MATRIX AI Network
A Great Adventure of Blockchain and AI
A Great Adventure of Blockchain and AI
MANTA is an auto-machine learning platform built on MATRIX Mainnet. It enables anyone with no technical knowledge in AI to perform machine learning on a MATRIX node and get the AI models they need.
MANAS is a distributed AI Service Platform built on MATRIX Mainnet. Its functions include AI model training, AI algorithmic model authentication, algorithmic model transaction, paid access to algorithmic models through API, etc. We aim to build a distributed AI network where everyone can build, share, and profit from AI services.
MANART is a platform dedicated to AI-related NFT asset generation and authentication. It has two key elements: 1. the “Big Threes” of AI (Computing Power, Data, and Algorithmic Models), and 2. AI art.
MANITO is an industrial network service platform built on MATRIX leveraging the high-performance blockchain. By incorporating AI, Big Data, IoT and blockchain technologies, it will be a powerful tool for implementing Industry 4.0.
On MATRIX AI Network, MAN tokens are used for rewarding, payment and digital asset authentication.
Block-generating miners, transaction validators, and participants in the MATRIX ecosystem have the privilege to earn MAN tokens as a reward.
From leasing spare computing power to purchasing AI services, on MATRIX AI Network everything is paid in MAN.
The process of converting computing power, algorithmic models, or AI artworks into digital assets involves burning a required number of MAN in the forging of NFTs.
Demo version of MANAS, the AI Service Platform; Release of Watch Man, an upgraded version of blockchain browser; Release of Bank Man, an upgraded MAN wallet; Core network function upgrade on MATRIX Mainnet for distributed GPU computing.
Closed beta test for MANAS, the AI Service Platform; AI algorithmic model authentication platform—MANIA; MANTA (beta).
More than 10 AI applications available for commercial use on MANAS; MANTA available on MATRIX platform; Authentication platform for MANART; NFT trading platform for MANART; Synchronization with FileCoin distributed storage platform.
AI Service Platform agency promotion mechanism; Release of MANART; Cross-chain platforms enabling asset transfer using NFT; Data authentication and NFT generation on FileCoin Mainnet;
A business system involving NFT agencies and auction platforms; An ecosystem of NFT for the trading of computing power, algorithmic models and data; Auto Machine Learning hardware and authentication functions.
Mr. Tao holds a dual bachelor’s degree in Physics and Economics from Peking
University. He has served as CEO of several high-tech start-ups and has
abundant experience managing the development of new products and Internet
technologies.
Noteworthy achievements including leading the development
of a 3D virtual community as well as establishing China’s first embedded
game advertising system.
Mr. Tao also led the development and operation
of China’s first cross-border e-commerce platform; which acquired 20 million
users worldwide.
Professor Deng is an Associate Professor at the School of Software, Tsinghua University
(清华大学软件学院), where he has served as faculty member since 2008.
Professor
Deng’s research interests include machine learning, industry data analytics and computer architecture.
He has authored over 50 papers. His textbook, “Structural VLSI Design and High-Level Synthesis,” is used by
Tsinghua and other universities.
Professor Deng has served as Principal Investigator (PI) and Co-PI for numerous
national level research projects. Since 2016, he has served as Vice Principle Architect of China Railway
Rollingstock Corporation’s Prognostics Health Management for High-Speed Trains Project.
Professor
Deng’s work on deep-learning based image detection was ranked #1 in many prestigious
challenges including PASCAL VOC and VSCOCO; beating out teams from Google, Intel, Facebook and Microsoft. He has
received numerous awards including “Best Paper” at the International Conference on Computer Design in
2013, the “NVIDIA Partnership Professor” Award and the Tsinghua University “Key Talent” Award.
As the marketing director of MATRIX AI Network, Ken has nine-year experience in International marketing and community management. He has diversified channels of International marketing and maintains his own network with figures in the field of commence, politics and media in European and Southeast Asia areas.
Ethan is the vice president of operations of MATRIX AI Network. Graduating from Peking University, Ethan once worked as senior R&D engineer in Microsoft. He has extensive experience in the development of large-scale software systems, computing platforms and multiple blockchain projects.
MATRIX adopts a unique hybrid PoS + PoW consensus mechanism. The PoW is performed in a significantly smaller network of delegates, which are selected with a randomly distributed voting algorithm. The probability of a node to be selected is proportional to its PoS. The “winner” delegate shares the PoW reward with other nodes in its cluster.
Due to the random nature of MCMC computations, the mining of MATRIX is supported by dedicated stochastic hardware. The first generation of mining machine, a Bayesian reasoning machine, was already designed and prototyped. The prototyping design will be integrated as a single IC chip in the timeframe of two years.
Major challenges include:
(1) Programming Barrier
(2) Security Vulnerabilities
(3) Lack of Flexibility
(4) Wasteful Mining
(5) Subpar Speed
