Verifiable Compute for AI, ML and More: Web3's New Frontier | Alison Haire at SmartCon 2023

Summary

Alie, CEO and co-founder of Lilypad, discusses the project's focus on verifiable, trustless, decentralized compute as the new frontier for Web 3.0. Lilypad, incubated at Protocol Labs, aims to provide a scalable, trustless compute network with a global marketplace for GPUs, enabling mainstream adoption of Web 3 applications. Alie highlights the practical and ethical challenges of centralized AI and ML, emphasizing the importance of decentralized compute for innovation, accessibility, and data ownership. The project also envisions incentivizing node operators with LP tokens and plans for multichain compatibility.

Takeaways

• 🌐 Alie, the co-founder and CEO of Lilypad, discusses the project's incubation at Protocol Labs, highlighting the company's involvement in the decentralized web 3 space.
• 📈 Lilypad aims to address the current gap in verifiable, decentralized compute, which Alie sees as a critical component for the modern internet and a frontier for web 3 development.
• 🤖 The project focuses on AI and ML compute, aiming to provide a trustless and decentralized platform that can be utilized at an internet scale, facilitating mainstream web 3 applications.
• 🔐 Verifiable compute is presented as a solution to ensure the integrity and security of computations, which is particularly important as AI and ML become more integrated into web services.
• 💡 Lilypad's vision includes creating a new data economy by leveraging idle processing power and providing an accessible, permissionless compute network with a global marketplace for GPUs.
• 🌱 The project is designed to be highly native to running nodes, incentivizing users to contribute their spare GPU resources and be rewarded for their participation.
• 🚀 Lilypad has recently released version 2, with plans to expand to multi-chain compatibility and an incentivized testnet, indicating active development and a roadmap for future growth.
• 💼 Alie emphasizes the practical and ethical challenges of centralized AI, such as ownership, access, and the potential biases introduced by relying on models from a few dominant companies.
• 🌐 The project aims to democratize AI by providing open-source models, which are said to be faster, more customizable, private, and capable, thus fostering innovation and reducing biases.
• 💼 Lilypad's compute network is positioned as an alternative to centralized cloud providers, offering a more distributed and efficient model that could be more accessible and better for pricing and supply-demand dynamics.

Q & A

What is Lilypad and what does it aim to do?

Lilypad is a project being incubated at Protocol Labs, aiming to create a verifiable, trustless, decentralized compute platform. It focuses on enabling mainstream web 3 applications to utilize internet-scale data processing without relying on centralized services like AWS.

Why is verifiable compute considered a core need for the modern internet?

Verifiable compute is one of the three components of the modern internet, but it has been a missing piece in the decentralized internet story. It's now seen as essential for bringing the next billion users to web 3 and for allowing users to do more with their data in a decentralized manner.

What are the challenges in building decentralized and verifiable compute?

The challenges include the complexity of building such a system and the fact that the research is still cutting-edge and catching up. Additionally, the crypto space has been focused on solving more pressing problems like account abstraction.

How does Lilypad plan to make decentralized compute accessible and affordable?

Lilypad aims to create a global, trustless, permissionless compute network with an independent global marketplace of GPUs. This marketplace is designed to eliminate middlemen, keep prices low, and make the service accessible to everyone on the same terms.

What is the significance of being able to run AI and ML models on a decentralized network like Lilypad?

Running AI and ML models on Lilypad allows for ownership of AI models, access to models, and the ability to train models with security and speed. It also supports innovation through open-source projects and addresses practical and ethical challenges arising with mainstream AI adoption.

How does Lilypad address the issue of AI model ownership and access?

Lilypad promotes decentralized AI through distributed networks, which helps prevent central entities from controlling access to AI advancements. It also encourages open-source models that are more customizable, private, and capable.

What are some practical use cases for Lilypad's decentralized compute network?

Lilypad can provide cheaper, more open, better distributed, and more efficient compute power. It can also help with security, data ownership, and provide a stake in the ownership of AI solutions.

What is the 'water lily' project mentioned in the script?

The 'water lily' project is a proof of concept that uses Lilypad to train an AI model on artist input data. Users can then pay a small amount to get images generated by the trained model, demonstrating the practical application of Lilypad's compute capabilities.

What is Lilypad's roadmap for the future?

Lilypad has recently released version 2 with updated models and plans to go multichain, being EVM compatible. They also intend to release an incentivized testnet and aim for a mainnet launch in the following year.

How are participants in Lilypad's network incentivized?

On the supply side, node operators are compensated with LP tokens. The network tracks all participants for future launches, implying that there will be incentives for their contributions.

Is Lilypad's compute network limited to GPUs, or does it also include CPUs?

While Lilypad primarily focuses on GPUs due to the AI and ML focus, it does imply the potential for a network that could include various types of hardware, including CPUs.