Introduction to Deep Brain Chain DBC Part 2

How DBC solves problems of AI companies. Team and details.

 

The needs of artificial intelligence companies

Today, projects related to artificial intelligence need several things. They train artificial intelligence on neural networks, so they need vast amounts of data, storage for that data, and computational resources that will train their artificial intelligence algorithms and help them index the data.

The more data they have, the more sources they need and the more time it takes to work with the data. The timeline for a typical project usually lasts from a week to several months. If there are mistakes or software bugs discovered during the training process of artificial intelligence, the project typically needs to start from the very beginning.

Because of the significant competition in the industry, long training times are an extreme disadvantage and increase the chances of competition getting first to the market with the same features. This leads to only major corporations such as Google, Amazon, and Apple being able to develop new features and test new ideas. It is not that small startups do not have ideas, it is that to test the ideas quickly they would need millions of dollars worth of expensive hardware equipment that currently many of them simply can’t afford. Even when a small company is able to break into a market and starts attracting users, the more users it attracts, the more storage and computational resources it needs, which is also problematic. Finally, users often access platforms during certain times, meaning that even if a company is able to purchase the hardware, the hardware will then sit idle for a significant percentage of the time.

DeepBrainChain helps members of the DBC ecosystem in a number of ways.

First, it helps startups and other organizations running artificial intelligence computations lower their costs by running computations on the hardware of other members of the ecosystem.

Second, it helps optimize the performance of neural networks, which makes running computations on the DeepBrainChain network highly efficient and fast.

Third, DeepBrainChain offers a network with low latency and flexible supply of resources.

 

Technical details about DeepBrainChain

Nodes on the DeepBrainChain network can take a number of forms, from full function nodes consisting of large server clusters to individual GPU computing nodes that could consist of powerful home computers and individual users who want to earn extra income by providing the DeepBrainChain network with additional computational power. The income of the miners on the DeepBrainChain will be similar to the income of the miners on the Ethereum network in that it will consist of GAS (rewards for performing calculations) and token rewards for contributing to the network’s blockchain.

To become a mining node on the network, all a user would have to do is install DeepBrainChain mining software.

The DeepBrainChain network will trade its tokens on the NEO network via smart contracts, which ensures safety, stability, and transparency of the system.

The whole DeepBrainChain network will be distributing rewards to the miners on the network once every hour. The total number of coins added to the circulation via rewards to the miners will be equal to five billion. Similarly to how the halving of the rewards occurs on the Bitcoin network, the rewards on the DeepBrainChain network will be splitting in half every five years.

The total circulation of coins on the DBC network will equal 10 billion. Out of this amount, 9% became available for sale during the public sale, 25% become the property of the DeepBrainChain Foundation and would be released in 10 installments over 10 years. 6% became available during the presale, 10% went to the DeepBrainChain team and 50% will be added to the circulation in the form of mining rewards.

 

The team behind DeepBrainChain

The team behind DeepBrainChain consists of highly accomplished and recognized experts.

The chief executive officer of the project is Feng He, who has worked on the first voice assistant in China called Smart 360, which has close to 20 million registered users. He has also led the development of the first Chinese intelligent home assistant called Small Zhi that his team launched 6 months before the launch of Amazon Echo.

The chief technology officer of DeepBrainChain is Shu Chang. Chang has a Ph. D. degree in artificial intelligence from the University of Nottingham.