BPT AI Governance

It designs AI's decision-making process to be used in the BPT Platform, which includes what data to collect and analyze, which algorithms and models to use, and which strategies to achieve certain goals.

Data collection and preprocessing methods: It defines how to collect and pre-process the data needed to train AI models. It includes collecting data from patent databases or various data sources on the internet and cleaning and processing the data.

3D image model selection and performance evaluation criteria: It selects a BPT AI model and sets the criteria to evaluate it, which includes determining the metrics and evaluation methods to evaluate the model's performance.

Model training and tuning: It manages the process of training and optimizing BPT AI models, including training models based on training data and adjusting hyper-parameters.

Ensure transparency and accountability: BPT AI Governance establishes policies and procedures to ensure transparency and accountability of AI models. This includes adopting explainable AI (XAI) techniques to understand how the model works and providing adequate explanation and transparency to users.