Phala Network and Pond are thrilled to announce their partnership on developing trading AI agents! As a leading DePIN Coprocessor, Phala transforms decentralized AI agents through its AI-Agent Contract platform. Phala develops an ideal toolset for constructing intelligent applications, with the aim of revolutionizing agent control and governance. By leveraging Pond’s Graph Neural Network model, Phala users will have access to precise on-chain behaviors and predictions for their trading research, enabling the development of advanced and AI-powered trading strategies.

About Pond

Pond is building a decentralized Graph Neural Network (GNN) model for Web 3.0. This model is designed to learn on-chain behaviors and predict future behaviors. Developed by leading data scientists and machine learning researchers, our model is the first of its kind and will support crypto-native use cases such as on-chain trading, leveraging on-chain liquidity, social networking, security, and more.

By harnessing the inherent structure of data and delivering enriched and denser information, Pond's model facilitates more informed decision-making and driving innovation across various sectors in crypto.

Focusing on decentralization Ethos & simplified Tokenomics

Phala’s AI-Agent Contract transforms AI agents into autonomous entities, emphasizing decentralization to promote unbiased decisions free from central control, leading to more resilient, transparent AI aligned with user interests.

AI-Agent Contract integrates tokenomics, revolutionizing governance and incentivization within the ecosystem through cryptocurrencies. This approach rewards contributions and enables decentralized, community-driven management of AI agents, enhancing participation and autonomy.

Tackling dAGI challenges together

This partnership aims to address several key challenges facing decentralized AI:

1. Complex and Noisy Data: On-chain data is often unstructured, complex, and noisy, including various types of transactions, smart contracts, and market anomalies that need to be organized for effective modeling.

2. Lack of Standardized Datasets: The AI industry lacks standardized evaluation frameworks, a problem worsened in Web 3.0 as most protocols do not share their datasets or code, requiring each protocol to develop its own evaluation methods.

3. Evolving Technology: The rapidly changing nature of permissionless networks makes it hard to create models that stay accurate over time.

4. On-Chain vs. Off-Chain Data: Effective models need to consider off-chain factors like social media and macroeconomic influences, adding complexity to implementation.

5. Scalability: The enormous volume of data from blockchain networks poses challenges in creating and maintaining efficient models, especially when considering cross-chain data.

6. Limited Resources and Tooling: There are few tools and resources for developing AI models for crypto, making the process time-consuming and costly, often requiring custom solutions.

Together, Phala and Pond will work on research and development to further push the boundaries of AI & web3.

Stay tuned for more details here:

https://cryptopond.xyz/

https://phala.network/ai