Nebula AI is committed to building a decentralized artificial intelligence computing infrastructure chain (Zhiyun Chain), which reduces the energy consumption of traditional workload proofs by converting GPU mining machines into artificial intelligence computing services. NBAI tokens are used to purchase computing power, such as: developer testing; use of DAI applications; purchase of DAI training services, etc.

Project goals

In order to improve the current situation of centralized cloud computing, we use the decentralized nature of blockchain technology to rent and distribute computing power to artificial intelligence machines on a global scale . Blockchain encryption technology effectively avoids the existence of internal leaks, and the maintenance of distributed AI computing units is handed over to the owners of large and small artificial intelligence computing units, which greatly reduces the maintenance work quantity. This overall goal can be split into the following sub-goals:
1. Shared AI computing platform
Sharing AI computing equipment platform will solve the problem of the owner and use of AI equipment The unbalanced demand situation among consumers. Owners of AI computing devices cannot use 100% of their computing potential, resulting in idle computing resources. At the same time, a large number of users who need artificial intelligence computing power cannot obtain cost-effective AI computing resources. The point-to-point payment and blockchain bookkeeping technology through blockchain technology can allow the sharing of AI computing power to complete payment and sharing in the most convenient way.
2. AI physical computing unit
A large number of GPU computing mining machines can be converted into AI computing units, thus transforming from simple hash calculations to more meaningful AI task calculations . Due to the particularity of AI computing, it is necessary to pre-install a specified system and regularly update the client, including the accounting system, in order to better utilize the performance of the hardware and share the AI ​​computing power.
3. Decentralized AI application
When a decentralized AI application (Decentralized AI Application) is connected to the system, it needs a corresponding interface to allow DAI App programmers to conveniently Make development calls in this way to use the powerful computing power in the platform. It mainly includes payment API, computing power estimation API, workload estimation API, etc., so as to speed up the development of AI applications.
4. Integrate IPFS distributed storage
Decentralized applications need to use file storage system to store data, one option is IPFS storage system to replace the traditional Chinese Centralized cloud storage or local file storage to achieve better distributed storage.
IPFS InterPlanetary File System (InterPlanetary File System, IPFS for short) is a network transmission protocol designed to create persistent and distributed storage and shared files. It is a content-addressable peer-to-peer hypermedia distribution protocol. Nodes in the IPFS network will form a distributed file system. In the future, most IPFS will use cross-chain technology calls. For cross-chain technology, please refer to cross-chain service calls.
5. AI Engineer Training Center
Nebula AI will establish a systematic artificial intelligence training center to provide basic knowledge in the field of artificial intelligence practice. Learning, project practice, gradually building and training artificial intelligence models in product design. We are committed to disseminating the latest applications and knowledge in the artificial intelligence industry, and cultivating and delivering outstanding artificial intelligence talents. The mission is to fill the talent gap and give full play to the power of artificial intelligence in business.

Value in use

The tokens of the system are used to purchase computing power. When the training data is relatively small, the tokens consumed are relatively small, and when the training data is large, the tokens consumed increase accordingly. The fee paid is related to the training cost and the value of the current token. Calculate the computing power generated by each 1080Ti graphics card for one minute, which is 7514 GFLOP/s×60.

User usage scenarios

1. Quantitative trading
Quantitative trading has been using machines for auxiliary work from a very early stage. Analysts use various quantitative models to design some indicators and observe Data distribution, using the machine as a calculator. Until the rise of machine learning in recent years, data can be quickly and massively analyzed, fitted and predicted, so as to more accurately predict the trend of future financial products. However, the calculation of these models requires a large number of people AI computing capability. If the traditional method is adopted, each trading department needs to build its own data center. And shared computing power can save expensive maintenance costs. Let financial trading firms focus more on forecasting itself.
2. Artificial intelligence learner plan
Colleges and universities are gradually offering artificial intelligence courses. This trend will become more popular in the next few years. When students learn Generally, you will choose to run small tasks locally, and run time-consuming tasks in the school computer room. However, these fragmented tasks can be completely solved by blockchain computing power cloud. The low-cost AI computing service is very suitable for students to complete various computing exercises and quickly modify their own models.
3. Biomedical Artificial Intelligence
The early screening of tumors is of great significance, but due to the small lesion area of ​​early cancers, traditional methods are difficult to judge benign and malignant, which makes clinical diagnosis difficult , Doctors often need to conduct biopsy detection, which not only increases medical costs, but also brings great pain to patients. The application of artificial intelligence to medical image recognition and multidisciplinary collaborative diagnosis can effectively break through this difficulty, improve doctors' diagnostic capabilities, help rapid decision-making, and promote the transformation of medical services to individualization and precision.

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