Modification
After testing, AI modification involves refining algorithms, improving data quality, and adjusting model parameters to enhance accuracy and performance. Developers analyse test results, identify errors or biases, and make necessary updates to optimise the AI system. This iterative process ensures the AI remains reliable, efficient, and aligned with ethical standards before deployment.
Steps for Modifying an AI Model:
1. Identify the Issues
Carefully review the model’s performance and the errors it made.
For example, if the model misclassified objects, check the specific cases where it went wrong. Understanding the mistakes will help you know what to improve.
2. Adjust Hyperparameters
The model’s performance can often be improved by adjusting its hyperparameters like the,
- learning rate
- batch size
- number of epochs
You can experiment with different values to see which ones give better results.
3. Use More or Different Data
If the model struggles with certain patterns or categories, consider adding more data.
For instance, if the model has trouble recognizing certain objects, try including more images or examples of those objects in your training data.
You can also improve your dataset by cleaning it or correcting any mislabelled data.
4. Fine-Tune the Training Process
You can also fine-tune the model by changing how it is trained.
For example, you can try using techniques like data augmentation (creating variations of your data through transformations like rotations or flips).
5. Re-test the Model
After making modifications, you should re-test the model to check if the changes improved its performance.
Use the same testing process as before to ensure the model works well with new data.
6. Iterate and Repeat
Modifying an AI model is an iterative process.
If the model still doesn’t perform well, continue to make adjustments and test again.
Over time, with the right modifications, the model should improve and perform more accurately.