • Google • AntennaPod • Podcast Addict • Amazon • RSS.
Chetan Gupta is the Head of AI Research at Hitachi. This episode explores the applications and challenges of generative AI in industrial settings. It covers an introduction to industrial AI and its unique challenges like high stakes consequences, data scarcity, and the need for explainability. Potential use cases for generative AI in industries are discussed, including software development, customer support, knowledge management, and process transformation. Key challenges highlighted are reliability, security, cost optimization, and handling task complexity.
The discussion delves into utilizing open-source foundation models, domain adaptation, and developing specialized models for different data modalities like acoustic and time series data. Synthetic data generation using generative AI to train models in data-scarce scenarios is explored. The role of structured data, metadata, and knowledge graphs like fault tree diagrams in reducing hallucinations and improving reliability is examined. Responsible AI practices, with a focus on reliability and trustworthiness, are emphasized. Future opportunities discussed include continuous learning from sensor data, end-to-end optimization using agents, and multimodal AI integrating generative models with simulation and symbolic methods.
Interview highlights – key sections from the video version:
-
- State of AI/ML Adoption in Industrial Settings Pre-Generative AI
- Excitement Around Generative AI Across Industries
- Getting Started with Generative AI for Industrial Use Cases
- Unique Industrial Challenges for Generative AI
- Foundation Models: Open Source vs Proprietary
- Importance of Efficiency and Domain-Specific Models
- Taking the First Steps with Generative AI
- Synthetic Data Generation
- Role of Knowledge Graphs and Structured Data
- Responsible AI: Focus on Reliability
- Future of Agents and Continuous Learning
- Process Transformation with Generative AI and Key Takeaways
Related content:
- A video version of this conversation is available on our YouTube channel.
- Generative AI in Industrial Applications
- An Industrial AI Perspective to Generative AI
- What is Graph Intelligence?
- Emil Eifrem: The Future of Graph Databases
- Semih Salihoglu: The Intersection of LLMs, Knowledge Graphs, and Query Generation
- Philipp Moritz and Goku Mohandas: Navigating the Nuances of Retrieval Augmented Generation
- Building a Fleet of Custom LLMs
- Jerry Liu: An Open Source Data Framework for LLMs
If you enjoyed this episode, please support our work by encouraging your friends and colleagues to subscribe to our newsletter: