Generic computing on Prometheus consensus with open/free market for software, computing power and data
by introducing a single point of failure (which is the center itself):
attacks and leaks in data centers or even at the level of processors
risks of its capture of power (new «nuclear weapon»)
We need to be antifragile and decentralisation is the key. Free & open markets are the most decentralised type of systems invented by humans. However, free market requires censorship resistance, they are synonyms (since each censorship is a constraint, that makes the market closed in some part).
That’s why regulations for AI & computing limit the free market, introduce fragility and long-term risks. They also draw negative economic impact, including higher computing costs, less competition etc.
To secure steady progress and mitigate risks from AI humanity needs decentralised, trustless global computing network with two key properties:
To achieve it we have found proper scientific models (with byzantine fault tolerance, game theory, non-linear and complexity science) and we are developing formally-verified technology. We have an «unfair advantage», since most of blockchain projects do not pay attention to censorship resistance (the only two exceptions are Bitcoin and Monero).
We see that in the future computing will be the core of the economy. Most of it will require running antifragile AI at its backends. Economically, we expect that by creating free market and fundamentally-private and uncensored system we will attract a significant part of the global economy to the network, like Internet has done over the past three decades.
neuroscience, machine learning, complexity science, business & scientific management
Founding director at Bitcoin Foundation Ukraine, Head of BICALabs.org, Soros Prize laureate, multiple scientific awards
game theory, governance systems, finance, deregulation, incentives, competition
PhD candidate and researcher in Athens University of Economics and Business
blockchain, game theory, decentralised systems, software development, information security
CTO at Satoshi Fund
product design, HR
quality assurance & operations in software development, project management, biotechnology, brain-computer interfaces, machine learning, HR
COO, PMO and QA at software outsourcing & AI consulting companies
business management, blockchain researcher and investor since 2012
Entrepreneur, investor in a number of crypto-related projects, including industrial-scale mining facilities
IT business, software engineering, machine learning, decentralisation technologies, blockchain
Dash Core member; Founder at 42coffeecups.com
business development, decentralisation technologies, private equity, stock exchanges, finance
Executive Director at Warsaw Stock Exchange (WSE), Representative Office in Kyiv; Founding head & director of Bitcoin Foundation Ukraine
nonlinear science, game theory, computer science, data science, AI, multiagent systems, technology, machine learning
University of Groningen
Ubisoft team lead
software architecture, system development, C++
10+ years of software architecture design & development
enterprise software architecture, system analysis, machine learning
web apps, APIs
20+ years of software architecture design & development
full stack web software architecture, business analysis, continuous integration
Art-director, UI/UX product designer at software & design agencies
UI/UX, product design, conception, branding & identity, style guides, system design (22 years of experience)
ANN, brain-computer interfaces, topology, learning algorithms
mindhack.me and aiworker.com co-founder and CTO
Ethereum-based PoCW consensus
Start Testnet 1
Python node: ANN inference
Test token economy
of existing protocols
Start Testnet 2
Block production &
First node in Rust:
Kademlia-based p2p networking
Bitcoin Script support
Formally verifiable with proper QA
Most of the popular decentralisation solutions take ad-hoc approach: they create a technology and test it in the real world. While software development QA allows to reduce the number of bugs, it still can not be applied neither to the economic part of blockchain systems nor to general consensus designs. This results in multiple hacks and value losses due to poor design or bad balance of risks and rewards.
Pandora uses proper scientific models (byzantine fault tolerance, game theory, non-linear and complexity science) to design Prometheus — a consensus. Protocol with provable properties. Its implementation in the Pandora network will be formally-verified as well.
Core of the system — an algorithm for proving computing work in trustless environment without repeating actual computing. It utilises game theory with provable Nash equilibriums. Works for:
Prometheus combines PoW and PoS consensuses into two-tier hybrid protocol.
Core of the system — an algorithm for proving computing work in trustless environment without repeating actual computing. It utilises game theory with provable Nash equilibriums.
Reputation (coming from computational proof) at the second tier: proof of reputation, a kind of PoS, but with reputation instead of stake.
instead of energy-consuming PoW and attackable BFT and PoS models Prometheus uses external randomness coming from actual computing work
two-tier consensus renders them economically inefficient
mining rewards come either from useful computing work or from unalienable reputation; it’s not proportional to the current stake of the nodes
multiple types of nodes with different profit models create a truly decentralised environment
Simple full node. Listens to the network, detects byzantine faults and is rewarded for reporting them
Node with locked PAN stake. Performs useful computations (running/training AI models and other forms of computing)
Worker with stake AND high reputation. Checks computing results
Verifier with higher reputation. Participates in arbitrations in case of verifier failures
Top-N reputation nodes of the network, N~1000. Mints blocks at the top level of consensus (proof of reputation)
Blockchain is a technology, not a goal in itself. Blockchain is fundamentally unscalable.
Scalability comes via:
Economic rewards designed with game theory leading to provable Nash-equilibriums for non-byzantine strategies. Two token types are required: