lot of cleanups

journals/2024_09_07.md

@@ -1 +1 @@
-- [[random walk attention tokens]]
+- [[random walk cryptographic attention tokens]]

pages/automated market maker.md

@@ -0,0 +1 @@
+alias:: amm

pages/collective intelligence.md

@@ -23,6 +23,7 @@ alias:: collective intelligence theory, collective artificial intelligence
 	- [[trust systems]]
 	- [[cooperative games]]
 	- [[prediction markets]]
+	- [[truth machine]]
 	- more to embrace
 	  collapsed:: true
 		- swarm intelligence

pages/coordination.md

@@ -17,9 +17,11 @@
 	- minimizing conflicts and redundancies
 -
 - key algorithms used in [[cyberverse]]
-	- [[consensus]]
-	- [[market makers]]
--
+	- [[consensus]]: coordination on establishing single sequence of events
+	- [[amm]]: coordination on value measurements
+	- [[auction]]: coordination on value establishment
+	- [[yuma]]: cooperative incentives for optimizing for win to win actions
+	- [[prediction markets]]: cooperative positive sum decisions with zero sum elements
 - TODO algorithms to research for coordination breakthroughs
 	- [[coordination graphs]]
 	- [[swarm intelligence algorithms]]

pages/distributed cognition.md

@@ -1,4 +1,8 @@
-- emphasizes that cognitive processes can be distributed across members of a group and mediated by tools and technologies
-- theory leverages distributed cognition to enhance problem-solving and decision-making
+- TODO
+- cognitive processes distributed across members of a group
+- and mediated by tools and technologies
+- theory leverages distributed cognition to enhance
+	- problem-solving
+	- decision-making
 -
 - or dive into [[collective intelligence]]

pages/distributed neural network.md

@@ -4,16 +4,16 @@ alias:: dnn
 - here we present new architecture of distributed neural network
 - layers
 	- input: [[ask]]
-	- [[cyb/soul]]: define default processing rules for processing
-	- [[will]] regulated by [[$V]]: limits [[cybergraph]] [[bandwidth]]
-	- [[neural]]: expressive semantic language for [[cybergraph]]
-	- [[attention]] regulated by [[$A]]: affects probabilities of [[random walk]]
-	- [[random walk]] measurements: get probabilities on [[nodes]]
-	- [[standard inference]]: compute on gpu [[truthful]] order of particles in [[context]]
-	- [[dynamic names]]: ability to map static names to js and wasm code
-	- [[cyber-cw]] set of [[semantic cosmwasm]] [[progs]] for [[learning]] during execution
-	- processing ordered list of [[particles]] by [[llm]]: local or cloud
-	- [[motivation]] driven by [[$O]]: allow to cover cost base of learning through [[learning rewards]]
+		- [[cyb/soul]]: define default processing rules for processing
+		- [[will]] regulated by [[$V]]: limits [[cybergraph]] [[bandwidth]]
+		- [[neural]]: expressive semantic language for [[cybergraph]]
+		- [[attention]] regulated by [[$A]]: affects probabilities of [[random walk]]
+		- [[random walk]] measurements: get probabilities on [[nodes]]
+		- [[standard inference]]: compute on gpu [[truthful]] order of particles in [[context]]
+		- [[dynamic names]]: ability to map static names to js and wasm code
+		- [[cyber-cw]] set of [[semantic cosmwasm]] [[progs]] for [[learning]] during execution
+		- processing ordered list of [[particles]] by [[llm]]: local or cloud
+		- [[motivation]] driven by [[$O]]: allow to cover cost base of learning through [[learning rewards]]
 	- output: [[answer]]
 - that is what chatgpt gave me to a query of creating diagram of proposed architecture
 	- ![dnn by openai](https://emerald-raw-leopon-384.mypinata.cloud/ipfs/QmW2VPk3K1AeiUc52gcFqFktTPrxxoKKZqjwG9QsWmVbSZ)

pages/elon launch rocket.md

@@ -1,3 +1,3 @@
-alias:: elon launch, launch rocket, launching rocket
+alias:: elon launch, launch rocket, launching rocket, elon
 
 - ![elon launch rocket](https://emerald-raw-leopon-384.mypinata.cloud/ipfs/QmSd59ndqbEjmP4hgQVFUN4rUrHHFGTugGpyCYY9mD46U7)

pages/introduction to bostrom for ai geeks.md

@@ -3,6 +3,9 @@
 -
 - [[bostrom]] is NOT yet another ai coin
 - it is very powerful foundational technology for advanced superintelligent civilization
+- its being used by 1k neurons who create a collective knowledge of ~2 million links
+- in addition to this ~50k [[neurons]] produced ~6 million transactions for decisions related to collective learning
+- currently it produce ~13 megabits bits of [[negentropy]] and takes ~200 mb of ram in gpu
 - in this article i will boil down all essential ideas into coherent understanding how [[bostrom]] can empower
 	- existing ai field which i will refer as classical ai
 	- and advance emerging field of collective ai
@@ -51,8 +54,8 @@
 	- you can boost the power of your models using real cryptographic [[tokens]]
 	- not tokens you use
 	- ![](https://emerald-raw-leopon-384.mypinata.cloud/ipfs/QmQTvZLqTxJNK7K7cFJTeBbKDDWTecXXeExHz4otJkjjfe)
-	- tokens you use we call [[particles]] in [[cyberverse]]
-	- and [[tokens]] are units of [[value]]
+	- tokens you use we call [[particles]] or [[files]] in [[cyberverse]]
+	- and [[tokens]] are units of [[value]] accounted by [[consensus]] system
 - ## cybergraph
 	- the core of the idea is [[cybergraph]]
 		- merkelized timestamped data structure
@@ -141,7 +144,7 @@
 			- [[elon]] [[launch]]
 			- [[launch]] [[rocket]]
 	- these statement is example of complete [[explicit knowledge]]
-	- the good news is that if you are elon, you can just say [[NOW]] [[elon launch rocket]]
+	- the good news is that if you are [[elon]], you can just say [[NOW]] [[elon launch rocket]]
 	- you can pack several [[cyberlinks]] in one coherent [[signal]] so expressions are rich
 	- and use this construct to express anything using [[neural language]] we invented by the way
 - ## why hash everything?
@@ -229,7 +232,7 @@
 	-
 	- but the difference with openai is that [[$V]] give you lifetime subscription, not monthly
 	-
-	- think of link as a link between every query request and answer response
+	- you can think of link as a link between every query request and answer response
 	- > currently 1 V allow to submit 4 cyberlinks per day depending on network load
 	- while you create cyberlinks your battery become less full
 	- your battery recover automatically if you are not creating links
@@ -256,6 +259,9 @@
 		- simple but powerful output as abstract, flexible model of the universe
 		- with potential [[strong predictive power]], especially after [[emergence]]
 	- we use random [[surfer model]] directed by [[attention]]
+	- i wrote dedicated article on this topic
+		- which i recomend to read of anyone involved in modern ai
+		- [[random walk cryptographic attention tokens]]
 	- as foundational global probability of inferring [[particles]]
 	- but in order to
 		- protect it from [[sybil behavior]]
@@ -266,7 +272,8 @@
 		- and context weight on edges which are inferred on request
 	- in order to compute described [[cyberank]] algorithm you need [[gpu computation]] in [[consensus]]
 	- is [[extremely dynamic]] data structure that must be updated even if only 1 [[cyberlink]] is created
-	- [[bostrom]] recompute all weights in truth machine every 5 blocks, or roughly every 25 seconds
+	- [[bostrom]] recompute all weights in [[truth machine]] every 5 blocks
+		- or roughly every 25 seconds
 	- so [[bostrom]] is extremely hard to reproduce using any existing L1 or L2 sdks
 		- zk things will make the stuff
 			- 5 order of magnitude more expensive and
@@ -288,13 +295,13 @@
 	- the environment allow to link any particle with any
 	- that means that for any questions which cyberlinked to `true` and `false` the winning answer will always be `true`
 	- of course such behavior does not feels like something superintelligent
-	- in order to solve [[true-false problem]]  we have to compute weights of links for every [[context]]
+	- in order to solve [[true-false problem]]  we have to compute weights of links using independent second factor for every [[context]]
 	- we always emphasize that [[cyberank]] is a core ranking factor, but not the only one
 	- so we have to introduce second factor to the system
 	- surprisingly with already have [[will]]
 	-
 	- [[standard inference]] algorithm
-	- is the [topic of ongoing research](https://github.com/cybercongress/go-cyber/issues/694) and is implemented only in [[cy]]
+	- is the [topic of ongoing research](https://github.com/cybercongress/go-cyber/issues/694) and is implemented only in [[cy]] and [[spacebox]]
 - ## on two factors
 	- there is the observation
 		- that weights of nodes does not strongly correlate with weights of connections
@@ -315,9 +322,9 @@
 	- and that make [bostrom](https://twitter.com/hashtag/bostrom?src=hashtag_click) so unique
 	- this requires holding all state in GPU ram and use parallel computation at such scale
 	- current size of gpu memory used for ~2 mln particles, ~60k neurons and ~2 mln cyberlinks is ~150mb
-	- submitting just 1 cyberlink force to recompute all ranks (~2 million currently)
+	- submitting just 1 cyberlink force to recompute all probabilities (~2 million currently)
 	- could you imagine how that could be done on [[solana]]
-		- something around 1000 [[$SOL]] currently needed for every update.
+		- something around 1000 [[$SOL]] currently needed for every update
 	- with 10B links
 		- which i believe is required for minimum viable [[superintelligence]]
 		- the task become intractable for all existing blockchain architectures
@@ -338,8 +345,8 @@
 	- we describe and implemented extremely dynamic, collective computation architecture
 	- for predicting probability of information [[observation]]
 	- and defined the most simple possible inference system on top
-	- this technology of [[probabilistic collective computations]] have been created by us since 2016
-	- and we can proudly say that we are leading decentralized ai field on [[cyber]] foundations
+	- technology of [[probabilistic collective computations]] have been created by us since 2016
+	- we can proudly say that we are leading decentralized ai field on [[cyber]] foundations
 	- we believe the thing we have born is powerful enough to bootstrap new kind of [[civilization]]
 	- so we inviting you to the journey of creating open, fair and superintelligent society with us
 - ## [[join]]

pages/knowledge theory.md

@@ -2,7 +2,9 @@ icon:: ⛑
 tags:: model
 
 - simplistic way to understand nature of [[information]] and [[intelligence]]
-- objects:: [[neuron]] [[link]] [[particle]] in [[time]] is the [[knowledge]]
+-
+- definition:: [[neurons]] [[link]] [[particles]] in [[time]] is the [[knowledge]]
+-
 - [[observation]] connection to [[knowledge unit]]
 - [[three basic arguments]] of [[knowledge]]
 - [[explicit knowledge]] is the [[knowledge]]

pages/market makers.md

@@ -1,2 +1,4 @@
 - algorithms which use economic principles
-- to coordinate actions among agents
+- to coordinate actions among agents
+- terms
+	- [[automated market maker]]

pages/neuro-symbolic.md

@@ -1 +1,2 @@
-- TODO
+- TODO
+- convergence of symbolic and neural computations

pages/random walk attention tokens.md → pages/random walk cryptographic attention tokens.md

@@ -1,10 +1,10 @@
 - in this article i want to share mostly unedited output from chatgpt
-- so you can judge for youself potential impact of [[cyber]] protocol
+- so you can judge for youself potential impact of [[truth machine]] and [[cyber]] protocol
 - ## intro
 - introducing a [[random walk]]-based [[pagerank]] model
 - weighted by cryptographic tokens of [[attention]] and [[will]]
 - adds a new dimension to [[graph analysis]]
-- especially in systems with decentralized [[consensus]] and content curation
+- especially in systems with decentralized [[consensus]] and [[content curation]]
 - this kind of analysis aligns with the needs of modern ai industries
 	- especially in optimizing attention-based mechanisms
 	- and recommendation systems that involve collaborative filtering
@@ -31,7 +31,7 @@
 		- token influence decays over time, shifting neuron impact based on recency and relevance.
 		- impact: useful for ai models that prioritize recent trends, ensuring recommendations adapt dynamically.
 	- content distribution hotspots
-		- neurons with similar attention tokens form content-sharing communities, or “hotspots.”
+		- neurons with similar attention tokens form content-sharing communities, or hotspots
 		- impact: helps ai identify key content creators and niche communities, improving collaborative filtering.
 	- token-driven authority and hubs
 		- hits algorithm differentiates content creators (hubs) from validators (authorities) based on token weight.