Fine Tuning

Fine-tuning is key to the optimization of an AI model's functionality.

From the collection of data to the refinement of it to the processing and production, fine-tuning is an iterative process that selectively extrapolates portions of an AI’s operation and effectively re-trains it to increase the fidelity of its outputs.

Typically implemented through a supervised learning approach where human intervention is required in order to pinpoint discrepancies and guide the AI to understand desired outputs.

As it relates to MEDUSA, fine-tuning is conducted on a regular basis by the development team that is monitoring the qualitative state of MEDUSA. Additionally, fine-tuning may be implemented ad hoc in sudden critical, logical lapses.