The following content is my personal opinion, it does not represent any investment advice.
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On September 27, 2021, PlatON 2.0 White Paper: Decentralized Privacy-Preserving AI Network has been published on PlatON Network Medium in its entirety. It shows that PlatON has done relatively sufficient research and work in preparation for the landing of the privacy AI network. And what is a privacy AI network, please refer to PlatON Cross | PlatON-based Decentralized Privacy AI Network.
Firstly,talking about my thoughts. As the dimensions of the White Paper are continuously improved from 1.0 to 2.0, the commercial landscape has also surfaced. It is a dream but it is also a force. Blockchain + privacy computing + AI, a combination that sounds fashionable. I believe that the organizational relationship woven in PlatON White Paper 2.0 is a conclusion of years of insight.
The self-organization of the decentralized world, the protection and flow of data provided by private computing，and AI continues to evolve itself in algorithms and models. If it is a closed, independent scientific research project, the energy and resources required will be calculated in the basic unit of “10 years”.
I once again see the power and accessibility of the privacy computing track after looked through the listed brands and technical fields.
The business route has been determined, and the next step is to achieve it.
Across L1-L3, this intuitive privacy AI network technology stack diagram, you can see all the heroes of the global privacy track.
Then, let’s start!
Then we will reveal the value behind the LOGO one by one based on the content of the Chinese version.
Layer1: Consensus Network
1.1 The value competition of public chains
Layer1 is the basic protocol of the blockchain, the core is consensus and smart contracts.Layer1 is the basis of decentralized computing, smart contract is a simple computing model, in a sense it is a kind of Serverless. There are a lot of blockchain projects to implement Layer1 of Ethereum model, such as Eos, Cosmos, Polkadot, Algorand, Dfinity, Solana, Near, etc. Algorand, Dfinity, Solana, Near, etc.
Then, the P2P layer, consensus layer, virtual machine, etc. are based on the public chain. Let’s take a look at the star projects of contemporary blockchain technology and next-generation blockchain technology.
Therefore, PlatON made an official feature and performance comparison of some major public chain projects in White Paper 2.0. (As shown below)
Therefore, every public chain project explores at certain two benchmarks, or achieves the ultimate. It is not possible to fully play the role in the game, including physical strength, agility, defense, dodge, and blood recovery. (Super bug)
1.2 Compatible developers
Here in addition to the development direction of the public chain. In order to better welcome developers, we look at virtual machines.
Expand reading Blockchain Virtual Machine, How to Realize the World to Share a Computer?
At present, PlatON supports both EVM and WASM virtual machines, and is compatible with Ethereum’s Solidity contract. The smart contract on Ethereum can be transplanted to PlatON with slight adjustments. It is also compatible with the mainstream, and Ethereum developers can transit. (Why emphasize this issue? Developers are more willing to try if there is good technical compatibility.)
1.3 Private transactions
Monero and Zcash will not be talked with more detalils. Let’s take a look at Manta Network, and protocol of AZTEC , and Raze Network .
- Manta Network is Polkadot’s first on-chain privacy protective project. Through cryptography technology zkSNARK, it provides DeFi users with the highest security level on-chain privacy protection, aiming to become the privacy protection infrastructure for the entire decentralized finance.
- Aztec protocol uses zero knowledge proof to realize encrypted transactions on Ethereum, so that transaction logic can be verified while maintaining anonymity and privacy.
- Raze Network is the cross-chain privacy protocol of the Polkadot ecosystem. It is built on the local privacy layer to provide end-to-end anonymity for the entire DeFi stack.
All above, privacy transaction technology is an inevitable option for privacy security on the chain in the future.
Firstly, back to the characteristics and technical route of the public chain, and look at PlatON aside from ecology. Having native privacy computing capabilities is indeed an innovation in the direction of technology, and it also conforms to some basic settings of Web3.
Secondly, technical settings applicable to multiple layers also contribute to product coverage and strengthen the definition of infrastructure.
Layer2: Privacy-preserving computation network
The amount of data that can be stored on Layer1 is limited, the logic of the smart contract cannot be too complex and does not have access to off-chain data either, so the training of AI models cannot be done in the smart contract.
Privacy-preserving computation network closely combines data, algorithms and computing power to build a complete computing ecology where all subjects, including individuals and institutions, would be financially incentivized to provide personal and professional data. Data security and privacy are guaranteed through through decentralization and secure computing, and subjects are feel more comfortable sharing sensitive data (spending, health information). Over time, the market will accumulate more and higher quality data. Artificial intelligence experts will be motivated to create and share higher performance AI models.
Privacy computing network is the core technology of PlatON! It is also a product that is worth looking forward to regardless of the technical direction of the public chain. Then pay attention to what are the brands LOGO in Layer 2 . In order to facilitate reading, we classify data, computing power, and algorithm into three categories for understanding.
1.1 Privacy-preserving computation network – 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 are also building decentralized interactive marketplace for data labeling that require human intelligence.
Data Market Agreement: An open source agreement for companies and individuals to exchange data and data-based services and profit from it.
Crowdsourcing Market: the practice of outsourcing to non-specific (and usually large) public volunteers in a free and voluntary manner.
As I know about the project Ocean, it located in Singapore, Chinese name: Ocean Protocol.
Project: Allow data providers to interact with data consumers through a decentralized data market, while ensuring the control, auditability, transparency and compliance of all participants. (In short, it is a decentralized data trading market)
Ocean Market’s self-positioning is a data economy tool of Web3 (data trading platform). In March 2018, it raised 16 million US dollars in the token pre-sale and received support from Singapore non-profit institutions. In 2019, as CoinList’s first token sale project, it raised 8 million U.S. dollars.
1.2 Privacy-preserving computation network -Computing power
Lot of the recent progress in AI has been facilitated by a massive ramp in computing power, that resulted both from better leveraging existing hardware, and also building new high performance hardware specifically for AI (Google TPUs, etc).
DeepBrain aims to share idle computing resources from around the world to enable decentralized arithmetic networks. Its general philosophy is comparable to other projects such as Akash, Golem, but DeepBrain Chain is more specifically focused on the type of computing power needed for AI.
Starkware, zkSync are all zkRollup scaling solutions for scaling payment transactions and smart contract transactions on Ether. LoopRing, Hermez are also zkRollup scaling solutions focused on scaling payment transactions and token transfer transactions.
Computing power: It means refers to the ability to process data.
Verifiable calculations: computing tasks can be outsourced to third-party computing power providers; (untrusted) third-party computing power providers need to submit a proof of the correctness of the calculation results while completing the computing tasks.
Part of the solution of Ethereum to issue L2 is involved here. Because the content is too professional, I will only mention it briefly.
Focus on Zk-Rollups (off-chain zero-knowledge proof to complete the transaction), Optimistic Rollups (side-chain to complete the transaction), Verification Game (off-chain game mechanism to complete the transaction), the above three schemes are verifiable calculations
1.3 Privacy-preserving computation network -Algorithm
For a decentralized computing networks to work, it is important to guarantee that whatever data is provided by individuals and companies is processed in a completely private manner.
Enigma, Phala and OpenMined all provide secure computing solutions, Enigma and Phala target general computing, OpenMinded focuses on privacy-preserving machine learning. Enigma and Phala use TEE techniques, and OpenMinded primarily uses Federated Learning, championed by Google, and Differential Privacy, championed by Apple. 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.
Trusted Execution Environment (TEE) is currently the most widely used technology for large-scale commercial use, such as fingerprint unlocking on mobile phones and face recognition. TEE data encryption must rely on hardware devices, the calculation process is performed in an isolated execution environment based on hardware protection capabilities, and a large number of privacy algorithms have been deposited for a long time. The application projects of TEE mainly include Phala Network, Oasis Labs, Enigma, etc.
The Layer2 privacy computing network summarizes the current global leading technology routes and brands, and has good industry support for data protection, algorithm model support, and computing power coordination. PlatON is involved in the above three points, for example The privacy protection of the PlatON basic chain, the computing power support of agents and calculators, and the algorithm model support of the privacy AI network platform.
However, think about this problem from the height of the collaborative AI network! All technical solutions on the market have paved the way for PlatON’s privacy AI network.
Layer3: Collaborative AI network
The privacy-preserving computation network provides the three key elements needed for AI: data, models and computing power. A decentralized AI marketplace will help create better AI. People provide their data, developers compete to provide the best machine learning models, and the entire system acts as a self-reinforcing network that attracts more and more participants and creates better and better AI.
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 actually in the conceptual stage.
The content of L3 has risen to the field of philosophy。This is the AI DAO, a decentralized autonomous organization which is indeed futuristic.
But back to reality, the development of AI still needs the following four aspects to support.
- Independent commercial platform service
- Security and stability of commercial agents
- Independent economic agency mechanism for the cooperation of various AI organizations
- Data resource–PlatON Privacy-Preserving AI Network
Data resources are the most primitive fuel to support the development of the AI industry. User data security, industry data security, scientific research data security, etc. are already became a forbidden area that all countries are committed to maintaining and occupying.
How to make data flow and generate value? Please look forward to the analysis content of the data article.
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