Human contribution in AI training is crucial for defining the problem, collecting and labelling the data, choosing and adjusting the model architecture, evaluating and fine-tuning the model, validating and corroborating the data, and ensuring that AI systems are developed and used ethically.
Gather a large and diverse dataset that is relevant to the problem you are trying to solve. Humans collect and label the data that is used to train the AI model. This involves selecting the data, ensuring it is relevant to the problem being solved, and manually annotating it with labels or annotations that are used as the ground truth for training.
Clean and organize the data to ensure it is suitable for training.
During training, the model is exposed to the data and makes predictions, which are compared to the actual outputs. Humans validate and rate the outputs to improve the training. The model's parameters are then updated to reduce the error between its predictions and the actual outputs.
Test the model on a separate, unseen dataset to evaluate its performance. Humans evaluate the performance of the AI model and fine-tune it to improve its accuracy.
Based on the evaluation results, adjust the model's hyperparameters and architecture to improve its performance.
Humans also play a critical role in ensuring that AI systems are developed and used ethically. This involves considering the potential impacts of AI systems on society and the environment, and ensuring that they are aligned with ethical principles such as fairness, transparency, and accountability.