Mining Exploration Unpacked (The Derby Mill Series ep 06)
The Derby Mill Series
Unifying Inputs for Enhanced Predictive Models
This chapter delves into the relationship between supervised learning and interpretability techniques, focusing on how integrating multiple input modalities can improve predictive accuracy. By using examples from health data and mining, it highlights the importance of understanding model behavior and the implications of diverse data processing.
In the first episode of The Derby Mill Series to post since our co-host Rich Sutton won the Turing Award, the highest distinction in the field of computer science, Sutton joins our panel of experts to further discuss the application of artificial intelligence to the task of mining exploration. What ensues is a remarkable conversation about the increasing relevance of cheap and easily discoverable data sources in an age dominated by artificial intelligence and reinforcement learning.
The analogy begins with mining, where core samples may provide an element of ground truth but are, at the same time, tough and very expensive to get. Other data sources, such as aerial imagery, chips and dust, are cheap and more easily available. So can the pattern-recognition abilities of artificial intelligence elevate the relevance of that lower-fidelity, more easily available information?
Extending the analysis, Intrepid Senior Advisors Niamh Gavin and Sendhil Mullainathan draw parallels with health care. Apple Watch’s skin sensors are certainly less accurate than an annually drawn blood test. But as the Watch conducts its tests numerous times a day, and as AI better recognizes troubling sensor patterns, the cheaper Watch data could become just as important as more expensive medical diagnostics.
Generalizing to other areas, Intrepid partner Ajay Agrawal notes that lower-fidelity data that is more easily available could become as informative as high-fidelity data that is tougher to extract. Our experts’ ultimate prediction, then, observes that cheap data plus artificial intelligence could transform the fundamental economics of many different industries.
Our panel of experts
Richard Sutton, 2024 winner of the Turing Award, pioneer of reinforcement learning and professor, University of AlbertaSendhil Mullainathan, MacArthur Genius grant recipient and professor, MITNiamh Gavin, Applied AI scientist, CEO, Emergent PlatformsAjay Agrawal, co-founder and partner, Intrepid Growth Partners
LINKS
Intrepid Growth Partners’ Senior Advisor Rich Sutton wins the Turing Award. NY Times. Financial Times. Betakit.This episode extends a discussion in Derby Mill episode 05: Mining Exploration.Referenced in this episode: Sendhil Mullainathan's heart attack studyyRich Sutton’s home page. Follow Rich on XSendhil Mullainathan’s website. Follow Sendhil on XBe sure to catch every episode by subscribing on the following platforms:YouTube // Spotify // Apple Podcasts
DISCUSSION POINTS
00:00 Introductions and opening credit01:43 Recap of Mining Exploration06:01 Clips: Mining industry transformation09:52 Niamh on narrowing the search zone12:18 Rich on sequential decisions and pattern recognition15:08 Sendhil on using supervised learning to train predictors19:34 Niamh on non-invasive markers21:52 Signals in healthcare vs. mining25:02 The combination of human + AI27:47 A new age of data analysis29:50 Data sources and reinforcement learning35:53 A cognitive barrier for data42:24 The indicator analogy45:58 Closing remarks
NUGGETS (short excerpts from the full episode)
NUGGET 01: Sum > Whole of Its Parts
Niamh Gavin argues that human + AI intelligence is better than either in isolation.
NUGGET 02: A More Interactive Feedback Process
Rich Sutton advocates for an awareness that important mining exploration problems require a wide diversity of data inputs.
NUGGET 03: The Less Invasive, Far Cheaper Data Axis
“What if I had a sweat test that was 10% as good as a blood test?” asks Sendhil Mullainathan, noting the way AI can make use of data from cheap and more easily available diagnostics to improve numerous different industries.
NUGGET 04: AI and the Increasing Relevance of Little Tests
Expert intelligence is expensive today, notes Rich Sutton, but as computational power decreases, AI will help to bolster the importance of all sorts of cheap and easy tests.
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.
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