Meet Ada, a Canadian AI agent platform automating the resolution of customer service interactions. When customers have complex requests—such as resetting passwords, checking order status, or requesting a refund—Ada uses large language models to radically reduce the amount of human effort required to fulfill the customer’s inquiry.
Here, Ada CEO Mike Murchison and Chief Product & Technology Officer Mike Gozzo join the Derby Mill podcast to discuss the intersection of AI and customer support—and where the technology may go, at the limit.
Our panel of experts:
Ajay Agrawal, co-founder and partner, Intrepid Growth PartnersRichard Sutton, 2024 Turing Award recipient, pioneer of reinforcement learning and professor, University of AlbertaSendhil Mullainathan, MacArthur Genius grant recipient and professor, MITNiamh Gavin, Applied AI scientist, CEO, Emergent Platforms
LINKS
Ada websiteAda CEO Mike Murchison LinkedInAda Chief Product & Technology Officer Mike Gozzo LinkedInRich Sutton’s home page. Follow Rich on X.Sendhil Mullainathan’s website. Follow Sendhil on X.Sendhil’s article on Algorithms Need Managers, Too published in the Harvard Business Review Be sure to catch every episode by subscribing on the following platforms:YouTube // Spotify // Apple Podcasts
DISCUSSION POINTS
00:00 Introduction01:49 Meet Ada, the company automating customer support05:05 Customer service & books: an analogy05:41 Murchison describes automated resolution08:50 Human feedback for automated improvement 23:17 LLMs in customer service26:10 The difference between language and action26:34 Ada’s use of LLMs30:01 Murchison on how “deterministic” Ada’s actions are30:59 Improving decision quality37:06 Protecting against LLM’s unreliability 44:40 Closing remarks
NUGGET 01: Human Feedback for Automated Improvement
Ada describes the role of humans "coaching" their AIs. Why this is one of the first areas for "automated improvement," and how can the preference data they are collecting through the coaching process be used to "drive automated improvements throughout the entire system."
NUGGET 02: Decision-Making Quality
Rich Sutton asks how Ada improves the quality of the system's decisions, and questions the role of humans vs. AI in terms of evaluating versus improving the quality of decisions.
NUGGET 03: Distillation
Given the cost and latency virtues of smaller models, when do we anticipate applications to use large foundation models at the limit? Is the Ada case a good example of using large models to bootstrap a commercial solution en route to smaller, more specialized models?
DISCLAIMER
The content of this podcast is for informational and educational purposes only and should not be construed as marketing, solicitation, or an offer to buy or sell any securities or investments. The opinions expressed in this video are those of the participants and do not necessarily reflect the views of Intrepid Growth Partners or its affiliates. Any discussion of specific companies, technologies, or industries is for illustrative purposes and does not constitute investment advice. Viewers are encouraged to consult with their own financial, legal, and tax advisors before making any investment decisions.