Cyber

introduction to bostrom for ai geeks

Updated Jan 9, 202616 min read

  • source code: @mastercyb
  • status of article: on review
  • 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
    • as we believe its the only viable way to build superintelligence

attention is not enough

  • you used to rely on a data you got
  • you have the dataset
  • you design neural network architecture
  • then, you train the model
  • and boom, now the model can predict some output based on any input
  • sounds really cool, and is powerful indeed, except the dataset thing in this story
  • now the good answers to ask: how does you model could define truth?
  • and the short answer - it cant
  • i will make a bold claim here that truth can not be defined without 3 ideas in foundation
  • knowledge graphs
  • cryptographic proofs
  • token engineering

knowledge graphs and llms

cryptographic proofs and llms

  • we believe that authenticity of models is a serious bottleneck for ai alignment and more
  • its quite strange that so technologically advanced industry in a broad sense
  • still have not advanced to possibilities behind, hashing, pubkey cryptography, merklization and logical clock
  • its kinda impossible to build multiparty protocols without these primitives
  • yep, i am ware about zkml movement
  • but this is a drop in the ocean given the knowledge graphs and llms argument
  • if we want to significantly advance in the field of superintelligence
    • we need something foundational
    • fully authenticated knowledge graph tech
    • which is cybergraph, but later on that

token engineering and llms

  • rewarding is essential for machine learning
  • we have ton shit of tokens with dogs, monkeys
  • you can boost the power of your models using real cryptographic tokens
  • tokens which are being used in ai field 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
  • of links between ipfs hashes
  • submitted by anyone
  • for clarity we refer to:
  • ipfs hashes as particles of knowledge, and data behind particles as files
  • links as cyberlinks, as they are unique and very different type of links
  • submitting agents as neurons, as they looks very similar
  • notes on implementation
  • timestamping in bostrom is done using simple and reliable tendermint consensus algorithm
  • sybil protection, rate limiting and motivation are implemented using energy set of algorithms
  • cybergraph is explicitly answer 3 fundamental questions:
  • who linked the information
  • when information was linked
  • what information was linked
  • in essence cybergraph is an array of append only fully authenticated quadruples
block heightneuronfrom particleto particle
42bostrom1d8754xqa9245pctlfcyv8eah468neqzn3a0y0tQmRjzv8iNpMX7NXmMswT9qq7nviQ4sC1gMMceryAVJdfPSQmRX8qYgeZoYM3M5zzQaWEpVFdpin6FvVXvp6RPQK3oufV
43bostrom1d8754xqa9245pctlfcyv8eah468neqzn3a0y0tQmRjzv8iNpMX7NXmMswT9qq7nviQ4sC1gMMceryAVJdfPSQmRX8qYgeZoYM3M5zzQaWEpVFdpin6FvVXvp6RPQK3oufV
  • i want to make it clear that notion of cyberlink is essential for the architecture described by this article
  • in conventional ai workflows you used to train over static datasets which already have been created
  • collective memory require to change our thinking on how knowledge emerge
  • good question to ask is what is the most small possible unit of learning?
  • conventional thinking is the notion of triple, which consist of subject, predicate and object
  • now lets ask the question what is lacking in this construction if our goal is to have provable statement?
  • first
  • second
    • we need to add notion of particle
    • for predicate and object
    • in order to authenticate these arguments
    • and give an answer to what question
  • and third
  • from this we arrived to a quadruple which is fully authenticated knowledge
  • we gave this a name: cyberlink

  • as the most fundamental such an atomic unit of knowledge and learning

  • the key to quantum jump of civilization
  • you append cyberlinks to the state of collective thought evolution
  • introducing delete make indexing a complex task
  • also its obviously not how nature works: you just cant forget in your head by wish, they forgotten by itself
  • although looks primitive, cybergraph is so much needed formal definition of explicit knowledge
  • lets analize a statment that cybergraph is complete form explicit knowledge
  • temporal dimension: when
    • including a timestamp offers a temporal context for each action
    • pivotal for grasping sequences of events, causality, and the unfolding of relationships over time
    • it facilitates tracking changes, comprehending the sequence of actions, and deducing patterns based on temporal data
  • agency and responsibility who
    • identifying the public key of the actor bestows agency and responsibility upon each action
    • crucial for ensuring accountability, authentication, and scrutinizing interactions at the individual actor level
    • this feature also aids in retracing actions to their sources, bolstering security and trust frameworks
  • relationships and interactions what
    • the structure distinctly portrays relationships and interactions via directed links from one content address to another
    • this aspect is vital for deciphering the network of connections among entities, the circulation of information or influence, and the overall architecture of the system
    • direction embed the following types of information
      • cause and effect
      • sequences
      • hierarchy
    • it is vital for tasks like planning, problem-solving, and decision-making
    • in nature relationships are inherently asymmetrical, so we cover it
  • the structure is extendable with motifs which can be constructed using signals
  • semantic conventions add additional layer of flexibility
  • hence, we can refer to cybergraph as objective knowledge of everyone

cybergraph vs knowledge graph

why hash everything?

  • yep, we know - you used to tokenize your data and make it as dense as possible
  • yes, we know - hashing data requires 32 bytes for every piece instead of several bytes
  • yes, we know - that make processing more expensive
  • but hashing have some superpowers (yet) unavailable for you
  • multimodality
    • your model cant infer answers in full content space
    • why your model have to reinvent all data every time?
    • people would love to have answers with content they love
  • universal, static, abstract model
    • fixed length give a room for soft optimization as you don’t need to think about typing
    • types can be created by implicit knowledge, e.g. by topology of links, so typing is the job of cybergraph and learning technics on top
    • fixed length for hardware optimization means that specialized hardware can be simple and efficient
  • peep to peer
    • since bittorrent times its clear that content addressing is the only way for reliable peer to peer exchange
    • ipfs being the leading p2p data exchange protocol and software open enormous abilities for collective ai interactions
  • saga on evm and price of computations
  • there was foundational decision to start from 256 bits architecture
  • everyone around say we were crazy
  • but looking back i do believe it is very powerful decision of founders

  • they will say: you never want exchange aka tokens for hashes

  • but once you got it, you have no way back

why merkelize?

  • automatic deduplication
  • while the means of deduplication is hashing what makes it practical is merklization
  • small changes of files lead to a change of only some leaves, not all underlying file
  • merklization significantly reduce data storage requirements for incremental updates
  • proving in multi agent setting
  • merklization is the core of blockchain technology
  • but why does classical ai needs it?
  • well, the truth is that its likely don’t
  • but if you design a multiparty computation system you must have ability to prove pieces of data you have
  • in case of cybergraph, existence of any given link (and more) can be proved by alice to bob by giving
    • link
    • root hash of cybergraph
    • path in cybergraph
  • this opens the door for mirriad applications for multiparty computation, such as
  • i also asked chatgpt how merkle trees can be used in classical ai field?
  • data integrity and verification
  • merkle trees can be used to ensure that the data used for training ai models has not been tampered with
  • this is crucial for applications where the authenticity and integrity of data directly affect the model’s performance and reliability
  • version control for datasets
  • by using merkle trees, ai practitioners can maintain a tamper-evident history of changes to datasets
  • this allows for better management and auditing of data versions used in training models
  • decentralized ai models
  • secure model sharing: merkle trees can facilitate the secure and efficient sharing of ai models in a decentralized manner
  • by breaking down the model into smaller chunks and organizing them in a merkle tree, the integrity of the model can be verified without needing to download the entire model
  • collaborative training: in scenarios where multiple parties contribute to the training of a model without wanting to share their data directly, merkle trees can ensure the integrity of the contributed data.
  • this aids in building trust in collaborative ai projects
  • now you see that everything you know about highly efficient information dense models just will not work for multi agent adversarial environments. NO WAY. sorry to tell you that.
  • image.png

why new blockchain?

  • the cool thing in cybergraph idea is that it is entirely blockchain agnostic
  • data structure can be reproduced in any blockchain environment and in local offline environment too
  • and that makes it so powerful
  • but applications of cybergraph are limited within existing blockchain environments
  • expensive, fee based usage
  • no means of computing cool stuff in consensus as cool stuff is inherently parallel
  • bostrom solves both of these problems, but more on that later
  • also bostrom organically formed cybergraph of several million cyberlinks and particles
  • that is on par with capability of tech giants for manual labeling during finetuning
  • and bostrom is provably accelerating …
  • so you can use this cybergraph
  • as toy dataset in your conventional ai workflow experiments
  • with graph neural networks too
  • a lot of smart guys are say that people will never want to pay fees for every social interaction
  • the truth is that information emerge from communications and social interactions
  • so if we will not provide a convenient way for that
  • its likely we will not achieve practical results in collective learning
  • we believe that social layer over cybergraph is essential for the development of an idea
  • that is why bostrom offer a model of usage based on bandwidth
  • the model is practically the same as being already used in chatgpt
  • $V or volt is will token
  • allow to create cyberlinks
  • and derive truth using standard inference
  • but the difference with openai is that $V give you lifetime subscription, not monthly
  • 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
  • so effectively buying $V you buy a package for lifetime usage

  • current price of V is something around $1

  • that means that for 1$ anyone can get around 4k interactions during 3 year of usage

  • for ~$10 you can have enough interactions comparable with your average twitter, github or chatgpt usage

  • for ~$30 you can link all your public photos, music, videos and documents collected during life

  • for ~$100 you can describe some domain of science or the core of any language

  • you see how cool is lifetime subscription model of bostrom
  • this approach also work as
  • spam protection
  • partial sybil protection
  • and as inference factor (read further)

truth machine

standard inference

  • obviously in our setting the simplest possible way
  • to infer particles in the context of any particles
  • would be to sort by random surfer probability
  • but this led us to a kinda true false problem
  • let us imagine that true particle have cyberank 10, and false particle have cyberank 9
  • 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 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 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
  • in both natural and artificial systems
  • relevance machine coupled with standard inference runtime learns based on two fundamental factors
  • attention: more about weight on nodes
  • and will: more about weight on edges
  • and yep, you have to pay in order to learn bostrom
  • because otherwise it seems impossible to protect cybergraph from abusive behavior
  • so in essence
  • in proposed distributed neural network
  • attention and will serves as
  • cost factors which defined by computing resource factors
  • yep, our truth model is fundamentally two factor

on speed

  • bostrom is extremely dynamic blockchain, the first in its kind
  • recomputes probabilities of observation every 25 second for every information piece that was submitted (currently ~2m)
  • and that make bostrom 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 probabilities (~3f million currently)
  • could you imagine how that could be done on solana
  • 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
  • current bostrom architecture can handle (rough optimistic estimations) up to 1T cyberlinks
  • on par with GPT4 with 1T parametrs
  • but in blockchain, baby
  • to be honest things cant be compared 1 to 1, far from it

learning incentives

  • all benefits of proposed system fades out under assumption that you have to spend resources on learning
  • what is motivation to do it?
  • the solution is to make a system which will rewards high quality learning based on subjective evaluation
  • we reimplemented yuma, a coordination consensus and now testing it in spacepussy
  • in coming months we will deploy it to bostrom
  • so players that make links above some quality threshold could have possibility of break even

conclusion

  • the article does not touch topics of all bostrom features
  • purpose is to give a sense of key internals in the context of deai development
  • 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
  • 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

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