Quadrant Protocol

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Quadrant Protocol

Major tech companies have actively reoriented themselves around AI and machine learning: Google is now AI-first, Uber has ML running through its veins and internal AI research labs keep popping up.They’re pouring resources and attention into convincing the world that the machine intelligence revolution is arriving now. They tout deep learning, in particular, as the breakthrough driving this transformation and powering new self-driving cars, virtual assistants and more.But at which specific situation are we going to use ML or AI in or daily life, you may ask. But the real question is, which applications are you using in your daily life that contains Machine Learning or AI algorithm. Because ML and AI are in our daily life already. The only thing is they needed to be worked on more, to be able to be more useful.Today, an AI and ML contained programs can

Recognize objects in images
Navigate a map of the London Underground
Translate between languages
Speak
Recognise emotions in speech
Drive
Fly a Drone
Discover new uses for existing drugs

Overview

Vast amounts of authentic data are needed to power today’s algorithms, however the current data economy is fraught with problems. There is an ever-widening gap between those with the resources to collect and store their own data and those that do not. The data these have-nots do have access to is often fragmented and of questionable authenticity—the kind of data that produces poor results when fed to algorithms. Part of the reason why the data lacks authenticity is because the suppliers of it are not properly incentivized. Fair revenue distribution does not exist for both data producers and vendors. Without a healthy and transparent data economy, the increasing demand for authentic data will not be met. Quadrant aims to solve these problems by providing a blueprint for mapping disparate data sources. It will support proof of data authenticity and provenance via data stamping, the creation of “Constellations” (data smart contracts) for disparate data sources, and fair remuneration and incentive sharing. Data Consumers can trust the authenticity of the data they purchase, “Nurseries” (Data Producers) are compensated fairly every time their data is used, and “Pioneers” (Data Vendors) have the incentive to create innovative Constellations. This new transparent ecosystem ensures that companies get the authentic data they need.Quadrant is designed to work with both centralised and decentralised services. The architecture consists of the core Quadrant blockchain, clients (Data Producer, Data Consumer and Anchor), and Guardian Nodes. Quadrant will operate on a Proof of Authority consensus mechanism so that it can handle more transactions, operate at a lower gas price, achieve faster transactions, and restrict malicious nodes from entering data into the network. An external Proof of Work chain will be used as an anchor for security purposes. For the time being, the Ethereum blockchain will be used for anchoring but it can be replaced by any public chain in the future if needed.

Fair Revenue Distribution

Producers of the original data sources have it the worst with respect to revenue distribution. They need to be incentivised to continue producing data, yet more often than not they are paid just once for the data that they provide. It is the Data Vendors that have the ability to resell the same data again and again. There is no way for the producers to find out what happens to the data downstream, where it goes and for what purpose. What this does is cast an opaque layer over the data, so that the producers have no idea of how much money they are owed.

Atomic Data Producers (ADPs)

At this level of the data value chain, the biggest problem is that the ADPs are not paid their fair share of the revenue made by the data that they produce. Individual data has little value on its own. Its real value is derived when it is combined with other data sets. As a result, most data producers will sell their data up the value chain to aggregators and resellers who can sell interesting data sets alongside one another to multiply the impact of the insights. The problem for ADPs is that they receive payment only once, no matter how many times the data is resold via the resellers and aggregators. Each additional sale beyond the initial transaction (between the ADP and the reseller) does not translate into revenue for the ADP. That is not the only thing working against ADPs. With existing data transaction architectures, there are prohibitive costs incurred in compensating ADPs for the data that they provide. Take a CSV file that has thousands of medical prescriptions sourced from multiple ADPs as an example. Figuring out the exact percentage of revenue to share amongst the contributing ADPs is inherently cumbersome and expensive.

Use Case for Quadrant and Constellations

Let one consider a real example of a data value chain for Mobile Location Intelligence data: a government body is doing a project in which it wants to understand the most crowded roadways in the morning and understand where everyone comes from. To execute this study, the government will enlist a Mobile Location Intelligence company to do this work. This intelligence company will gather data from multiple Location Aggregators who source the data from the many SDK providers that produce this data. These SDK providers install their libraries in thousands of Apps from publishers who in turn have their Apps installed in millions of mobile devices. It is quite complex for such a simple use case. By making Constellations available on Quadrant, they can be aggregated to form new Constellations at each level along the data value chain. It will never be the case in which the top Data Consumer sources directly from the millions of devices. What the Quadrant protocol can enable is that when the top layer Constellation is purchased, then all the corresponding Constellations are also triggered, hence enabling the release and remuneration of each party from the Location Aggregators, to the SDKs and Publishers.

Widespread Token Distribution

New use cases are projected to emerge in which individual devices and sensors are remunerated for their data contributions. Applications that source from IoT or mobile devices require millions of individual endpoints to produce useful data products. Each of these endpoints will require remuneration in the form of micro-payments. These payments will be made using the QUAD ICO token of the Quadrant network.

Token Details

ICO Start Date: 26th June 2018 at 1200 hrs (SGT/GMT+8)
ICO End Date: 26th July 2018 at 1200 hrs (SGT/GMT+8) or when the cap is reachedAmount To Raise: $5M USD
Total Hardcap (Private Pre-sale + Public): $20M USD
Tokens For Sale (Pre-ICO & ICO): 40%
Token Price: $0.05 USD