Summary
Why Structure Matters
FSL addresses fundamental challenges across LLMs, data, humans & AI, and ecosystem.
Current Challenges
Logical inconsistencies and hallucinations
Current language models struggle with maintaining logical consistency and often generate unreliable outputs.
No real traceability of reasoning
The decision-making process within LLMs remains opaque, making it difficult to understand or verify their outputs.
Unstructured textual data
Most of the world's textual information lacks the structure needed for efficient processing and analysis.
Fragmentation across systems
Data is scattered across different systems and languages, creating barriers to integration and insight.
Limited human-AI cooperation
Current interactions between humans and AI lack clarity, leading to misunderstandings and reduced effectiveness.
No semantic space for sharing
There's no common ground where humans and AI can exchange meaning in a precise and unambiguous way.
Closed and costly approaches
Many AI systems depend on proprietary technologies that are expensive and difficult to maintain long-term.
No universal standard
Without a shared framework, integrating diverse knowledge sources and experiences remains challenging.
What FSL Brings
It directly encodes meaning — reducing errors and ambiguities, and making every reasoning step traceable.
FSL provides a direct representation of meaning that eliminates confusion and makes reasoning transparent.
It structures data in a universal, interlingual, and interoperable way.
FSL creates a common format that works across languages and systems, enabling seamless data integration.
It provides an open and durable foundation for connecting humans, AIs, and systems.
FSL offers a stable, open-source infrastructure that supports long-term collaboration between all parties.