How can you easily grasp the current market trends, technological developments, ecosystem progress, and governance dynamics in the Web3 industry? Web3Caff Research's "Market Pulse Analysis" column delves into the front lines to identify and select current hot topics, providing valuable interpretations, commentary, and fundamental analysis. See through the phenomena to the essence, and follow us to quickly capture the forefront of the Web3 market.
Author: Hendrix , Researcher at Web3Caff Research
Cover: Logo from this project, Typography by Web3Caff Research
Word count: 3100+ words in total
As large language models evolve from simple chatbots to intelligent agents capable of independent decision-making, an insurmountable technical bottleneck is hindering the large-scale deployment of AI: the inherent uncertainty of AI-generated content. Given the same input prompts, AI models cannot produce completely consistent outputs. This characteristic prevents large models from participating at scale in economically impactful decision-making processes. For example, in an AI shopping scenario, the intelligent agent attempts to understand the user's intention to purchase goods. If, after purchase, the user receives a product that does not meet their expectations, they need to resolve after-sales issues with the merchant. At this point, it's crucial to first determine what kind of purchase instruction the intelligent agent issued. If the output is uncertain, the intelligent agent may offer a different choice during dispute resolution, potentially causing financial losses for the user. To address this issue, EigenCloud recently launched the EigenAI platform. By building a complete technology stack from underlying hardware to consensus protocols, EigenCloud can provide users with verifiable and reproducible AI inference services under relatively secure and privacy-preserving conditions, laying the foundation for the deployment of AI intelligent agents in more serious fields.
To better control the model's output, EigenAI proposed an end-to-end deterministic inference strategy. This strategy rigorously controls and customizes each layer of the large language model's inference stack, transforming the original probabilistic inference into a precise deterministic function.
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