Exploring Zero-Knowledge Artificial General Intelligence Within The Context Of DePIN

We live in a world where powerful technological forces such as AI and blockchain are not only shaping our digital destiny but also redefining the boundaries of innovation. A prime example on our radar is DePIN (short for Decentralized Physical Infrastructure Network), an amalgam of web3 ecosystems offering cost-effective solutions to the everyday challenges faced by humans.

DePIN empowers a more democratic future for computing power through a vast global network where everyone can participate in a resource-sharing economy. As a peer-to-peer infrastructure, it unlocks efficient distribution of computing resources and allows users to earn rewards for their contributions.

This innovation seeks to bridge the gap between the digital and physical worlds through blockchain technology. There have been different groundbreaking DePIN projects such as StorX Network, Drife, Helium Network, Healthblocks, Smartpoints, Airweave, etc. Drife, for example, is not only poised to take Uber and Lyft out of business but it is also set to disrupt the transportation sector.

The emergence of DePIN has paved the way for innovators to create cost-effective solutions to the inefficiencies and limitations of the existing critical infrastructure such as transportation, water supply, energy, communication systems, financial services, healthcare, and defense. Most especially, it is set to eliminate centralization and encourage decentralized ownership of these critical services.

As an independent researcher, I find DePIN projects fascinating enough to be studied individually. In this article, we explore Aten Krotos (aka Suraj Venkat) and Arthava’s works on  Zero-knowledge Artificial General Intelligence(ZkAGI), an open-source AI project built on DePIN. Leveraging Zero-Knowledge and DePIN, the duo proposed ZkAGI to tackle the privacy concerns in AI.

What Are Zero-Knowledge Proofs(ZKPs)?

Zero-Knowledge Proofs (ZkPs)are a cryptographic technique that allows one party (the prover) to convince another party (the verifier) that they possess a specific piece of information, without revealing the information itself. In a zero-knowledge system, a prover convinces a verifier they hold a secret without revealing it, using a special proof and verification key.

This relatively new field  has become a cornerstone of privacy in the cryptocurrency world, reinventing how sensitive data or information is shared without it being revealed. Since the keypoint about ZK is to share data while preserving privacy, the team made a strong case for extending Zero-knowledge  use case to Artificial Intelligence(AI) within the DePIN ecosystem.

As an open-source DePIN project, ZkAGI seeks to tackle the challenge of privacy in AI by combining two cutting-edge technologies: Zero Knowledge (ZK) and Decentralized Physical Infrastructure Network (DePIN). This innovative approach highlights the need for secure and private AI computation on a cost-effective platform.

Using a diary analogy, Aten, the CEO and Founder, describes it as “a secret diary where you store important information but the diary not just records thoughts but understands what has to be done and gets it done”!

Defining Zero knowledge Artificial General Intelligence(ZkAGI)

ZkAGI (Zero-Knowledge Artificial General Intelligence) is an open-source framework that facilitates secure and private AI computation on a decentralized network (DePIN). By integrating federated learning and zero-knowledge proofs, ZkAGI incentivizes all participants – GPU providers, trainers, developers, and data owners – to contribute to a privacy-focused AI ecosystem.

Aten (Suraj) the founder and CEO of ZkAGI writes that: “ZkAGI addresses the privacy issue in AI by merging Zero Knowledge (ZK) technology with decentralized GPU infrastructure (DePIN), offering secure and private AI computation on a cost-effective platform.”

“Leveraging Zynapse, our ZkML Coprocessor, the API empowers seamless AI operations without exposing sensitive data, making it the prime choice for developers and enterprises seeking privacy-centric AI solutions with high processing speeds and significant cost savings.”

Zynapse API

As an open-source DePIN project, ZkAGI offers valuable insights into Zynapse, its proposed solution to privacy concerns in AI. According to Aten, Zynapse is a ZkML(Zero Knowledge Machine Learning) Coprocessor, which empowers seamless AI operations without compromising sensitive data.

The associate API for Zynapse makes ZkAGI the ideal choice for developers and enterprises seeking privacy-centric solutions, high processing speeds, and cost-effective solutions. The choice for DePIN is justified by the fact that it offers efficient and scalable AI computation and significantly lowers the cost compared to what traditional AI platforms offer. By leveraging Zynapse, the goal is to help “ developers and enterprises unlock the full potential of AI while ensuring the privacy of their data”.

Components of Zero-Knowledge Artificial General Intelligence

It is crucial to briefly discuss the layers upon which Zero-Knowledge Artificial General Intelligence(ZkAGI) as a DePIN project is built. These layers are the basic building blocks for ZkAGI and they include: DePIN, Zk,Decentralized Data Ownership, Federated Learning and Incentivization.

DePIN

Aten defines DePIN “as a network of physical infrastructure resources (such as computing power, storage, and networking) that are distributed across various locations rather than being centralized in one data center”,highlighting the fact the this decentralized model offers benefits such as improved fault tolerance, scalability, and reduced latency.

Incentivization/Resource-Sharing

Experts have identified the key advantage of DePIN projects as their ability to provide token incentives, which helps to boost the supply-and-demand side of it . It is essentially the lifeblood that is sustained by participants who contribute their computing power and users who are in need of it.

In the words of Aten: “ZkAGI aims to motivate various stakeholders within the ecosystem. GPU providers are incentivized to contribute their computational resources to the network, model trainers are encouraged to develop and refine AI models, and AI developers are motivated to create innovative applications. These incentives can take various forms, such as financial rewards, access to network resources, or reputation within the community”.

Decentralized Data Ownership

As the AI landscape continues to expand at a breakneck speed, one major concern that has recently surfaced in AI and data-driven applications is the ownership and privacy of data.

According to Aten, solving this challenge through ZkAGI requires “implementing mechanisms to enable data owners retain control over their data while still allowing it to be used for training AI models. This solution may incorporate techniques such as federated learning “where models are trained collaboratively across multiple devices or servers without sharing raw data, and zero-knowledge proofs, which allow parties to prove the validity of a statement without revealing the underlying data”

Federated Learning

This underscores the role and importance of a machine learning model in the development of ZkAGI as a privacy-centric AI built on a Decentralized Physical Infrastructure Network. Aten describes Federated learning as “a machine learning approach where the training process is decentralized, and individual devices or servers collaboratively train a global model without sharing their raw data”.

ZK Proofs

As discussed earlier, Zero-knowledge proofs are cryptographic techniques that allow the sharing of sensitive data or information between one party(the prover)and another(the verifier)without revealing its content. In the context of ZkAGI, zero-knowledge proofs can be used to verify the integrity of computations or to attest to the accuracy of AI models without disclosing sensitive data.

Conclusion

DePIN as a web3 innovation is revolutionizing how we access and share computing power. This shift fuelled by its potential to provide cost-effective solutions has seen the rise of groundbreaking companies such as Drife, a decentralized ride-sharing platform, and StorX Network, a decentralized cloud computing platform.

This decentralized approach also empowers a more democratic future for AI. However, the demands for more powerful infrastructure, incentives for stakeholders, and heightened concerns for privacy are growing. The pressing concern over privacy protection in AI gave birth to ZkAGI, an innovative open-source framework poised to reshape AI data privacy within DePIN ecosystem.