Knowledge Infusion for Robust Machine Learning Models

The patent applies preliminary action by pre-training a teacher model on large-scale data before deployment. This teacher model is then used to generate synthetic training data for student models, eliminating the need for students to undergo lengthy training from scratch. The knowledge gained from the teacher model's preliminary training is transferred to student models through generated training examples, significantly reducing their training time while maintaining accuracy.

The patent implements copying by using the teacher model to generate synthetic training data that replicates the knowledge and patterns learned from extensive training. Instead of requiring student models to relearn from raw data, the teacher model creates copied training examples that encapsulate essential knowledge, allowing student models to achieve comparable performance with minimal training time.

The patent applies universality by designing a multi-context training framework where student models are trained on synthetic data generated from multiple different contexts and scenarios. The teacher model generates training examples that encompass diverse situations, enabling student models to learn universal patterns that generalize across different contexts rather than overfitting to a single context, thus improving transferability.

The patent implements dynamics by making the training process adaptive to different contexts. The teacher model dynamically generates synthetic training data tailored to various contexts, and student models are trained on this diverse data distribution. This dynamic approach allows models to adapt to new contexts encountered during deployment without requiring retraining, enhancing transferability.

The patent applies the intermediary principle by introducing a teacher model as a mediator between the learning process and the real system. The teacher model, trained on extensive data, generates synthetic training environments and guidance for student reinforcement learning agents. This intermediary allows agents to learn through simulated interactions rather than direct real-world exploration, preventing harmful actions while maintaining learning effectiveness.

The patent implements preliminary action by having the teacher model pre-generate safe training environments and guidance policies before student agents begin exploration. This preliminary preparation ensures that when student agents do explore, they do so within safe boundaries defined by the teacher model, preventing harmful actions while still allowing sufficient exploration for effective learning.

The patent implements copying by using the teacher model to generate synthetic labeled training data that replicates the quality of manually annotated data. Instead of requiring human annotators to manually label training examples, the teacher model creates copied labeled data with consistent quality, eliminating labor costs while maintaining the data quality necessary for effective supervised learning.

The patent applies self-service by enabling the system to generate its own training data through the teacher model without external human intervention. The teacher model autonomously creates labeled training examples, allowing the system to self-generate the data it needs for training student models, thereby eliminating dependence on manual annotation labor.