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1. Best practices for research by mining biographies of great scientists 2. Rank decades against one another 3. Interestingness Maximization 4. Trans X and the levels below it (tiers of progress, categories of progress) 5. Take every philosophical transhumanist goal and discover / create concrete paths (curriculum + plan of action). Begin with:

  1. Machine Superintelligence
  2. Gene Editing
  3. Existential Risk
  4. Brain-Computer Interfaces
  5. Anti-Aging Pathways
  6. Write down the competition between hierarchies
  7. How to Become Batman
  8. Giving of agency to to the non-agentic
    1. What do the tears understand?
    2. What does circling want?
    3. Meditations on moloch
    4. Memetics
  9. When thinkers are way ahead of their time, how are they ahead, and how can that be replicated?
  10. How to make your emotions work for you, consistently.
  11. Mastery
  12. Arrogance
  13. Judgmentalness
  14. Learning from Criticism
  15. Using People
  16. Control
  17. The mechanics of Ego
  18. Empirical Observations of my Sexuality
  19. Decompose Identity (Christianity as making identity about beliefs, not grounded in reality)
  20. Differences between thinking in Narrative vs. thinking Conceptually / Analytically / Statistically
  21. Meaning Maximization
  22. Oh my god. How have I not created this yet? It its most beautiful form?
  23. Groundedness / Ungroundedness
  24. Refers to levels of analysis, map / territory conflict
  25. Self-awareness
  26. What is self-awareness?
  27. How to maximize self-awareness?
  28. Why ranking is the most dark thing that happens all the time (almost necessarily, tacitly)
  29. Invalidation of specialness narratives at scale. Better than, worse than, not enough. Creates deep feelings of inadequacy.
  30. How not to get trapped in a thought loop
  31. How to see thought loops
  32. Deconstruct ‘Values’
  33. Intuition pumps, different definitions, connotations, contexts in which it varies, meta-values, etc.
  34. Meta-Identity
  35. Question Generators
  36. Meta-Generation
  37. Superforecasting
  38. To Create: Fundamental Questions in Representation Learning
  39. I want to create a curriculum for representation learning that’s OLD. Exclusively papers from 2000 or earlier. (Hint: Old papers only cite other old papers. And check ICA.)
  40. Take every objective in ‘What Makes a Representation Good’, add my own objectives, and for each one specify:
  41. A way (or set of ways) to measure the objective
    1. Distinguish between the concept of the objective and the mathematical instantiation of the objective (unless they’re truly identical)
  42. The downstream consequences of doing better or worse on the objective
  43. Compare two different networks over the objective
  44. The rationale (and intuition pumps) for the objective
    1. The counterarguments
  45. Apply inversion to all of the ideas in a frontline paper in representation learning. When it works well, you’ve discovered something you think is true that others disagree with. And if it’s a foundational assumption, you can get started on making progress.
  46. How do we know what we claim to know in Representation learning? Ask this of a shortlist of ‘sacred beliefs’.
  47. Create a ‘sacred beliefs’ in representation learning list.
  48. Create a ‘consistently questioned beliefs’ in representation learning list.
  49. Why learn Discrete / Sparse Representations? Be able to give a fully fledged, fully throated defence and attack.
  50. Why ‘representation’ is this crazily important concept. The dramatic, windfall differences that come out of slightly different representations.
  51. Survey all possible papers I could push hard at in Abstract Representation Learning
  52. Explicate all my categories of idea as low level ideas
  53. Generate new categories of idea
  54. List out all of the goals for representation learning as a field and multiple pathways that would fulfill each goal
    1. Order the goals in terms of importance
  55. List out the unknowns, the missing categories, the assumptions behind the goals, and the mistakes
  56. List of likely to be true / likely to be false assumptions, and ways to prove or disprove each assumption
  57. Transfer between each related field and representation learning
  58. Decide what the goal is. Work backwards to research paths that accomplish the goal. Value parts of the research frontier insofar as they relate to the goal.
  59. What would a benevolent authoritarian do with governmental control?
  60. Creation of umpteen programs creating citizens with valuable skillsets.
  61. Believability is such an important concept.
  62. My own lack of it is destroying me. It’s destroying my goal setting, destroying my ability to execute.
  63. It’s why people don’t trust x: she’s not believable.
  64. It’s what I’ve been trying to build with reputation, but it’s not equivalent to reputation. Reputation can be fake. Believability is real.
  65. It’s about the state of your models and how they interact with reality.
  66. It’s about whether what you say will happen happens.
  67. What is generalization? Is there a right answer to how to operate “out of domain?”
  68. Map out existing value systems
  69. Holes / weaknesses in existing value systems
  70. This visualization technique (first walking through the motions of the necessary action in your mind, and then treat real life as just another vivid visualization) is extremely powerful and symmetric.
  71. Apply sys creativity to it. And use it, over and over again.
  72. Arguments have statistical properties. Map out the properties of common forms of argument.
  73. For example, person1 won’t take action1, he belongs to category1. (Implicit, other agents in category1 tend not to / can’t / won’t take action1, this is an example of generalization) One could treat this as an inductive prediction and use the
  74. Emotional Transmogrification
  75. Redirection
  76. Map out emotional space
  77. Language patterns / body language for each emotion
  78. Systematizing systematizing
  79. Planning
  80. Optimal Sleep
  81. Falling asleep
  82. Setting up for sleep
  83. Sleeping deeply
  84. Dreaming
    1. Lucidity
    2. As creativity
    3. As problem solving
  85. Getting to sleep on time
  86. Getting out of bed upon waking
  87. AI Memespace
  88. Emotional foundations to all of the memes that propagate amongst ML researchers
  89. Cult Leadership Playbook
  90. Deconstructing Incentive Structures
  91. Reputation systems
    1. Equilibria in Uber / Lyft ratings
    2. Lack of incentives to rate
    3. Use of gains in reputation to motivate
      1. Open source contribution
      2. Sharing in social networks
      3. Publishing
  92. Market Mechanism
  93. Blockchain
  94. Bitcoin Mining
  95. Skin in the Game
    1. This is about creating downside risk - it’s fear of loss, not hope for gain.
  96. Auctions
  97. Prediction Markets
  98. Peer-to-Peer
  99. Credit Assignment in Research
    1. Citation
    2. Other forms of credit
  100. Insurance 1. Education as insurance 2. Abstract and generalize - what is actually about a sense of security / safety, that people say is about something else?
    1. Salary
  101. Psychological interactions with all of these mechanisms 1. Use of fear in reputation - fear of loss
  102. Invert 1. Fear of loss vs. gains 2. Reward vs. Punishment
  103. Convenience 1. Underrated, extremely important 2. Easiest to perform action as Default 3. Abstraction as source of money - create a nice interface to harsh reality
  104. Organizational incentive structures, how to get promotion, how to get status, etc. 1. This literature exists. And it’s evolved over centuries.
  105. The impact of measurability on all of these 1. In reputation systems, other people’s opinions are competence 2. Distinction between mechanisms in reality vs. social reality.
    1. Reality is skin in the game, often. Social reality is a layer over reality that often destroys skin in the game
    2. Social reality has evolved, itself. It’s cooperation. But it has to stay at a lower level (individuals), not a higher level (firms)
  106. Scalable vs. Non-Scalable Mechanisms

Source: Original Google Doc

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