What is LNM ?

• Advanced computational frameworks designed to handle and process vast amounts of numerical data.
• Ability to integrate and analyze large datasets from multiple domains to generate precise and actionable insights.
• Pre-trained using SXI++ on extensive numerical data.

Characteristics of LNM:

• High Accuracy and Precision: LNM boasts greater than 95% accuracy and precision, minimizing errors and ensuring reliable predictions.
• No Hallucinations: Unlike some AI models, LNM does not produce hallucinations, which are incorrect or nonsensical outputs generated by the model.
• Pre-trained and Validated: LNMs are pre-trained on billions of numerical data points, making them robust and reliable without the need for additional spot training.
• Scalability and Distribution: LNM can follow a distributed architecture, with domain-wise segmentation for different use cases, enhancing processing speed and efficiency.
• Data Integration and Feature Analysis: LNMs integrate top features from individual models within a domain to train a master model, improving prediction accuracy.

Two Phases of Operation:

Past Data Exists: When past data is available, it is used to train new sub-models within the domain, and the new data's contribution is checked against the master model. This phase allows for continuous improvement and feature identification.

No Past Data: In cases where no past data exists, the system relies on common features identified from previously executed models within the same domain to guide new data capture.