At Qognitive, we have developed novel analytical AI algorithms based on the QCML architecture, whose foundational differentiators enable our platform, Hone, to offer significant advantages over traditional approaches.
Classical AI/ML models are statistical and attempt to learn the joint distribution of the dataset. As a result, resource requirements grow exponentially with the number of features. On the other hand, QCML's requirement grows only linearly.
QCML handles datasets contaminated with irrelevant features, redundant information, and misleading correlations without degradation in performance. It automatically identifies and prioritizes the most predictive features while suppressing noise.
Unlike statistics based methods, QCML is able to maintain its accuracy even as the feature set grows and the number of data points does not. QCML provides 10-40% improvement in accuracy over industry standard models.
Seamlessly processes mixed structured data with multiple data types, without requiring human guidance or specialized pre-processing for each data type. The ability to handle multi-modal data without intervention is key for many applications.
A direct consequence of QCML's efficiency is scalability. Exponential increase in resource requirements hampers classical methods, while QCML can handle very large datasets with feature sets extending to the hundreds of thousands to millions.
Real life data is inherently messy, and QCML maintains its performance even when the datasets have gaps and errors, eliminating the need for extensive data cleaning and imputation required by traditional ML. Maintaining performance even under extreme conditions.
Short introduction to the foundational differentiators of QCML
Lecture on the intuition behind QCML