At RSS3, we want to foster AI innovation that’s accessible to everyone whether you are a consumer, model trainer, data contributor, or node operator. Our goal is to ensure that everyone can benefit from the advancements and opportunities presented by AI and the Open Web.
RSS3 AI employs an MoE architecture to make blockchain AI models statistically more reliable and executable across any network.
Traditionally, large language models (LLMs) are prone to hallucinations, meaning the outputs seem plausible on the surface but are actually incorrect, misleading, or even entirely fabricated. Many factors are contributing to this:
- LLMs are over-generalized and thus lack the ability in specific areas.
- LLMs lack access to real-time data and thus attempt to generate outputs that might sound correct but are baseless.
- LLMs do not learn from previous mistakes in real time.
RSS3 AI's MoE architecture alleviates this by dividing a complex task into several more specific tasks. Each task is then handled by a specialized Expert AI model (the right tool for this job), leveraging the classic divide-and-conquer strategy to boost reliability.
LLMs' ability is largely bounded by their training data. When this limitation is combined with the lack of access to real-time data, the risk of intent misinterpretation is increased. As a consequence, automated intent execution becomes unreliable, if not impossible.
Backed by the comprehensive coverage of the RSS3 Network, RSS3 AI trains multiple specialized Expert AI models to offer unmatched intent executability.
RSS3 AI enables ChatGPT to access real-time data
Given the complex nature of the AI API, access to it is currently provided on a per-application basis. This approach allows us to cater to the unique requirements associated with each application's use of the API, and collect feedback more efficiently for improvements.
We kindly request you reach out to us via [email protected] to discuss your unique requirements.
Updated about 2 months ago