Learning from algorithms to make Human decisions
summary
Computer science provides concrete prescriptions for everyday choices: use optimal stopping problems to decide how many apartments to see, treat dinner choices as a multi-armed bandit to balance exploration and exploitation, and apply caching and scheduling ideas to storage and time management. Evidence includes A/B testing practices and a machine learning demonstration that a model can mimic a judge’s decisions while reducing variability across a 10-hour day. The industry’s KPI focus — often minutes of video viewed per user per week — shapes what systems optimize for.
AI-written summary · about
chapters (10)
- 0:00Introduction
- 1:08Whats the message
- 6:25Questioning algorithms
- 10:15Explicit AI
- 12:57Human nature
- 14:59From possibility to positivity
- 19:18Transparency
- 21:48Impact on the audience
- 24:15Being yourself
- 27:11What is your message
related talks
42mAugmenting Human Decision Making with Data Science
34mAlgorithms to live
47mAI Ethics, Strategic Decisioning and Game Theory
44mHow to Improve UX with Machine Learning: Insights and learnings until now
34mAugmenting Human Decision Making with Data Science
38mDesigning Machine Learning for Humans
02mThe Missing Piece
15mUnderstanding Decisions Driven by Big Data