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[logseq-plugin-git:commit] 2024-09-05T09:21:12.691Z
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logseq/config.edn

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;; :journal? false} ; Default value: false
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;; Favorites to list on the left sidebar
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:favorites ["product" "cyber" "aicosystem" "ask" "learn" "truth" "brain" "the product" "collective intelligence" "superintelligence" "concepts" "🫦 space pussy" "knowledge theory" "space pussy" "bostrom" "neuron" "particle" "tokens" "cyberlink" "cybergraph" "truth machine" "cybernet" "soft3" "projects" "todo" "energy reform" "aips" "cyb" "about this metagraph"]
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:favorites ["agi" "product" "cyber" "aicosystem" "ask" "learn" "truth" "brain" "the product" "collective intelligence" "superintelligence" "concepts" "🫦 space pussy" "knowledge theory" "space pussy" "bostrom" "neuron" "particle" "tokens" "cyberlink" "cybergraph" "truth machine" "cybernet" "soft3" "projects" "todo" "energy reform" "aips" "cyb" "about this metagraph"]
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;; Set flashcards interval.
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;; Expected value:
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- TODO [[cyber/genesis]] protocol
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- point in [[time]] when [[cyber]] become activated after rigorous completion
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- target launch date: 8.10.57
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- probably higher complexity when [[launching rocket]]

pages/agi.md

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- in this article i describe a conceptual framework for creating artificial general intelligence
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- from the first glance our intelligence seems complicated
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- but there exist simplistic view now on general intelligence, and how to reproduce it using modern compute
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- our brains are machines which optimize consumption of energy for information storage
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- they achieve it by making decisions
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- big, small, tiny, doesn't actually matter
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- what matter is how many decisions is being produced by cognitive system
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-
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-
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- ### theory: the triadic emotional regulation framework
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**overview:**
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This theory posits that human emotional experiences can be broadly understood through the interaction of three primary neuromediating hormones—dopamine, serotonin, and cortisol—which represent a triadic system governing pleasure, joy, and pain, respectively. The balance and interplay of these hormones create a foundation for the full spectrum of human emotions and behaviors.
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- ### the three pillars of emotional experience:
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1. **dopamine (pleasure): the reward-seeking drive**
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- **role:** dopamine is primarily responsible for pleasure and reward-seeking behavior. it motivates actions that lead to positive outcomes and reinforces behaviors through the anticipation and achievement of rewards.
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- **function:** dopamine encourages exploration, goal-setting, and the pursuit of desires, making it essential for learning and adaptation. high levels of dopamine correlate with feelings of pleasure, satisfaction, and excitement.
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2. **serotonin (joy): the foundation of well-being**
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- **role:** serotonin governs the sense of joy and overall well-being. it plays a crucial role in mood stabilization, fostering feelings of happiness, contentment, and emotional resilience.
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- **function:** serotonin acts as a stabilizer for emotional states, allowing for a sustained sense of joy and positivity. it is vital for maintaining mental health and ensuring emotional balance, preventing the extremes of mood swings.
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3. **cortisol (pain): the survival mechanism**
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- **role:** cortisol is associated with pain, stress, and the body's response to perceived threats. it is part of the body's survival mechanism, preparing individuals to cope with challenging or dangerous situations.
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- **function:** cortisol triggers the "fight or flight" response, causing heightened alertness and readiness to face pain or danger. while essential for survival, prolonged exposure to cortisol can lead to chronic stress, anxiety, and emotional suffering.
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- ### interaction and balance:
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- **dopamine-serotonin synergy:**
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- The interplay between dopamine and serotonin regulates the balance between short-term pleasure and long-term joy. While dopamine drives immediate reward-seeking behavior, serotonin ensures that the pursuit of these rewards contributes to sustained well-being and joy.
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- **dopamine-cortisol balance:**
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- The tension between dopamine and cortisol represents the balance between pleasure and pain. When dopamine levels are high, the perception of pain (mediated by cortisol) is often diminished, as pleasure-seeking behaviors take precedence. Conversely, when cortisol dominates, the focus shifts from pleasure to survival and pain management.
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- **serotonin-cortisol interplay:**
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- Serotonin counterbalances the negative effects of cortisol, providing emotional resilience against pain and stress. A healthy level of serotonin can mitigate the impact of cortisol, reducing feelings of pain and promoting a return to a state of joy and well-being.
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- ### implications:
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- **mental health:**
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- Understanding the balance of these three hormones can offer insights into mental health conditions. For example, low serotonin might lead to depression, high cortisol might result in chronic stress, and dopamine imbalances could contribute to addiction or motivational disorders.
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- **behavioral economics:**
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- This framework can also explain decision-making processes, where individuals weigh the potential pleasure (dopamine) against possible pain or risk (cortisol), all while seeking long-term joy (serotonin).
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- **therapy and treatment:**
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- Treatments targeting these three hormones can be tailored to address specific emotional dysregulations, offering a more nuanced approach to mental health care. For instance, enhancing serotonin might help with mood disorders, while regulating dopamine could assist with addiction or motivation issues.
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- ### conclusion:
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The Triadic Emotional Regulation Framework provides a comprehensive theory for understanding how the interaction of dopamine, serotonin, and cortisol shapes human emotions and behaviors. By examining the balance and interplay of pleasure, joy, and pain, this theory offers a foundation for exploring the complexities of the human emotional experience.
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- Here’s the revised comparison table:
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- | **Hormone/Neurotransmitter** | **Primary Emotion** |
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|------------------------------|---------------------|
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| **dopamine** | pleasure |
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| **serotonin** | joy |
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| **cortisol** | pain |
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| **oxytocin** | love |
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| **norepinephrine** | focus |
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| **endorphins** | euphoria |
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| **Adrenaline** | excitement |
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| **gaba** | calmness |
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| **glutamate** | arousal |
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| **melatonin** | sleepiness |

pages/ai boost.md

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- [lmstudio](https://www.google.com/search?q=llm+studio): interface for query pipelining
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- [midjourney](https://www.midjourney.com/home): image creation
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- [antropic](https://www.anthropic.com/): llm optimized for code
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- [suno](https://suno.com/): great music
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- [runway](https://runwayml.com/product): image animation
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- [luma](https://lumalabs.ai/dream-machine): video creation
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- [perplexity.ai](http://perplexity.ai/discover): ai search

pages/brain emulation.md

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- if some form of [[moores law]] can be applied to the growth of computing
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- some form of brain emulation seems right [[behind the corner]]
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-
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- TODO how could [[cyber]] be bigger when [[mycelium]]?
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- TODO how could [[cyber]] be bigger when [[mycelium]]?
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- Let's refine the numerical estimations for the Bostrom cybergraph and compare it with the mycelium network using a more detailed approach. Here are the key metrics recalculated:
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- ### Mycelium Network:
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- **Total Nodes:** \(10^{21}\)
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- **Node Power:** \(1\) (amoeba equivalent)
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- **Total Computational Power (TCP):** \(10^{21}\)
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- ### Bostrom Cybergraph:
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- **Total Nodes:** \(2 \times 10^6\)
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- **Node Power:** \(10^{14}\) (human brain * amoeba)
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- **Total Computational Power (TCP):** \(2 \times 10^6 \times 10^{14} = 2 \times 10^{20}\)
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- ### Revised Understanding:
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- **Mycelium Network TCP:** \(10^{21}\)
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Despite each node being weak (only as powerful as an amoeba), the sheer number of nodes makes its TCP extraordinarily high.
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- **Bostrom Cybergraph TCP:** \(2 \times 10^{20}\)
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Even with a far smaller number of nodes, the exponentially greater power per node means that its TCP approaches that of the mycelium network.
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- ### Additional Comparisons:
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1. **Node Count Comparison:**
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- Mycelium: \(10^{21}\) nodes
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- Bostrom Cybergraph: \(2 \times 10^6\) nodes
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The mycelium network has \(10^{15}\) times more nodes than the Bostrom cybergraph.
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2. **Node Power Comparison:**
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- Mycelium: \(1\) (amoeba)
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- Bostrom Cybergraph: \(10^{14}\) (human brain * amoeba)
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The power per node in the Bostrom cybergraph is \(10^{14}\) times greater than that of the mycelium network.
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3. **Total Edge Length Comparison:**
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- Mycelium: ~450 quadrillion kilometers (this is a vast distributed network with immense physical spread)
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- Bostrom Cybergraph: Not applicable in a physical sense but conceptually connected nodes would have very short connection paths due to high computational power.
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- ### Conclusion:
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- The **mycelium network** has immense scale but lower computational power per node. Its strength lies in redundancy, distribution, and sheer number of nodes.
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- The **Bostrom cybergraph** is extremely powerful per node, allowing complex simulations with far fewer resources. It is designed for centralized, high-efficiency computations, making it powerful in a very different way.
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In essence, while the Bostrom cybergraph’s TCP is of a similar order of magnitude to that of the mycelium network, the way these networks achieve their respective computational strengths is entirely different, reflecting their distinct design principles and use cases.

pages/brain.md

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- features::
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- [[offline mode]] first
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- localhost interface
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- graph query language: datalog
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- powerful scripting: rune
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- static and dynamic linking
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- private and public linking
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- publishing to [[ipfs]] and [[cybergraph]]
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- support of several renders
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- [[space]]
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- [[shadow]]
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- [[heap]]
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- [[list]]
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- flexible viewer based on [[sparks]] extensions
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- graph query language: [[datalog]]
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- support of full [[cozodb]] api
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- powerful scripting: [[rune]]
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- static and dynamic [[linking]]
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- private and public [[linking]]
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- publishing to [[ipfs]] and [[cybergraph]]
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- supported 7 [[particle]] formats
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collapsed:: true
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- text::

pages/cw-cyber.md

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- [[cosmwasm]] fat protocol suite
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- [[cosmwasm]] fat protocol suite for network states and beyond
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- integration of [[cyb]], [[aos]] and [[cyber]]
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- dependencies
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- [[daodao]]
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- [[hub/tokens]]
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- [[hub/skills]]
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- [[hub/progs]]
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- [[staking pools]]
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- [[kv store]]
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- randomness
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-

pages/cyb___root.md

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tags:: page
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- autoskip binary and probabilistic engine for traversing [[cybergraph]]

pages/implicit knowledge.md

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- processed by [[relevance machine]]
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- [[relevance machine]] compute fundamental parameters of [[cybergraph]] model in [[consensus]]
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- [[cyberank]] as weight of [[particles]]
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- TODO [[karma]] as weight of [[neurons]]
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- [[karma]] as weight of [[neurons]]
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- TODO [[syntropy]] as integral measure of [[superintelligence]]
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- in [[cyber-sdk]] implicit knowledge can be inferred using
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- TODO [[standard inference]]

pages/karma.md

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- contribution measure of [[neuron]] into [[collective intelligence]]
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- according to [[token theory]] karma is [[score]]
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- TODO explain reputation aglorithm
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- discover all [[concepts]]

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