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Vanishing Gradients
Episode 14: Decision Science, MLOps, and Machine Learning Everywhere
Nov 20, 2022
Hugo Bowne-Anderson discusses decision science, MLOps, and the ubiquity of machine learning models. Topics include decision-making under uncertainty, biases in data collection, MLOps and DevOps convergence, digital feedback loops, Google's search evolution, and the impact of modern algorithms on reality perception.
01:09:01
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Quick takeaways
- Principled decision-making involves probabilistic thinking, quantitative risk assessment, skeptical data analysis, and wise decision prioritization.
- Assessing decision quality independently of outcomes is essential, focusing on rational evaluation based on probabilities and impact.
Deep dives
Principled Decision-Making Under Uncertainty
Decision Making in a Time of Crisis essay discusses adopting a principled approach to decision-making under uncertainty. It emphasizes four tools: thinking probabilistically, considering risks quantitatively, analyzing reported data skeptically, and prioritizing decisions wisely. The essay contrasts binary decisions like doubling down in Blackjack to complex real-world choices, underscoring the importance of assessing decision quality regardless of outcomes and distinguishing between risk and uncertainty.
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