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Parsimony
Our core technology is a tree language model. Trees are better than current language models because they mimic the human brain.
What is P7Y?
1
Word Disambiguation
Parsimony determines the precise meaning of words based on their context. For example, it resolves whether the word "lead" is meant as the verb (to lead) or the noun (a type of metal). This disambiguation process helps in understanding the speaker's intended meaning by analyzing the context in which the words are used.​
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2
Discrete Word Mapping
After disambiguating the words, Parsimony converts each word into discrete elements, unlike traditional models that use fuzzy vectors. This allows the system to treat words as distinct, individual units that can interact in predictable ways, similar to how Tesla's world model treats cars as distinct objects with predictable behaviors.
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3
Contextual Interpretation and Prediction
Once words are mapped as discrete units, Parsimony uses their context to predict how these words will behave in relation to one another, much like how Tesla predicts the movement of objects in its world model. This approach enhances the system’s ability to handle language by making more accurate and explainable predictions based on word relationships.
