What Makes Matrix AI Different? (part 2)
In the following part, we will compare Matrix with a few well-known AI projects.
Matrix vs DeepBrain Chain
DeepBrain Chain is essentially doing the same thing as Matrix: building an efficient distributed computing power network. But beyond this, there are differences:
Consensus Mechanism vs Consensus Algorithm
DeepBrain uses a DPoS consensus mechanism, while Matrix has a DPoS+PoW consensus mechanism. Matrix also employs AI-based random clustering algorithms in its DPoS. Of the two, Matrix's consensus mechanism is more secure, and with AI algorithms in its PoW, Matrix has more thoroughly integrated AI into its consensus mechanism.
Currently, DeepBrain is focused on building a computing network made up of multiple powerful computing centers. In contrast, Matrix is building a truly decentralized network. Both arrangements have their advantages. DeepBrain Chain is more suited for large-scale computing centers, while Matrix is better at gathering ordinary users' and companies' spare computing power. By the same token, DeepBrain is better for time-sensitive AI tasks with low privacy requirements, while Matrix is better for non-time-sensitive tasks requiring a high privacy level. At the same time, Matrix's decentralized structure will make its computing power more affordable.
DeepBrain envisions a computing power distribution platform, while Matrix is not limited to computing power but also aims to build a data platform, an algorithm and service platform as well as a trading platform. This complete ecosystem will be to Matrix's advantage in attracting more algorithm scientists.
Matrix vs Singularity NET
Singularity NET aims to build a distributed AI platform where AI algorithms are backed up onto every single node on the blockchain. AI scientists can also provide their own AI algorithms and services to Singularity NET’s users, who will pay for the services with the designated token. In this sense, its business model is similar to MANAS of the Matrix ecosystem, although there are differences too.
Singularity intends to use its own platform to host all transactions and has built its own commercialization team. In contrast, Matrix will primarily focus on developing new technologies and platform features, and for the commercialization, Matrix will provide API and SDK for anyone with customer resources or experience operating a business to build his/her own AI cloud computing/AI service platform using our tools. This is an ecosystem where all parties can play to their strengths.
A Single Algorithm Service Market vs A Complete Ecosystem of Market, Data and Computing Power
Singularity provides a secure and easy-to-use market for AI scientists to sell their algorithms. Matrix goes one step further by providing computing power and data for AI scientists as well, which makes working on Matrix very convenient for the scientists. Users of algorithm services on Matrix can benefit from cheaper computing power, and Matrix miners are also better rewarded.
Matrix supports minting algorithm scientists’ works into NFTs to protect their intellectual property. This will also create more liquidity for algorithms on Matrix and give algorithm scientists a new way to profit.
Matrix vs Fetch.ai
Fetch.ai aims to use AI technologies to bring real-world people, equipment, and even companies into the digital world and build an autonomous economic agent (AEA). Simply put, Fetch.ai is an automated data transaction network. So how is this different from the data platform Matrix is building?
Fetch.ai uses the Sharding technology of DAG for data storage. This is a different type of decentralized storage from Matrix’s more traditional IPFS storage, and only time can tell which one is better.
Trading & Usage
Fetch.ai is purely a data trading platform. Its innovative UPOW is advanced in many ways. However, the current design of Fetch.ai makes it relatively easy to duplicate data on its platform. Matrix, on the other hand, has separated the ownership and usage rights of data, making data easier to be traded. Matrix’s distributed machine learning system (federated training) also protects data from being duplicated. This guarantees the value of data so that owners can keep profiting from their data assets into the future.