AI machine learning process for Conversational AI systems

The following information about the AI machine learning process for conversational AI systems is from ChatGPT.

Deep Learning Pre-training Process for Conversational AI systems

Data preparation: Cleaning and preprocessing the raw text data, including tokenization and data formatting.

Model architecture selection: Choosing an appropriate deep learning architecture, such as transformers.

Training: Utilizing the pre-training algorithm to train the model on the unlabeled text data.

Optimization: Tuning hyperparameters and employing techniques like masked language modeling (MLM) or other unsupervised objectives to improve the model's language understanding.

Saving the pre-trained model: Storing the learned model parameters for future use.

Deep Learning Fine-tuning Process for Conversational AI systems

Data preparation: Cleaning and preprocessing the annotated training data, including tokenization and data formatting.

Model setup: Initializing the pre-trained model with the appropriate architecture and loading the pre-trained weights.

Task-specific training: Fine-tuning the model on the annotated training data using supervised learning techniques.

Optimization: Updating the model's parameters to minimize the chosen objective function (e.g., cross-entropy loss).

Saving the fine-tuned model: Storing the updated model parameters for deployment.

Reinforcement Learning Process for Conversational AI systems

Environment setup: Defining the reinforcement learning environment with the necessary state, action, and reward structures.

Model initialization: Loading the fine-tuned model as the initial agent policy.

Training iterations: Interacting with the environment, taking actions based on the current model, and receiving rewards.

Policy update: Employing the reinforcement learning algorithm to update the model's policy parameters based on the received rewards.

Iteratively improving the policy: Repeating the training iterations and policy updates to enhance the model's conversational performance.

Will the benefits and risks arise from our interactions with Conversational AI black box systems ?

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