For AI that serves people, data curation DePINs hold the key

For AI that serves people, data curation DePINs hold the key

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Disclosure: The views and opinions expressed here are solely those of the author and do not represent the views and opinions of crypto.news editorial.

In today’s digital economy, a handful of technology conglomerates enjoy unprecedented control over the most valuable resource of the modern era: user-generated data. Companies like Google, Meta, and Amazon have built big data empires by collecting, storing, and monetizing the personal information of billions of users. This centralization of data stifles competition, limits innovation, and creates data silos where only a few have access to it.

While the concept Decentralized physical infrastructure networks– also known as DePINs for short – have successfully mobilized users to participate in decentralized infrastructures – and data remains one of the most underserved sectors. This is where a new field of DePIN comes into play, data processing networks (DCN). DCNs, a term that refers to decentralized networks that capture and organize data directly from users, can offer an innovative solution to break free from these data silos.

DCNs represent a particularly good opportunity for the exponentially growing AI market. AI requires unique, high-quality data sets to function optimally, and large data sets are essential for training models, improving systems, and powering the next generation of applications. DCNs can also handle regulatory matters Fears Transform AI bias by creating diverse, open human-made datasets.

DePIN’s market capitalization has already reached its peak Transgression $50 billion, and it is estimated To reach a potential market value of 3.5 trillion by 2028. This demonstrates the potential of decentralized networks to shift ownership of data back to users and allow them to benefit from their contributions. DePINs offer a transformative solution by moving data collection away from giant corporations and back into the hands of individuals.

As AI technology develops, the demand for diverse, high-quality data will increase. Centralized companies are not equipped to capture the full range of data needed for many AI use cases. Unlike corporate-controlled data sets, which are often biased by the platform’s user base or limited by a company’s reach, DePIN networks can pull data from a wide range of sources. This leads to more comprehensive and diverse datasets, which are essential for building better and more comprehensive AI models, and generating new use case capabilities.

Take, for example, the development of autonomous vehicles. Autonomous systems require vast amounts of real-time data about traffic patterns, road conditions, and driver behavior to operate safely and efficiently. Traditionally, this data has been collected by large companies that have access to connected vehicles and road sensors. Setting up central entities is expensive, requiring investment in infrastructure and significant man-hours. Instead of building this infrastructure and assembling a workforce specifically for this task, crypto networks can incentivize people to turn their terminals into data collectors, passively collecting valuable data throughout their average day. This allows geographically diverse data to be organized more efficiently, and results in organic datasets ready for AI training.

Self-driving vehicles are one of many examples where decentralized networks can collect critical data to improve safety and performance. Combining real-time data from decentralized sources with the analytical power of artificial intelligence could revolutionize industries from transportation to healthcare.

AI models developed to meet human needs must rely on human-generated data as a source of truth for model training. As more and more IoT and wearable devices become equipped with computing power and AI-accelerated chips, and with billions of connected commodity devices such as smartphones, edge-powered DCNs have the potential to expand exponentially, dramatically increase their scope and capacity, and place data Processing on steroids by simplifying the data collection process, and improving the quality of available datasets.

Instead of requiring users to invest in new devices, commodity-based DCNs use devices that are already part of people’s daily lives, such as smartphones and laptops. This significantly reduces the hurdles that often come with hardware manufacturing and distribution, greatly simplifies the setup process, and allows for seamless user engagement at virtually no upfront cost. In the emerging DCN landscape, critical data sets are often curated by curating existing physical infrastructure backed by innovative cryptographic incentives. For example, some projects in the web3 space offer web scraping services through a Chrome extension for PCs, while others leverage smartphone cameras for mapping, demonstrating that by leveraging existing infrastructure, commodity-based DCNs are Reducing barriers to adoption.

In this new model, users are the real beneficiaries. They gain control over their data, enjoy the financial rewards of contributing to decentralized networks, and benefit from the AI-driven innovations these networks enable. This not only creates a more equitable digital ecosystem, but also encourages broader participation in the data economy, ensuring that progress in AI is driven by the needs and contributions of ordinary people rather than the profit motives of a few large companies.

Co-authored this article Ali Reda Quds and Tommy Eastman.

Ali Reda Quds and Tommy Eastman

Ali Reda Quds He is the CEO and co-founder of NATIX. He has a Ph.D. in geospatial localization and extensive experience as a research and development engineer in the geospatial data market, autonomous driving, and building local dynamic maps. Previously, he led the European IoT and blockchain operations for PWC.

Tommy Eastmana research leader at Plaintext Capital, spent two years leading decentralized AI and DePIN efforts as a software engineer at Foundry. Previously, he worked at L3Harris, a defense contractor, where he built a fundamental understanding of artificial intelligence and real-world solutions through machine learning for object detection.

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