"Fetch.ai Unveils ASI-1 Mini: Revolutionizing Web3 AI Access"

Fetch.ai Inc., a pioneering member of the Artificial Superintelligence (ASI) Alliance, has launched ASI-1 Mini, the first Web3-native large language model (LLM) designed to promote autonomous agent workflow. The model, powered by the FET token through ASI wallet integration, marks the beginning of the ASI: initiative, aiming to democratize access to foundational artificial intelligence (AI) technologies and allow users to invest in, train, and ultimately own their own models.

The AI model is immediately accessible to FET holders as part of a tiered freemium model. ASI-1 Mini features four dynamic reasoning modes — Multi-Step, Complete, Optimized, and Short Reasoning — promising advanced adaptive reasoning and context-aware decision-making. According to Humayun Sheikh, CEO of Fetch.ai and chairman of the ASI Alliance, ASI-1 Mini will lay the foundation for a new decentralized ecosystem, with the model dynamically selecting specialized AI models optimized for specific tasks.

ASI-1 Mini is designed to deliver enterprise-grade AI performance while operating on just two graphical processing units (GPUs), offering greater hardware efficiency, lower infrastructure costs, and increased scalability. On the Massive Multitask Language Understanding benchmark, the model was able to match or outperform leading AI models in domains such as medical sciences, history, and business analytics. Soon, the model will be expanded with an extended context window, allowing it to process larger amounts of information.

In addition to tackling performance issues, ASI-1 Mini will also help address the black-box problem in AI. Unlike traditional models, ASI-1 relies on multi-step reasoning, allowing for real-time self-correction and improved decision-making transparency. This is crucial in industries such as healthcare, where precision and clarity are of utmost importance.

ASI-1 Mini plays a central role in the ASI: initiative, set to empower the Web3 community and encourage end-users to participate in AI development directly. Through a decentralized compute network, users can stake, train, and own their own AI models, allowing the financial rewards of AI advancements to be distributed more fairly. ASI-1 Mini promises real-time execution, autonomous workflows, scalable deployment with minimal computational overhead, and enhanced knowledge representation. Users will soon be able to deploy AI agents capable of executing real-world