Chandra Khatri, Chief Scientist and Head of AI at Got It AI, was a key team member in the early days of AI at eBay, Amazon, and Uber. He has been on the cutting edge of NLP research for more than a decade and now leads AI at Got It AI. Chandra and the team are making it easier for customers to have conversations with bots. He's making innovative use of transformers and active learning to use "small data" to train sophisticated large language models to automatically answer customer questions in fields as diverse as healthcare, financial services, education, and defense.
Listen and learn…
1. What the AI culture is like at eBay, Amazon, and Uber 2. About transformers, why they’re important, and how they're improving NLP accuracy 3. How we’ve moved AI from search ranking (recommender systems) to other use cases including operations and bots 4. How the rise of open source and no-code tools is making “Google-like” AI maturity accessible to every company 5. How startups with limited access to data can use transfer learning to improve AI accuracy 6. What’s holding back broader adoption of AI in the enterprise 7. How the rise of Technical Product Managers (TPMs) is bridging the gap between engineers and business analysts 8. How to eliminate bias from training data 9. How long before we’ll all have a personal JARVIS
References in this episode…
Got It AI Chandra on LinkedIn Hugging FaceChristopher Nguyen on AI and the Future of Work
Chandra Khatri, Chief Scientist and Head of AI at Got It AI, was a key team member in the early days of AI at eBay, Amazon, and Uber. He has been on the cutting edge of NLP research for more than a decade and now leads AI at Got It AI. Chandra and the team are making it easier for customers to have conversations with bots. He's making innovative use of transformers and active learning to use "small data" to train sophisticated large language models to automatically answer customer questions in fields as diverse as healthcare, financial services, education, and defense.
Listen and learn…
1. What the AI culture is like at eBay, Amazon, and Uber 2. About transformers, why they’re important, and how they're improving NLP accuracy 3. How we’ve moved AI from search ranking (recommender systems) to other use cases including operations and bots 4. How the rise of open source and no-code tools is making “Google-like” AI maturity accessible to every company 5. How startups with limited access to data can use transfer learning to improve AI accuracy 6. What’s holding back broader adoption of AI in the enterprise 7. How the rise of Technical Product Managers (TPMs) is bridging the gap between engineers and business analysts 8. How to eliminate bias from training data 9. How long before we’ll all have a personal JARVIS
References in this episode…
Got It AI Chandra on LinkedIn Hugging FaceChristopher Nguyen on AI and the Future of Work
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