1. Latticework of Mental Models

    1. Ex., pushing How to Think into intuition

  2. Systematizing Creativity

    1. Ex., Reframing, Questioning Assumptions, Abstraction & Generalization, Decomposition, Composition / Recombination, Generators, Leading Questions, etc.

  3. Filter at the Intersection of Competing Worldviews

    1. Ex., Worldview Building

    2. Ex., Internalizing the conceptual style of intellectual giants

  4. Intelligence Decomposition & Optimization Along All Axes

    1. Memory (Working, Episodic, Long Term)

    2. Attention (though this feels like a limitation)

    3. Having a model

    4. Abstract Knowledge Representation

    5. Ability to Generalize

    6. Learning / Adaptivity

    7. Creativity

    8. Information processing / Computation speed

    9. Goal accomplishment

  5. Technical Consilience (Seeing the unity of knowledge behind every natural science) by internalizing:

    1. Information Theory (Cover / Thomas)

    2. Statistical Mechanics (Talman)

    3. Algorithmic Game Theory (Nisan)

    4. Nonlinear Dynamics and Chaos (Strogatz)

    5. Mechanism Design (Borgers)

    6. Algorithms (CLRS)

    7. Neuroscience (Principles of Neural Science)

    8. Electromagnetics (Haliday / Resnick)

    9. Quantum Mechanics (Griffiths)

    10. Nuclear Physics (Krane)

    11. Chemistry (Brown)

    12. Intro Proof (How to Prove It)

    13. Analysis (Abbott)

    14. Set Theory (Halmus)

    15. Abstract Algebra (Dummit, Foote)

    16. Topology (Munkres)

    17. Category Theory (Pierce, then Awodey)

    18. Probability Theory: The Logic of Science (Jaynes)

    19. Machine Learning: A Probabilistic Perspective

    20. Computational Learning Theory (Kearns)

    21. Learning Invariant Representations (Poggio)

    22. Causality (Pearl)

    23. Computability and Logic (Boolos)

  6. Thinking and Deciding (Baron)

    1. Types of thinking

    2. On the Study of thinking (meta)

    3. Rationality

    4. Logic

    5. Normative Theory of Probability

    6. Descriptive theory of probability judgment

    7. Hypothesis Testing

    8. Judgement of correlation and contingency

    9. Actively open-minded thinking

    10. Normative theory of choice under uncertainty

    11. Descriptive theory of choice under uncertainty

    12. Choice under certainty

    13. Utility Measurement

    14. Decision Analysis and Values

    15. Quantitative Judgment

    16. Moral judgment and choice

    17. Fairness and Justice

    18. Social Dilemmas: Cooperation vs. Defection

    19. Decisions about the future

    20. Risk

  7. Path to Awakening / Enlightenment, ex. the intersection of:

    1. Shinzen Young’s The Science of Enlightenment

    2. Calduasa’s The Mind Illuminated

    3. Chapman’s Meaningness

    4. Wallis’s Trantra Illuminated

    5. Ingram’s Mastering the Core Teachings of the Buddha

  8. Discover and internalize lost philosophical traditions

    1. Ex., Al Gazali, Mohisim, Presocratics, I Ching

  9. Rationalism (May be too similar to Thinking and Deciding…)

    1. As framed in SSC / LW / Sequences / HPMOR / Inadequate Equilibria

  10. Turn machine learning into a fully fledged philosophy around how to think (May be a subset of technical concilience…)

    1. Bias-Variance Tradeoff

      1. Overfitting

      2. Controlling complexity

        1. Model simplicity (restriction methods)

        2. Selection methods (over features)

        3. Regularization

    2. Curse of dimensionality

    3. Ensemble Modeling

    4. Occam’s Razor (Formalized)

    5. Training vs. Generalization Error

    6. Interpolation vs. Extrapolation

    7. Smoothness

    8. VC Dimension

    9. Variance Maximization

      1. Optimizing for Volatility vs Expected Value

    10. Bayes Rule

    11. Bayes Error

    12. Exploration-Exploitation

    13. Manifolds as Data Representation