Exploring the Future of Secure AI in Web3: Insights from Sight’s Twitter Space with Crust Network

In a recent Twitter Space hosted by Crust Network, we had the privilege of diving deep into the future of secure AI in the Web3 ecosystem with Brian, a representative from Sigh. The discussion was insightful, shedding light on how Sight’s groundbreaking technology is set to revolutionize the way we think about data privacy and security in the decentralized world.

Sight’s Mission and Vision

We kicked off the session by exploring Sight’s core mission and the vision driving their innovations. Brian shared that Sight is primarily focused on addressing the critical issue of security in the blockchain space. With everything on the blockchain being public, there’s a growing concern around surveillance and theft, which Sight aims to solve by leveraging Fully Homomorphic Encryption (FHE). This advanced encryption technique ensures that privacy is maintained at all times, allowing computations on encrypted data without ever revealing the underlying information.

When asked about Sight’s role in the evolving Web3 landscape, Brian explained that Sight is building an FHE computing layer that can be integrated with applications across various blockchains, including Ethereum, Solana, Cosmos, and even Web2 services. This decentralized computing network enables secure data processing, where the data remains encrypted throughout the entire computing process, never being decrypted.

Introduction to Fully Homomorphic Encryption (FHE)

The conversation then moved into the technical aspects of Sight’s core technology — Fully Homomorphic Encryption (FHE). Brian provided a clear explanation for those in the community who might not be familiar with this concept. FHE is a form of encryption that allows computations to be performed on encrypted data without the need to decrypt it first. This means that even when data is being processed, it remains fully secure and private.

Brian highlighted that while the concept of FHE was proposed as early as the 1970s, it wasn’t until 2009 that a major breakthrough was achieved by Craig Gentry, making arbitrary computations on encrypted data possible. However, despite this breakthrough, FHE remains a significant engineering challenge. One of the primary challenges lies in managing the “noise” that accumulates during computations, which can affect the accuracy of results. Sight addresses this challenge with advanced techniques like bootstrapping, which helps manage noise and enables infinite computations on encrypted data.

This technology is particularly important for secure AI computations in the decentralized world. Brian explained how Sight’s use of FHE ensures that private data is never revealed during the computing process, making it a powerful tool for maintaining privacy in AI applications.

The Collaboration Between Sight and Crust Network

The collaboration between Sight and Crust Network was a focal point of the discussion. When asked about how this partnership came about and what the initial goals were, Brian shared that the collaboration is centered around applying FHE to decentralized storage. This partnership aims to create a robust solution for keeping personal data secure while still allowing for complex analysis and computations.

Brian provided an illustrative example: imagine having personal financial data stored on a decentralized network. With Sight’s technology, a bank could analyze this data for fraud detection or financial planning without ever accessing the actual unencrypted data. This ensures that while valuable insights are generated, the individual’s financial information remains completely private.

The benefits of Crust’s decentralized storage to Sight’s operations are significant. Brian highlighted that Crust’s solution enhances data redundancy and resilience by distributing data across a global network of nodes. This not only protects data from being lost or compromised but also improves security by dispersing it across multiple locations, making unauthorized access more difficult. Additionally, this decentralized approach reduces latency and speeds up data access, which is crucial for efficient AI processing.

Real-World Use Cases

The discussion then turned to the practical applications of Sight’s technology. Brian provided several compelling examples of how Sight’s FHE-powered solutions are being used across various industries. He emphasized that decentralized technology has the potential for wide adoption, and Sight is already exploring numerous use cases with its community and partners.

One particularly interesting application is in the field of decentralized finance (DeFi), where Sight’s technology can be used to create private and dark pools that obscure transaction details, providing enhanced privacy for users. Another exciting use case is in gaming, specifically in fully on-chain games like poker or real-time strategy (RTS) games, where FHE can be used to secure incomplete information, ensuring that players’ strategies and decisions remain confidential.

Brian also highlighted the potential for Sight’s technology in more traditional sectors, such as healthcare and finance. For example, in the healthcare industry, FHE can be used for genomics analysis, allowing researchers to analyze sensitive genetic data without compromising patient privacy. In the financial sector, insurance companies could leverage FHE to analyze user data securely, offering tailored services without ever accessing unencrypted personal information.

Looking forward, Brian expressed excitement about the intersection of FHE and large language models (LLMs). He believes this combination represents the “end game” for secure and decentralized AI applications. With the ability to support decentralized versions of popular AI tools like ChatGPT, Siri, and code copilots, Sight is poised to play a pivotal role in the future of AI-driven services that generate massive amounts of data.

The Future of AI and Decentralization

As the discussion moved towards the future, Brian shared Sight’s vision for how AI will evolve within the decentralized web. He confidently stated that AI will be a major driver of mass adoption for decentralized technologies. Citing the rapid growth of AI tools like ChatGPT, which reached 200 million monthly active users in just two months, Brian highlighted the potential for AI to become the backbone of various on-chain applications.

Sight aims to be the first and only secure decentralized AI infrastructure, providing the foundation for a wide range of on-chain AI solutions. Brian also teased some exciting upcoming developments, including the launch of Sight’s first version testnet and a new game powered by FHE that they have developed in partnership with their community. These projects reflect Sight’s commitment to pushing the boundaries of what’s possible with AI and decentralized technology.

Broader Implications for Web3

As the conversation approached its conclusion, Brian discussed the broader implications of integrating AI with decentralized storage and how this fusion could influence the future of the Web3 ecosystem. He emphasized that the combination of AI and decentralized storage will make Web3 services more secure, efficient, and user-friendly, ultimately driving the next wave of innovation in the digital space.

One compelling example Brian provided was the potential for decentralized social media platforms. In such a platform, users’ posts and interactions could be stored across a global network, ensuring data security and privacy. With AI working on this decentralized data, the platform could offer personalized feeds, friend suggestions, and content moderation, all without exposing any private user data. This represents a significant shift from current centralized systems, where user data is often exploited for commercial gain.

Brian also touched on the broader impact of these technologies on data ownership and integrity. By leveraging decentralized storage, AI-generated content can have clear and immutable ownership records, ensuring that creators maintain control over their work. This could revolutionize industries like digital art, where smart contracts could automate royalties and usage rights, providing creators with fair compensation for their work.

Audience Q&A

The Twitter Space concluded with an engaging Q&A session, where Brian addressed several thought-provoking questions from the audience.

Question: How can AI benefit from decentralized storage in Web3, and what advantages does this combination offer for users compared to current centralized systems?

Answer: Brian explained that decentralized storage provides a more secure and resilient environment for the vast amounts of data required by AI applications. In centralized systems, large companies control and often exploit user data. Decentralized storage, like that provided by Crust Network, offers a way to democratize data ownership, ensuring that users have control over their information while still enabling AI to function effectively.

Question: As AI-generated content becomes more prevalent, how can decentralized storage solutions address concerns around data integrity, authenticity, and ownership?

Answer: Decentralized storage ensures that AI-generated content has clear ownership records encoded in smart contracts. This not only helps in maintaining data integrity and authenticity but also allows creators to automate royalties and usage rights. For instance, an AI-generated image stored on a decentralized network can include encoded ownership details, ensuring that the creator receives appropriate compensation whenever their work is used.

Question: How can edge AI applications benefit from decentralized storage, and what opportunities arise from integrating Crust Network with edge AI frameworks?

Answer: Brian highlighted that decentralized storage like Crust Network allows edge AI applications to perform computations directly on the stored data, reducing the need to transfer large datasets to a central server. This integration offers significant advantages, including reduced latency, increased data privacy, and improved performance for AI applications deployed at the network’s edge.

Question: What is one potential of AI in the development of Web3 platforms?

Answer: Brian expressed his excitement about the potential for a “Crypto Siri” or decentralized voice assistants. With FHE, these applications can operate without compromising data security and privacy, lowering the barrier for entry and making such AI-driven platforms accessible to a broader audience.

Conclusion

In closing, Crust Network is thrilled about the promising collaboration with Sight. We believe this partnership will pave the way for groundbreaking advancements in secure, decentralized AI applications, enhancing the overall Web3 ecosystem. A big thank you to Brian for sharing his valuable insights and shedding light on the innovative work Sightis doing. We encourage our community to stay connected by following our social media channels for more updates and to keep an eye out for future Twitter Spaces like this one. We’re just getting started, and there’s much more to come!