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    You are at:Home » Fetch.ai unveils first web3 LLM for agentic AI
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    Fetch.ai unveils first web3 LLM for agentic AI

    James WilsonBy James WilsonFebruary 25, 2025No Comments2 Mins Read
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    Fetch.ai says ASI-1 Mini will open up artificial intelligence and web3-native large language model architecture to the community.

    According to the Delaware-based artificial intelligence company, which is a founding member of the Artificial Superintelligence Alliance, ASI-1 Mini offers users the opportunity to build and optimize agentic workflows.

    The Artificial Superintelligence Alliance (FET) token will power this web3 LLM ecosystem, with ASI-1 Mini also leveraging ASI wallet integration.

    As part of its mission to improve artificial intelligence, blockchain, and cryptocurrency integration, ASI-1 Mini democratizes both access to artificial intelligence models and opportunities in investing, training, and decentralized ownership.

    In recent months, the broader industry has seen significant growth at the intersection of artificial intelligence and cryptocurrency. One area driving this expansion is the surging interest in agentic artificial intelligence.

    “ASI-1 Mini is just the start,” said Humayun Sheikh, chief executive officer of Fetch.ai and chairman of the ASI Alliance. “Over the coming days, we will be rolling out advanced agentic tool-calling, expanded multi-modal capabilities, and deeper Web3 integrations. With these enhancements, ASI-1 Mini will drive agentic automation while ensuring that AI’s value creation remains in the hands of those who fuel its growth,” he added.

    ASI-1’s unveiling introduces capabilities such as real-time execution and adaptability in agentic workflows. The feature allowing for scalable deployment on smaller hardware reduces computational overhead, while transparent outputs help address the black-box problem.

    By black-box problem, Fetch.ai refers to cases where an artificial intelligence system generates outputs without explaining how it reached a conclusion. For example, a healthcare artificial intelligence model might outline the risks associated with an ailment but fail to explain how it arrived at that assessment.

    According to Fetch.ai, ASI-1’s design helps address the black-box problem through a multi-step reasoning feature that enables real-time corrections. While opacity remains an industry challenge, the platform enhances transparency, intelligent collaboration, and clearer insights.



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