DeepBrain Chain progress report

DeepBrain chain, the decentralized artificial intelligence computing platform for AI products has issued #34 of the DeepBrain Chain progress report. Their native cryptocurrency DBC is traded via smart contracts based on NEO.

DeepBrain Chain progress report #34

DeepBrain Chain, 16/01/2019:

Dear Community,

Welcome to the latest DeepBrain chain Progress Report. Join us as we discuss the latest developments of DeepBrain Chain including technical updates, progress with our cooperative efforts, new details regarding our AI Training Course and the first steps to allowing open source development of the platform.

HuBrain Cooperation Progress

At the invitation of HuBrain, DeepBrain Chain and VisionX will jointly participate in the 2019 Japan International Exhibition on Electronic Components, Materials and Production Equipment (NEPCON JAPAN 2019). Launched in 1972, NEPCON JAPAN is a world renowned expo for showcasing developments in electronic manufacturing and R&D. DeepBrain Chain and VisionX will take the opportunity to showcase the technology behind each respective project and the ways in which this technology can be utilized in cooperation.

For further information about the exhibition, please visit the website:

HuBrain has already integrated VisionX’s AI software into its Quality Control system. HuBrain is well-known throughout Japan as a leading image quality inspection company in Japan and is currently engaged in image quality inspection work for Tesla. Hubrain is looking forward to using VisionX products to enhance AI quality inspection software, increase image quality inspection functions, expand industry customers, and increase sales.

AI Training Course

Deepbrain Chain is offering an AI Training Course to those interested in learning AI specific knowledge from leaders in the industry. The program currently covers three main areas.

1. AI Data Identification

• Learning data identification tools, machine learning, deep learning and more.
• Deploying a machine learning environment on the DBC platform.
• Performing training on the identification of character recognition, ECG heart disease diagnosis, object recognition, quantitative transactions, and model training.

2. AI Product Mechanic Training

• Learn Linux command line and Python programming
• Use the DBC platform to build a machine learning environment
• Learn to use GPUs for image recognition on the AI cloud
• Practice training of the VisionX’s Dataonomy cross-industry image recognition software.

3. Artificial Intelligence Project Training

• Learn linear algebra, Linux command line, Python programming, OOP Python, computer vision and OpenCV.
• Build machine learning environment H2O, Tensorflow, etc. on DBC platform,
• Train neural network in Python,
• Set deep learning environment, in GUI application Practice training such as deploying models on the program.

Technology

GUI Program

We have taken the first steps towards the inclusion of community developers via open sourcing the project in what we have dubbed the GUI (Graphic User Interface) project. We have carefully and diligently selected the first of such developers. For obvious reasons, the security requisites are very serious; our innovative technology is extremely valuable and we must ensure its safety. As such, those that wish to become a community developer must pass stringent tests and credential verification to ensure their trustworthiness. Nevertheless, we are very excited to be entering this chapter of the platform’s development and are confident that the advantages of an open source project are substantial enough to warrant the potential risks.

Expect further details very soon.

R&D team focus:

• Continuous optimization of the DBC network

Architecture Design:

• Support DBC full network grayscale upgrade scheme
• Container core technology design
• Support Skynet network maintenance
• Support reverse proxy
• Support resource cleanup
• Image Add general software installation.

Development:

• Console command line optimization;
• Support Ctrl+C key optimization;
• Support mining machine BenchMark test;
• Support remote restart for DBC mining machines;
• DeepTunnel development: support automatic commencement of IPFS process;
• Revision of WEB personal center.

Skynet Data:

Number of GPUs: 203

The cumulative number of times AI users rent computing power from miners: 358

The cumulative number of AI training tasks performed: 1682

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