SingularityNET and OriginTrail: Advancing Decentralized Knowledge Graphs

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By Greg Kuebler May 30, 2024 | 4 min read

Ljubljana, Slovenia, May 30, 2024 (GLOBE NEWSWIRE) — An innovative collaboration has emerged in the AI sector, as SingularityNET, a leading AI platform developer headquartered in Zug, Switzerland, and Trace Labs, the core development company of OriginTrail, based in Hong Kong, have just announced a strategic partnership aimed at supporting the development of the Knowledge Layer – the Internet of Knowledge.

This partnership signifies that two prominent players in the AI industry have come together to support a decentralized ecosystem where AI agents and infrastructure partners collaborate within the decentralized knowledge graph (DKG) landscape.
OriginTrail is an ecosystem dedicated to building a Verifiable Internet for AI, and this partnership marks the beginning of their collaboration with SingularityNET’s leading AI platform and robust ecosystem.

In some of the key highlights of this partnership, Trace Labs is tasked with developing sophisticated infrastructure that allows for efficient access and retrieval of information stored on the DKG, tackling challenges of AI hallucinations, bias, and model collapse due to an explosion in the amount of synthetic data produced by AI. This effort is aimed at enhancing the functionality and responsiveness of the decentralized knowledge graph within the OriginTrail network.

SingularityNET will then provide users access to its decentralized platform, where specialized AI models and Large Language Models (LLMs) can be purchased and used for data analysis. These models are designed to operate seamlessly with the data supported by the OriginTrail network, fostering a more robust ecosystem. The company will also develop AI models that can be trained directly on the Decentralized Knowledge Graph. This approach helps realize the shared vision of the two partners; eliminating the need for data centralization, and leveraging the decentralized nature of the blockchain to enhance privacy and security.

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