Ask yourself dumb questions and answer them (2020)
Asking seemingly “dumb” questions is framed as a powerful way to do real thinking, especially in mathematics, where challenging definitions, notation, and conventional methods can reveal why standard approaches exist or suggest new ones. Commenters explore how creativity, humility, and rigorous testing distinguish productive unconventional ideas from crankery, and note that social pressures often discourage basic questions in classrooms and workplaces. Several also describe practical strategies—like using AI to probe assumptions, forcing oneself to propose answers before asking for help, or starting from simple examples—to deepen understanding and foster more original problem‑solving.
Value and Risks of “Dumb” Questions
- Many see the key distinction between “ordinary,” “smart,” and “genius” as:
imagining new ideas, rigorously testing them, and being willing to discard failures. - “Dumb” questions can expose hidden assumptions, clarify why conventional wisdom exists, or occasionally lead to new insights.
- Others note that genuinely bad questions exist and that social and professional environments often punish visible ignorance, which discourages asking.
- Several anecdotes: graduate students and professionals stopped asking questions out of fear of looking ignorant, later realizing this blocked their learning.
Science, Authority, and Understanding
- A long subthread debates people who “trust scientists” versus those who can explain underlying arguments.
- One side argues that repeating “scientists say” without knowing which scientists or what evidence is anti-scientific and easily misused (e.g., climate denial).
- Others counter that laypeople must mostly trust institutions; independent verification of everything is impossible, even for scientists.
- Disagreement over whether knowing names/papers is a good proxy for real understanding.
- Some argue that beliefs form an interconnected web of explanations, not isolated faith claims; others emphasize that, in practice, most people still rely on diffuse authority.
Genius vs Crank and the Cost of Evaluation
- Discussion of how to distinguish cranks from rare geniuses.
- In mathematics, logical structure makes it somewhat easier: proofs can often be checked on paper.
- However, history shows extreme outliers can be misjudged; current incentive structures (publication counts, norms) may be hostile to unconventional brilliance.
- In experimental fields, empirical testing costs make filtering ideas and “dross” much harder.
Problem-Solving and Creativity Techniques
- Several commenters endorse deliberately trying “embarrassing” or “stupid” solutions in private, then rigorously editing or disproving them.
- Strategy: cast a wide net of qualitatively different hypotheses, try to falsify them quickly, then focus on what survives.
- Social conformity and being around others can collapse nascent ideas; solitude can help nurture fragile, half-formed directions.
Mathematical Foundations and Notation
- Some highlight the article’s mathematical context: asking why definitions (e.g., convexity, completeness) are chosen and what breaks if assumptions are removed.
- This leads to deeper appreciation and can reveal how subfields arise from initially restricted, tractable cases.
- Separate debate: whether traditional math notation, optimized for pen and paper, should be rethought for computers (e.g., s-expressions, Unicode).
- Pushback: current notation is optimized for human communication; proof assistants and alternative syntaxes already exist but haven’t replaced it.
Tools and Pedagogy
- Large language models are described as useful “dumb question” partners: asking simple, systematic questions that surface blind spots.
- Teaching anecdotes:
- Some instructors actively welcome the “dumbest” question as a learning opportunity.
- Others manage over-questioners by requiring them to propose solutions or batch questions, which accelerates their own reasoning and reduces disruption.