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.
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; MAN Training Assistant (MANTA) Private Test (GPU Mining as an integral part)
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.
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