Cyberia

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:

    • 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:

    • in essence cybergraph is an array of append only fully authenticated quadruples

    • | block height | neuron | from particle | to particle |

      | 42 | bostrom1d8754xqa9245pctlfcyv8eah468neqzn3a0y0t | QmRjzv8iNpMX7NXmMswT9qq7nviQ4sC1gMMceryAVJdfPS| QmRX8qYgeZoYM3M5zzQaWEpVFdpin6FvVXvp6RPQK3oufV |

      | 43 | bostrom1d8754xqa9245pctlfcyv8eah468neqzn3a0y0t | QmRjzv8iNpMX7NXmMswT9qq7nviQ4sC1gMMceryAVJdfPS| QmRX8qYgeZoYM3M5zzQaWEpVFdpin6FvVXvp6RPQK3oufV |

    • 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
    • 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

    • 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” class=“internal alias” data-slug=“$v”>volt is will token

    • 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
    • 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
    • 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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