Practical AI Real-time conversational insights from phone call data
Feb 17, 2020
In this engaging discussion, Mike McCourt, a data scientist at Invoca specializing in natural language processing, dives into the fascinating world of call analytics. He unveils how their Signal AI technology processes conversational data and the significant challenges it faces. Mike highlights the importance of linking phone interactions to marketing analytics and shares insights on training models using call data, addressing privacy and empathy. The conversation also explores unsupervised learning methods that enhance AI's ability to uncover themes and improve insights from diverse language patterns.
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Unplanned AI Journey
- Mike McCourt's path to AI was unplanned, initially aiming for a physics professorship.
- He transitioned into AI after a PhD in astrophysics and two postdocs, drawn by the field's rapid growth.
Physicists in AI
- Many physicists are transitioning to AI due to high demand.
- This is partly driven by the competitive academic landscape and the abundance of AI opportunities.
Physics and Data Science Synergy
- Physics provides a solid foundation for data science by training individuals.
- This training includes model creation, prediction, data analysis, and uncertainty quantification.