MindSpeech Progression
MindSpeech 1.0
How the Model Works
Current Limitations
As of Q1 2025, the model has two fundamental limitations. As we scale the training data, we believe both limitations with be automatically addressed (without any fundamental changes to the current model architecture):
Limited Semantic Spaces
Since the model has had limited training data, we currently limit the semantic space to achieve high accuracy levels. As we scale training data, the model automatically becomes capable of serving broad spectrums of semantic spaces.
Context Window
Due to limited training data, the current version of the model works best when we provide a one-word context window that defines and identifies the semantic space. This is a self-imposed artificial limitation that helps us work around limited training data.
Ambition for 2027
Universal Coverage
In the long-term, with adequate training data, MindSpeech will achieve universal coverage across all semantic spaces.
Our current limitations are artificially created as a workaround to achieving high accuracy without adequate training data. As training data is scaled, we expect universal coverage.
Our current limitations are artificially created as a workaround to achieving high accuracy without adequate training data. As training data is scaled, we expect universal coverage.
99%+ Accuracy
With key limitations, the model already deliver 90%+ accuracy levels across all samples. In the near future, and likely before 2027, MindSpeech will achieve 99%+ accuracy.
Our accuracy levels will persist even after removing the model’s limitations, which are linked with training data.
Our accuracy levels will persist even after removing the model’s limitations, which are linked with training data.
