PlatOn Network “Building a better Digital Life”

PlatOn Network

PlatON brings blockchain, AI and privacy-preserving computation together to create a decentralized collaborative privacy-preserving AI network that takes data utilization to a new level. It also serves as an infrastructure for autonomous AI agents and their collaboration that can facilitate the emergence of advanced AI and explore the path to artificial general intelligence (AGI). Based on an underlying blockchain network, we will first establish a decentralized privacy-preserving computation network that connects data, algorithms, and computing power through privacy-preserving computation protocols. Developers can obtain the required resources at low cost, train AI models and publish them to the network, where AI services or agents interact with each other to form a self-organized, collaborative AI network. Anyone can access AI technologies or become a stakeholder in its development on this network, thus achieving AI democratization. The PlatON network creates a new AI fabric that delivers superior practical AI functionality today, while moving toward the fulfillment of PlatON’s AGI visions of tomorrow. PlatON uses a different privacy technology route. PlatON uses secure multi-party computation technology based on cryptography, while Oasis, Enigma and Phala mainly use. New technologies such as machine learning, natural language processing and AI turn data analysis from an uncommon and retrospective practice into a proactive driver of strategic decision and action. Artificial intelligence can greatly step up the frequency, flexibility, and immediacy of data analysis.

Security as a critical foundation

All this data from new sources open up new vulnerabilities to private and sensitive information. There is a significant gap between the amount of data being produced today that requires security and the amount of data that is actually is underpinned by the growth of the Internet of Things. AI will be a crucial tool used to tag web content, and a truly semantic web will enable AI systems to leverage it in new and novel ways. Distributed ledger technology such as blockchain will underpin an intelligently connected Web 3.0, by facilitating data exchange and transactions between divergent systems, manufacturers and devices.
The key to the leap from Web 2.0 to the intelligent web remains the protection of data privacy and the ownership of data being should be able to be controlled by individuals themselves.

Developments and Issues in Artificial Intelligence

Trends in Artificial Intelligence

During the last 5 to 10 years, the rapid growth of the internet, mobile internet and The Internet of Things has generated enormous amounts of data. The increase in chip processing power, the popularity of cloud services and the decline in hardware prices have led to a significant increase in computing power. The broad industry and solution market has

enabled the rapid development of AI technology. AI has been everywhere in daily life, and
AI has been applied in many industry verticals such as medical, health, finance, education, and security.

Characteristics most commonly associated with Web 3.0

Ubiquitous Connectivity

Connect anyone, anywhere, anytime to anything that is open, trustless, and permissionless.

The Semantic Web

Web 3.0 will use efficient machine learning algorithms to connect
data from individuals, companies and machines in a cryptographic way, and machines will be able to understand and intelligently process the data in a human-like manner.

The Intelligent Web

Web 3.0 is an evolutionary path to AGI that can run intelligent applications, such as natural language processing, machine learning, machine reasoning,

and autonomous agents.

Self Sovereignty

Everyone is in control of their own identity and data. No need to rely

on third parties, individuals can sell or exchange their data without losing ownership and privacy. As structures that provide virtual higher-order cognition and self-awareness to the network emerge, interconnection, and become sophistic, the Global Brain will self-organize into a Global Mind.

Technologies of the intelligent web

Where Web 2.0 was driven by the advent of mobile internet, social network and cloud computing technology, the intelligent web vision is built upon three new layers of technological innovation: blockchain, AI and the Internet of Things (IoT). Its ubiquitous nature peoples’ lives whilst mining their private personal data with ease.

Entering the 21st century, many companies worldwide, including internet giants, have been exposed to data leaks and abuse. Google, Amazon, Facebook, Apple and others.
U.S. internet companies have been fined by the EU one after another in Europe in the past two years for data privacy, monopoly, taxation and other issues, which has caused widespread concern worldwide and made people gradually aware of the importance of personal privacy protection. Countries around the world have successively introduced bills to further regulate the market. The promulgation of Cybersecurity Law of the People’s Republic of China and the National Cyberspace Security Strategy in China, and
the General Data Protection Regulation (GDPR) in the EU have had a profound impact on the protection and regulation of personal information.

Expensive Training Costs

While advances in hardware and software have been driving down AI training costs by 37% per year, the size of AI models is growing at a much faster rate of 1000% per year.
As a result, total AI training costs continue to climb. ARK believe that state-of-the-art AI training model costs are likely to increase 100-fold, from roughly $1 million today, to more than $100 million by 2025. In the field of mainstream AI, deep learning has made breakthroughs in recent years, rekindling hopes for “human-like” AI. Technology giants such as Apple, Amazon, Alphabet, Microsoft and Facebook have invested heavily in AGI research and development, with Google spending $540 million to acquire DeepMind in 2014, Microsoft investing $1 billion in Open AI in 2019, and according to a report on general AI by Seattle research firm Mind Commerce, investments related to general AI will reach $50 billion by 2023.

  • The right to use powerful AI models.
  • The right to use algorithms and models without advanced mathematical and computational science skills.
  • The right to use the computational resources required by the algorithms and models.

AI needs Blockchain & Privacy-preserving Computation

Blockchain, privacy-preserving computation, and AI affect and utilize data in differing ways. The combination of these technologies can take data utilization to new levels while enhancing blockchain infrastructure and the potential of AI. Blockchain consensus algorithms can help subjects in decentralized AI systems collaborate to accomplish tasks. The intersection between AI and cryptography economics is another interesting area where blockchain combined with AI can enable the monetization of data and incentivize the addition of a wider range of data, algorithms and computing power to create more efficient AI models.
Blockchain can make AI more coherent and easy to understand, and as all data, variables and processes used in AI training decisions will have an untamperable record, they can be tracked and audited. AI models require massive amounts of high-quality data for training and optimization, and data privacy and regulation prevent effective data sharing. Blockchain and privacy-preserving computation enable the privacy and security controls needed for compliance and facilitate data sharing and value exchange.

Consensus Network

Consensus network is a decentralized blockchain network composed of blockchain nodes, which are connected to each other through a P2P protocol and can be consensual through consensus protocol in an trustless environment. Consensus Network is the basic protocol of blockchain, the core is consensus and smart contracts, it is the basis of decentralized computing, and smart contracts are a simple computing model, which is a kind of Serverless in a sense. There are a lot of blockchain projects to implement Consensus Network, which are basically Ethereum model and decentralized computing, such as Ethereum, Eos, Cosmos, Polkadot, Algorand, Dfinity, Solana, Near, etc.

On Consensus Network, cryptographic technologies such as zero-knowledge proofs and homomorphicencryption are adopted to encrypt blockchain data to achieve data privacy, including

privacy transactions and data privacy in smart contracts, such as Monero, Zcash, Manta, and those based on other Consensus networks such as Aztec and Raze Framework. On the blockchain network, smart contracts can be executed, but due to performance and transaction cost limitations,

smart contracts do not support computational logic of an overly complex nature, and can only access on-chain data with limited storage capabilities.

Privacy-preserving computation network

The basic elements of computing are data, algorithms, and arithmetic power. Data nodes and computing nodes can be connected to the privacy-preserving computation network through P2P protocols to publish data and arithmetic power, and algorithms can be computed using data and computing power. Through smart contracts on the blockchain, a decentralized sharing and trading market for data, algorithms and computing power can be built. Based on the cryptographic economics on the blockchain, data, computing power and algorithms can be monetized, forming an effective incentive mechanism to motivate more data, algorithms and computing power to join the network.
The data in privacy-preserving computation networks is generally kept locally and are available invisibly through secure multi-party computation, federated learning, and other techniques for collaborative computation. Over time, the market will accumulate more higher quality data. AI experts will be motivated to create and share higher performance AI models.

Collaborative AI network

By using the datasets and computing power of privacy-preserving computation networks, AI models can be trained, which can be deployed on the AI network, and served externally through AI agents, forming a marketplace for AI services. Through technologies such as Multi Agent Systems, AI agents can operate independently and communicate and collaborate with each other to create further innovative AI services, enabling AI DAOs and formulation of autonomous AI networks. AI continues to thrive and accelerate through decentralized AI marketplaces. We will have the ability to create many types of AI for almost every task. These AI robots need an effective organizational model to help them cooperate in a transparent manner. Fetch AI works to build and enable Autonomous Economic Agents (AEAs) to cooperate in an organized manner. An AEA is a software entity that can perform actions without external stimuli, and can intelligently search for and interact with other AEAs.

Technology Stack

The technology stack of the privacy-preserving AI network is generalized based on the resources and technologies that privacy-preserving AI relies on. Some existing blockchain projects can be mapped to this stack, but certain projects may not match so well. There are projects that try to combine blockchain, privacy-preserving computation and artificial intelligence, some combine privacy-preserving computation and blockchain to enhance blockchain privacy protection and computing capabilities, some combine blockchain and AI to provide a marketplace for AI services, and some use the decentralization of blockchain to build computing power and data market. Although there seem to be many projects, they can all only meet part of the needs of privacy-preserving AI in a fragmented manner and cannot be combined organically, they have not yet formed a mature privacy-preserving AI ecology.

Data

Ocean and Computable Labs are working to build data marketplace protocols. Snips is using crypto economics to incentivize a network of workers involved in synthetic data generation.

Gems and Effect

AI are also building crowd-sourcing marketplaces, using cryptoeconomics to motivate people to complete data labeling and annotation. A lot of the recent progress in AI has been facilitated by the massive ramp up in computing power, which resulted both from better leveraging of the existing hardware, and also building new high performance hardware specifically for AI (Google TPUs, etc).

Algorithm

For a decentralized computation networks to work, it is important to guarantee that whatever data is provided by individuals and companies is processed in a completely private manner. The Algorithmia project enables an interactive machine learning model marketplace with the help of blockchain, which is actually a model transaction enabled by smart contracts.

SingularityNET

The SingularityNET platform currently focuses on providing a commercial launchpad for developers to launch their AI services on the web where they can interoperate with other AI services and paying subscribers. The Botchain project is a system that provides identity authentication to autonomous AI agents.
A step further than autonomous AI agent cooperation is that the entire network operates completely autonomously, supported by AI. This is the AI DAO, a decentralized autonomous organization supported by AI, which can be a decentralized organization run entirely by AI, with no or limited human intervention. Many companies in this field have ambitious plans, but are currently at the conceptual stage.

Competitive Landscape

Firstly, PlatON is an underlying public chain, which is not inferior to any mainstream public chains such as Ethereum, Eos, Cosmos, Polkadot, Algorand, Dfinity, Solana, Near, etc. in terms of decentralization, security, performance, and smart contract development. These public chains mainly aim to build WEB3 network infrastructure and decentralized application platforms, while PlatON is to build a privacy-preserving computation network as well as an collaboration AI network with the main applications being AI training, AI services and autonomous agents.
In comparison to other projects with privacy-preserving computation such as Enigma,
Oasis and Phala, PlatON focuses on the combination of privacy-preserving computation

and AI.

Game

With the development of the game industry, peoples' pursuit of immersive experiences
and personalization, more and more games have chosen to adopt open settings, Players
have the freedom to create unique content, which are actually the player’s private property.
Some types of games are also exploring the use of players' private data, such as human
body data, geographic location, social relationships, etc. Serious games blur the boundaries between games and general Apps.

Biomedicine

As an AI infrastructure, PlatON provides a credible data collaboration environment for hospitals, pharmaceutical companies, and various scientific research institutions etc. By integrating different types and fields of activities, research fields, operation modes and data streams, PlatON forms a large-scale data aggregation effect, maximizing the value of
pharmaceutical datasets, including clinical trials, medication use, electronic health records
and patient genomics data, etc

Financial Risk Control

Privacy-preserving AI in Financial Risk ControlOperators, internet platforms, insurance institutions and other multi-party data institutions can use privacy-preserving computation technology to open more risk control private domain data tags to collaborate with banks in a confidential manner. The collaboration can better support the financial risk control business, realize the whole-process monitoring before, during and after the loan, and improve the timeliness of risk control.

Smart City

In the 21st century, the intelligence of billions of human groups and the intelligence of tens of billions of machines will form a complex brain-like intelligent giant system of human-machine collaboration through the Internet brain architecture. Imagining such a specific scenario, in a smart city, a blockchain-based multi-agent AI digital services can provide smart mobile solutions in commercial real estate in the city center. Based on PlatON’s multi-agent-based autonomous collaborative AI network, an intelligent collaborative network is formed with the combination of cloud group intelligence and cloud machine intelligence in the Internet brain architecture.

Vision & Goal

Combining blockchain and privacy-preserving computation technologies, PlatON is building a decentralized and collaborative AI network and global brain to drive the democratization of AI for safe artificial general intelligence. To build the infrastructure needed for autonomous AI agents and their collaboration, to facilitate the emergence and evolution of advanced AI, and to explore the path to general AI.
Extend the power of AI to anyone who requires it through our decentralized network and open-source software tools to make the best AI technology accessible for the masses.
The overall goal is to be achieved in three phases.
A decentralized privacy-preserving computation network, establishing a decentralized data sharing and privacy-preserving computation infrastructure network that connects data owners, data users, algorithm developers and arithmetic providers.
A decentralized AI marketplace that enables the common sharing of AI assets, agile smart application development, and provides the whole spectrum of products and services from AI computing power and algorithms, to AI capabilities and their production, deployment, and integration.
A decentralized AI collaboration network that allows AI to collaborate at scale, bringing together collective intelligence to accomplish complex goals

  • Decentralization
  • Privacy-preserving
  • Low training costs
  • Low development threshold
  • Regulatory and compliance

Technical Architecture

PlatON does not attempt to implement the entire privacy-preserving AI technology stack, focusing on the combination of privacy-preserving computation and AI. The overall architecture is followed by a detailed description of each module.

Protocol and Privacy-preserving Computation

The current public chains cannot well meet the computing needs of privacy-preserving AI. Therfore, it is still necessary for PlatON to implement a complete basic protocol to deeply adapt to privacy-preserving computation and privacy-preserving AI.

P2P network
“Protocol and Privacy-preserving Computation”

  • Virtual Machine
  • Smart Contract
  • Privacy-preserving Smart Contract
  • ZKP Library
  • EVM Virtual Machine
  • Giskard Consensus
  • PPoS Economic Model
  • WASM Virtual Machine
  • P2P Network (REsource LOcation and Discovery,RELOAD)

Giskard consensus

Giskard is a consensus of the BFT category, which includes optimization in many aspects. While reducing complexity and further improving throughput through parallelism, it has the

advantages of high performance and low latency.
Three-stage Pipeline validation: After the previous block completes a round of voting, it can move on to the next block, and the final confirmation of a block requires the completion of the previous three block votes.

Concurrent block production and validation

Separate block production and confirmation, concurrently process in Prepare, Pre-Commit and Commit phases.

Communication optimization

Adopt aggregated signatures to reduce the communication traffic, and also provide an optimized version based on leader to further reduce the communication complexity.

View-change optimization

Integrate the view-change process into the normal process, eliminating the need for a separate view-change process.

PPoS economic model

PPoS is a staking economic model in which every LAT holder can participate. Any node that locked more than a pre-determined minimum number of LATs becomes an alternative node candidate. Other LAT holders can lock LATs delegated to alternative node candidates, and the top candidates with the highest number of votes become alternative nodes. After being randomly selected from the alternative nodes using VRF, the validators can participate in block producing and validation. The validators can receive block rewards and transaction fees. The validators and the alternative nodes share the staking rewards with their supporters according to the prior agreement.

Dual virtual machine support

PlatON supports both EVM and WASM virtual machines and is compatible with solidity
contracts. Smart contracts on Ethereum can be ported to PlatON with minor modifications

Privacy-preserving smart contract

Both the EVM and WASM virtual machines have built-in privacy-preserving algorithms (including homomorphic encryption and zero-knowledge proofs) that developers can use directly in smart contracts to protect the privacy of data within the contract. Based on the privacy-preserving algorithms, PlatON has developed a standard for privacy token contracts
that incorporates minting, destruction, and interaction with standard tokens to anonymize

them.

Privacy-preserving Computation Network (Metis)

Metis aggregates the data, algorithms, and computing power needed for computing in a decentralized manner to create a secure privacy-preserving computation paradigm.

Decentralized scheduling

Underlying network is the RELOAD overlay network, data nodes and computing nodes are connected through P2P protocol, and the RELOAD protocol is used to publish, discover, locate and schedule data and computing resources.

Data service

The data subject can either start the data node locally or host the data encrypted to the data node. Upon receiving a computation request, the data node uses secret sharing to slice the data and distribute it to randomly selected computing nodes for secure multi-party computation. The computation task and selection of computing nodes need to be confirmed among multiple data nodes via the consensus protocol. Data nodes can also encrypt the data by homomorphic encryption, distribute it to computing nodes for outsourced computation, and verify the returned computation results and computation proofs using verifiable computation algorithms.

Computation service

Metis supports two different types of privacy-preserving computation protocols and can also be extended with additional privacy-preserving computation protocols. The privacy-preserving computation is performed between computation nodes following the secure multi-party computation protocol, and the computation results are returned to the computation result party through blockchain smart contracts. In the case of AI model training, the completed AI model can be deployed to AI network and become an AI agent to provide AI services to the outside world. Through the blockchain based economic incentives and smart contracts, a decentralized
data, computing power and model trading markets is established on the blockchain network. PlatON realizes privacy-preserving computation economic model, capitalizing and monetizing data and computing resources. In order to ensure the security and validity of data and computation, the economic model contains staking and slash mechanisms. All data, variables and processes used in privacy-preserving computation have tamper-evident
records, which can be tracked and audited.

AI Oracle

Oracles in blockchain are a kind of middleware that connects the blockchain with external resources. The existing blockchain oracles still mainly collect data from other data sources and map them to smart contracts on the chain, or allow smart contracts to call external APIs to provide more functions for the blockchain, but they are still limited to external data acquisition interfaces.
Through the AI oracle, the AI services in the PlatON network can be aggregated and connected to smart contracts. PlatON is easily extended to AI oracle.
Users can easily implement oracle agents, intelligently search and access AI services in the network by using PlatON’s SDKs.

Official Website
Twitter
Medium
Telegram
GitHub

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Jibrin Aliyu

Jibrin Aliyu

Blockchain Enthusiast || Community manager || Brand promoter || Content Creator || Freelancer