Deep Evolution – Continuous Evolution of Deep Reflection

In today’s post, I will continue presenting the Deep Reflection concept, developed by us, focusing on its digital expressions and, this time, exploring continuous learning, which we call Deep Evolution.

Continuous learning within the Deep Reflection or Deep Evolution concept enables the SLM (Small Language Model) to constantly evolve, incorporating new digital expressions as they become available. This process ensures that the model remains up-to-date, relevant, and aligned with the individual’s thoughts, ideas, and knowledge over time.

The first step in this process is the collection of new digital expressions generated by the person, such as videos, texts, audios, and other media. These expressions are analyzed and processed to be integrated into the existing dataset, continuously and dynamically enriching the digital reflection.

Next, incremental learning is applied. This method allows new data to be added to the pre-existing model without requiring a full retraining from scratch. The fine-tuning process is performed incrementally, adjusting the model’s parameters to integrate the new information while preserving previously acquired knowledge.

Finally, the model can be updated periodically or on demand, depending on specific needs. This ensures that Deep Reflection remains a continuously evolving tool, keeping the digital reflection always adapted to new expressions and changes in the thoughts of the individual or the represented group.