Asimov Protocol unveils blockchain-based solution

For verifiable AI data

Asimov Protocol, a cutting-edge decentralised solution, is set to galvanise the artificial intelligence (AI) sector with its innovative blockchain-based approach to data verification.

Officially emerging from its stealth phase, the protocol introduces a new era of trust, transparency, and accountability in AI systems by ensuring that the data they use is verifiable and auditable.

In a world of rampant misinformation, the Asimov Protocol is pioneering a solution that leverages the ‘Verifiable Internet’ thesis. It employs blockchain technology to create a robust framework for verifying and tracking data and AI models. This ensures that AI systems can operate on secure, reliable, and credible information, significantly improving the trustworthiness and ethical output of AI-driven results.

The primary goal of the Asimov Protocol is to resolve the persistent ‘garbage-in, garbage-out’ problem in AI, which occurs when flawed or unverified data feeds into AI models, leading to inaccurate outputs. By adopting a neuro-symbolic approach—integrating knowledge graphs and large language models (LLMs)—the protocol enhances AI’s capacity to work with accurate and traceable data, addressing key issues with training data quality.

Arto Bendiken, Co-Founder of Asimov Protocol, explained the importance of this breakthrough: “For over 20 years, I’ve been passionate about combining blockchain with structured data representation. Our system empowers developers to query and verify data with certainty, using high-quality, verifiable sources, ensuring that the data AI consumes is as trustworthy as the models themselves.”

The Asimov Protocol is built on the NEAR Protocol, ensuring scalability and security. It provides a system for processing, verifying, and monetising data via knowledge graphs, guaranteeing a verifiable chain of custody for AI models.

The protocol facilitates interaction with datasets through Asimov Software Developer Kits (SDKs (SDKs), allowing publishers, curators, consumers, and validators to collaborate effectively and maintain high data quality. This approach aligns with Aristotle’s principle of ‘justified belief,’ ensuring that only trustworthy, verified data is fed into AI systems.

The protocol’s architecture fosters collaboration within the ecosystem:

• Indexers ensure data is processed and verified to high standards.
• Validators confirm that data meets the protocol’s verification criteria.
• Publishers and curators are responsible for uploading and enhancing datasets, ensuring they meet top-quality and verifiability standards.
• Consumers query datasets for actionable insights, ensuring that only dependable data is used.

One of the standout features of the Asimov Protocol is its knowledge graph-based data verification mechanism, which enables powerful queries, such as GraphQL and SPARQL, for local inference. This capability allows users to query data anywhere within the ecosystem, driving further transparency and accuracy.

Bendiken explained: “The ability to transform data from any source into a knowledge graph for complex AI queries sets Asimov Protocol apart. Integrating with NEAR ensures scalability, data integrity and accuracy across the entire ecosystem.”

Currently operational on the NEAR testnet, the Asimov Protocol has already processed significant transactions. With the main net launch imminent, numerous organisations, ranging from AI startups to academic institutions, are eager to join the protocol. These partnerships are vital to its success and scalability and contribute to the broader AI community.

“Our goal is to create a consortium of trusted experts who understand the critical role reliable data plays in shaping the future of AI,” said Bendiken. “By leveraging knowledge graphs, we can combine high-quality data sources to create more accurate and reliable models, fuelling the next generation of AI innovation.”

Asimov Protocol is well-positioned to lead the AI revolution, offering clean, structured, and trustworthy data. As AI models become more prevalent, the protocol tackles the critical challenge of ensuring data accuracy and integrity. It provides a frictionless ecosystem where enterprises can confidently rely on AI-driven insights marked by the Asimov Protocol seal of approval, ensuring that outputs are accurate, auditable, and enterprise-ready.

Image: Arto Bendiken, Co-Founder of Asimov Protocol. Credit: Asimov Protocol