How is GPT Protocol Building a Decentralized AI Network?

The convergence of AI and blockchain technology has led to the development of GPT Protocol, a groundbreaking framework aimed at establishing a decentralized AI ecosystem. In this post, we’ll delve into the core components of the GPT Protocol and explore its innovative approach to addressing the challenges of centralized control and data manipulation in AI.

Centralized control and data manipulation present significant challenges in current AI systems, leading to compromised data integrity and biased outcomes. Moreover, centralized control undermines user privacy and autonomy.

Infrastructure and Architecture

The GPT Protocol, constructed as a L2 solution on the Ethereum blockchain, is essential in fostering a decentralized ecosystem. The architecture integrates L2 solutions from Polygon’s zkEVM, employing validiums for efficient off-chain AI data processing using zero-knowledge proofs. This strategic decision aims to optimize the balance between on-chain efficiency and off-chain scalability. Which is essential for handling AI and machine learning workloads. We at Gateway.fm provided the L2 rollup and related bridges, faucets and block explorers.

The flowchart presents an architecture of the AI system, so let’s do a breakdown of the components and their relationships:

• Compute Providers: These are the sources of computational power necessary for processing the data and running the AI models.
• Datasets: These datasets are the data sources used to train and validate the AI models.
• AI Models: Connected to all datasets so they may be trained by multiple datasets.
• zk Proofs: Cryptographic verification and security layer within the system. zk Proofs allow one party to prove to another party that they know a specific value, without conveying any additional information.
• AI Interface: The user interface through which users interact with the AI models, sending prompts and receiving responses.

Advancements in AI Computation on L2

By transitioning from PoW to a combination of PoS and Proof of Resources (PoR), mining operations expand to encompass AI model-training and decentralized storage solutions. This involves mining through crowdsourced computational power for AI tasks using pre-minted tokens released on emissions schedule. The result is maximized energy efficiency, redirecting it from traditional blockchain mining to fueling AI’s extensive computational needs. In addition to this, the PoR mechanism enforces accountability, task execution and network integrity.

Roles of Operators and Workers

Operators within the GPT Protocol play a pivotal role in sustaining the network’s chain and overseeing its resources. They are responsible for establishing their presence in the network, managing computational resources and earning rewards based on the service quality provided. Workers, on the other hand, contribute computational resources such as CPU, GPU and storage to Operators and earn rewards based on the completion of AI processing tasks.

The management of workers and operators is done by ‘compute contracts’, which are self-executing smart contracts in the network, governing agreements between operators and workers. They handle operator and worker registration, token staking and resource management. These contracts automate network operations securing the transaction integrity and ecosystem functionality.

Incentives as Part of the Solution

The Rewards System operates as an automated mechanism within the network, monitoring active nodes and task completion. It calculates and distributes earnings across the network, ensuring that workers receive base fees and job-specific compensation, while operators are rewarded for resource management. The protocol incentivizes participation through $GPT token rewards on Ethereum (ERC-20), compensating miners, data providers and developers.

Large Language Models

Another significant aspect of the GPT Protocol is that Large Language Models (LLMs) are engineered to resist censorship and bias, promoting user privacy and autonomy in digital interactions. With the initial implementation featuring the GPT-Neox-20b model, the protocol includes compatibility with a variety of open-source LLMs such as Llama and Bloom.

A Future Solution for AI Resilience

Censorship-resistant AI networks are crucial for fostering creativity and freedom, ensuring that both human expression and autonomous applications thrive without interruption. GPT Protocol lays the foundation for an inclusive AI ecosystem that serves the diverse needs of society. As we progress into the Web4 era, this approach becomes vital, benefitting censorship-resistant AI infrastructure.