Sophia NLU Engine: Future Roadmap

Three upgrades are planned, with the first happening immediately after market validation:

1. POS Tagger Accuracy -- Initial focus is to train a few quick custom machine learning models to enhance accuracy. Although current POS tagger is accurate, there's no reason it shouldn't be 100% accurate, matching the clarity of human communication. There is no ambiguity when speaking English, hence the POS tagger should reflect this. Exactly how to accomplish this is already mapped out. Additionally, two more models will be generated: one for better phrase classification and splitting, and another for resolving noun stems. Both are considered non-issues.
2. Multi-Language Translation -- With the back-end architecture already developed to support multiple languages, adding romantic languages such as French, Spanish, and Italian will be straightforward. Tonal Asian and RTL languages will require some modifications, but these are easily attainable.
3. Contextual Awareness -- Depending on user feedback, we may develop an additional custom model to significantly enhance contextual awareness in our NLU results.