70. Dr. Brian Keating — Losing the Nobel Prize: A Story of Cosmology, Ambition, and the Perils of Science's Highest Honor
In this wide-ranging conversation Science Salon host Dr. Michael Shermer speaks with cosmologist and inventor of the BICEP (Background Imaging of Cosmic Extragalactic Polarization) experiment Dr. Brian Keating about the following topics:
- how he almost won the Nobel Prize for his research that confirmed the inflationary model of the Big Bang
- the problems with the Nobel Prize as it is currently structured, such as its limitation to only three people (when modern experiments are typically directed by a great many more); that it can't be awarded posthumously (thereby neglecting people like Amos Tversky, who did as much work as his Nobel Prize-winning collaborator Daniel Kahneman); its neglect of many women scientists as deserving of the prize as their male counterparts, and especially how it distorts incentives to collaborate in science
- his upbringing and what inspired him to probe the deepest questions about the nature of the cosmos and reality
- what it's like conducting research in the harsh conditions at the South Pole
- what banged in the Big Bang and what there was before the Big Bang
- the possibility (or not) of a multiverse model and a cyclical model of universes outside of, or before, our universe
- the relationship between science and religion and why they need not always be in conflict
- his Prager U video on why believing in the multiverse takes as much faith as believing in God.
Listen to Science Salon via iTunes, Spotify, Google Play Music, Stitcher, iHeartRadio, TuneIn, and Soundcloud.
This Science Salon was recorded on May 21, 2019. We apologize for the very poor audio-video quality of this recording.
You play a vital part in our commitment to promote science and reason. If you enjoy the Science Salon Podcast, please show your support by making a donation, or by becoming a patron.
Available Results
Generated results are saved to your library for reuse and search.
Choose Template
Pick the result you want. You can review provider and model before generating.
A concise first-pass summary for understanding the episode quickly.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
Memorable statements and important claims with attribution and source context.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
Investment theses, assets, catalysts, valuation reasoning, time horizons, and risks.
Chronologies, actors, causes, consequences, turning points, and competing historical interpretations.
Policies, proposals, stakeholders, arguments, implementation constraints, and predicted effects.
Career paths, skills, hiring signals, workplace decisions, transitions, and limitations of the advice.
Behavioral mechanisms, biases, motivation, habits, emotions, interventions, and evidence limitations.
Economic mechanisms, incentives, indicators, market structure, forecasts, and uncertainty.
Leadership principles, team systems, organizational design, culture, feedback, and failure modes.
Audience, positioning, messaging, acquisition, retention, experiments, metrics, and failed approaches.
Teaching methods, learning strategies, practice, feedback, assessment, and effectiveness evidence.
Theses, premises, arguments, objections, values, thought experiments, and unresolved questions.
Communication patterns, conflict, boundaries, expectations, repair methods, and contextual limitations.
Books, papers, authors, courses, and other learning resources mentioned in the episode.
Repeatable methods, frameworks, mental models, processes, and systems.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Memorable statements and important claims with attribution and source context.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.