It is worthy of note that majority of users would find it difficult reading through tens and hundreds of pages of whitepaper hence they barely get the real gist in vision of such project. Besides, as informative as the whitepaper is, it can be boring to read due to the technicalities of majority of its contents which is why most users would not give a click.
As potential and effective as Dxchain project is, it will be of a great advantage if the whitepaper can be simplified for an average user to understand and assimilate the content. In another term, it would greatly help investors to see beyond just cryptocurrency and make better decisions of investment.
Businesses can as well easily absorb and digest the core benefits offered by the project to make proper decision. Therefore, this post marks the first of many series of Dxchain whitepaper simplified.
Dxchain as the world's first decentralized big data and machine learning network powered by computing-centric blockchain, has all modules from planning stage to implementation contained in her whitepaper here.
Emphases
The keywords to note in that definition thus:
Decentralizing big data creates easy accessibility to data for anyone looking to work with such data needed for computation such as in the storage industry, geospatial data etc. Dxchain shifts accessibility as it is often available to centralized bodies to platform where anyone desire to work with such data can access it. Also, this can mean storage of large data in a decentralized manner which is achieved using blockchain technology.
To understand this part of Dxchain function, it is pertinent that i quickly dive into what machine learning is. Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
How It Works.
In a way, Machine Learning works in a similar way to human learning. For example, if a child is shown images with specific objects on them, they can learn to identify and differentiate between them. Machine Learning works in the same way: Through data input and certain commands, the computer is enabled to "learn" to identify certain objects (persons, objects, etc.) and to distinguish between them. For this purpose, the software is supplied with data and trained. For instance, the programmer can tell the system that a particular object is a human being (="human") and another object is not a human being (="no human"). The software receives continuous feedback from the programmer. These feedback signals are used by the algorithm to adapt and optimize the model. With each new data set fed into the system, the model is further optimized so that it can clearly distinguish between "humans" and "non-humans" in the end.
Through Dxchain innovative network, big data required in adequate quantity for machine learning computing (a Subset of AI) are made available for easy accessibility and removing monopoly of data. **`End users can potentially use the DX token (Dxchain's platform native token) on the network to exchange or trade data and as a business intelligence analytics platform to analyse data for supporting business insights. Dxchain introduces a new decentralized computing framework that establishes a probable data computing and verification game.
Dxchain is an independent Blockchain. That is, it uses blockchain architecture and set of protocols for implementation of her four major innovations thus:
A new decentralised computing framework to introduce Probable Data Computing
and verification game.
A new chains-on-chain design to orchestrate a master chain and two side chains—Data
Side Chain and Computing Side Chain.
Incorporate Hadoop to DxChain Network to facilitate big data and machine learning.
Flexible and powerful DxChain Network-based system architecture basically designed to support most business data exchange and data analytics requirements.
Stay tuned for next series of Dxchain WhitePaper Simplified.
References:
