On August 7, Nillion, a blind computation network, revealed that it has teamed up with Ritual, a decentralized open artificial intelligence (AI) infrastructure network, to develop decentralized blind AI inference technology.

This technology promises to democratize AI access while ensuring complete user data privacy.

Bridging the AI Privacy Gap with Decentralized Solutions

The collaboration aims to facilitate secure and verifiable AI inference for various applications. Both projects acknowledge that as personalized AI becomes increasingly prevalent, access to sensitive information is crucial.

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Currently, many organizations hesitate to use AI with sensitive data due to privacy concerns. This partnership aims to bridge that gap, allowing enterprises to utilize AI capabilities while maintaining complete data privacy. According to Alex Page, CEO of Nillion, the partnership will integrate Nillion’s blind computation technology into Ritual’s interface.

“The integration will empower developers to maintain the confidentiality of both user input data and AI models,” Page explained to BeInCrypto.

This synergy will also unlock numerous possibilities for applications across blockchain and AI sectors. Promising use cases include enabling AI in healthcare and the Internet of Things (IoT), processing and exploring price predictions that safeguard proprietary models and user data.

“It will also improve intent classification in chatbot systems for secure actions based on classified intents, develop secure alternatives to current anonymization layers between users and generative AI endpoints, and facilitate vector-based information retrieval crucial for Retrieval-Augmented Generation systems,” the Nillion spokesperson elaborated.

This partnership aligns with a recent report by McKinsey, which stressed the importance of high-quality data sets for capturing value from AI. Data-centric AI use cases are diverse and widespread. They include detecting and preventing fraudulent activities in financial institutions, promoting transparency in AI-driven diagnoses in healthcare, and identifying potential biases in quality control systems for manufacturers.

However, the centralized AI model presents challenges and risks. Grayscale’s report highlights how network effects and high capital requirements in the AI sector are significant barriers for many developers outside large tech companies.

This makes it challenging for them to access the necessary resources. Consequently, it becomes harder for them to monetize their work, ultimately limiting competition and innovation in the AI industry.

Furthermore, as AI grows in influence and importance, many worry about the concentration of decision-making power in a few companies. Decentralized AI, which uses blockchain technology, offers a solution by distributing ownership and governance of AI. This increases transparency and accessibility.

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Moreover, blockchain technology can help increase developer access to AI, lowering independent developers’ barriers to building and monetizing their work. This could enhance overall AI innovation and competition, providing a balance with the models developed by tech giants.