SuperGRAD AI networks by collecting decentralized data, embracing data monopolies, and enabling users.
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In the digital economy today, a handful of technology blocs has unprecedented control over the most valuable resource in the modern era: the data created by the user. Companies such as Google, Meta and Amazon built huge data empires by collecting, storing and investing in personal information for billions. This is the centrality of the data competition, determines innovation, and the creation of data silos, where only a few access are limited.
Assembly of AI’s decentralized data through data activation networks
With the development of artificial intelligence technology, the demand for various and high -quality data will only increase. Central companies are not equipped to capture a full set of data needed for many cases of artificial intelligence. Unlike corporate -controlled data groups, which are often biased by the basic system user base or limited through the company’s reach, DePin networks can pull data from a wide range of sources. This leads to more comprehensive and varied data groups, which are essential to build better and more comprehensive Amnesty International models, and generate new use capabilities.
Take, for example, the development of self -driving vehicles. Independent systems require huge amounts of data in actual time about traffic patterns, road conditions and driver behavior to work safely and efficiently. Traditionally, this data has been collected by large companies that can access connected vehicles and road sensors. Building central entities is expensive, and requires an investment in infrastructure and important human watches. Instead of building this infrastructure and specifically collecting a workforce for the task, encryption networks can stimulate people to convert edge devices to data collectors, and collect valuable data negatively throughout their normal day. This allows the formatting of various data geographically more efficiently, and leads to the maturity of organic data groups to train artificial intelligence.
Self -driving compounds are one of many examples where decentralized networks can collect important data to improve safety and performance. The data mix in the actual time of decentralized sources and analytical strength of Amnesty International can revolutionize industries from transportation to health care.
Folding the innovation of artificial intelligence with the reward of users
Artificial intelligence models that have been developed for human needs must rely on the data created by human being as a source of the truth of typical training. Since more and more of the wearable internet and devices are provided with an account and the AI-AICICICID, and with billions of connected basic commodity devices such as smartphones, the DCNS that works at the edge has the tremendous limitation, increasing its diet and its ability to capacity on steroids by simplifying data and enhancing data quality.
Instead of asking users to invest in new devices, the DCNS is used by goods, which are already part of people’s daily life, such as smartphones and laptops. This greatly reduces obstacles that often come with the manufacture and distribution of devices, greatly simplify the movement, and allows the participation of a series of users without almost any initial cost. In the emerging scene of DCNS, meaningful data groups are often coordinated by coordinating the current physical infrastructure supported by innovative encryption incentives. For example, some projects in Web3 Space provide web scraping services through the Chrome extension of personal computers, while others benefit from smart phone cameras to draw maps that by taking advantage of the current infrastructure, the commodity dns reduces the barriers that prevent adoption.
In this new model, users are the real beneficiaries. They control their data, enjoy financial rewards to contribute to decentralized networks, and benefit from the innovations driven by artificial intelligence provided by these networks. Not only does this create a more fair numerical environmental system but also encourage the broader participation in the data economy, ensuring that artificial intelligence developments are driven by the needs and contributions of ordinary people rather than profit motives for a few large companies.
Author: Aliza Ghods & Tommy Eastman
Alireza ghods He is the CEO and co -founder of Natix. He has a doctorate in spatial geographical resettlement and abundant experience as an engineer for research and development in the spatial geographical data market, independent leadership, and the construction of local dynamic maps. Previously, he led the Internet of Things and Blockchain in Europe to PWC.
Tommy EastmanHe spent research in Plaintext Capital, two years old, to lead decentralized efforts and DePin as a software engineer in Foundry. Previously, he worked in L3HARRIS, a defensive contractor, where he built a fundamental understanding of Amnesty International and the real world solutions through machine learning to detect objects.
