Jaxon's DSAIL (Domain-Specific AI Language) drastically reduces AI hallucinations, enhancing project success rates and accelerating the development of trusted AI applications.

Jaxon is a multi-approach training platform that amplifies a small number of human-provided labels into full-scale training datasets with fine- tuned high-quality models for text-oriented machine learning applications.

Jaxon AI Machine learning models are guided views of human-labeled data that are fine-tuned to adjust to confidence via strategic measurements and estimation techniques. This specialized use of AI modeling can be thought of as an adjustment to human so that each possible choice of label for a given document is orchestrated to be a correct one. This AI learning system re-establishes useful models when parts of a larger system are changed. It realigns annotation schemes to correlate results skewed by changes, and repairs to confidence – fine tunes - results modified from the first design. This AI machine learning system minimizes drift from human contributors during the phases of testing and experimentation and uses techniques to correct systemic biases in the human-provided labels. This guided learning system uses machine learning models that address concrete business problems with a consistent, standardized approach to model training. These specialized techniques bring effective models, tuned for optimal performance metrics in production, minimized labeling expense and mitigated opportunity costs.

Jaxon includes IBM's Starcoder LLM model.

Take on AI hallucinations with Domain-Specific AI Language built with watsonx. 

DSAIL systematizes the procedure of crafting intricate AI systems.

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