Decentralized Analysis System (DAS) Eyes Kensho Remake in an AI Powered Blockchain Ecosystem

Blockchain technology will soon be integrated with Artificial Intelligence for the world to leverage both technologies in this Fin Tech era. Recently in the 2018 BlockWorld Conference, Decentralized Analysis System (DAS), a major player in the blockchain developing space said that it aims to replicate a Kensho-like platform using blockchain.

Kensho is a popular firm for its operations in machine intelligence & data analysis. The company was bought for $550 million by S&P Global, a Wall Street based company whose buy out value is among the most expensive to date. Kensho’s value in the global financial markets has significantly pushed the wealth of its shareholders up. This is because the firm’s Artificial Intelligence built search platform has the ability to make accurate market price predictions.

This was the idea Decentralized Analysis System had in mind when venturing into the blockchain industry. Its main competitive edge is the decentralization aspect of digital ledgers. Statistics show that the current AI oriented firms operate centralized networks which require a huge data handling capacity. Therefore, this eats into a big chunk of the companies’ costs as they have to outsource the space from other potential entities.

Well, this could soon be an issue of the past with the innovation of blockchain networks. These platforms have experienced daily growth since they became famous given that data is unstructured at the moment. Decentralized Analysis Systems may however miss the latest information in the market given their time-specific inefficiency. The crypto community has been challenged by DAS to assist in solving its existing challenges, an approach that may need the blockchain stored data to be structured.

The platform participants will then be able to interact with the DAS interface and save their record based on the ecosystem. However, DAS will store only data that is in the required structure and has been given approval. Rewards in form of the native cryptocurrency ‘DASP’ will be distributed to network DAS network contributors.

In order to have an ample blockchain & AI powered ecosystem, DAS will introduce a 3rd party ‘Reviewer’ who can be from within the community. Their role will be data verification when users upload to the DAS platform using the decentralized avenue. They will oversee that all data is legitimate to avoid scammers who might want to take advantage of the DASP rewarding policy.

DAS is a big player in the data analysis arena as it stands. The firm has a data center running multiple libraries that process data with Tensorflow & Spark emerging as the most notable. These two are an ecosystem for machine learning & an advanced engine for the analysis of data respectively. DAS is well known for its resourceful architecture in pricing market products whereby it has leveraged machine learning & neutral networks for this purpose.

The DAS network uses four criteria to rank the digital assets, a group of market products whose price action is yet to be comprehended even by enthusiasts. This is possible through the in-built features on the neutral networks which rank the digital currencies. The four are; historical data analysis, sentimental analysis, popularity analysis & the general background review.

Furthermore, the DAS infrastructure has integrated an avenue for giving feedback within its blockchain ecosystem. This is built as a back up to rectify any misgivings attributed to the neutral network rating approach. Ideally, participants’ & neutral network ratings will be compared and a loss function incorporated within the neutral network ratings for adjustments.

This ensures that there is fairness and accuracy in the system’s operations. Finally, the new neutral network rating will combine all the feedback for integration with its model to improve the DAS prediction accuracy.

DAS is optimistic that its new development within the cryptocurrency industry will attract more sectors to leverage the blockchain networks!

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