NCA-GENM Question 151
Select 4A team is optimizing a multimodal AI model for energy efficiency, trustworthiness, and accuracy. They are deciding on the next steps in their workflow. Which of the following actions are most likely to improve the model's performance in line with these goals?
- A
Conduct hyperparameter tuning to fine-tune model performance.
- B
Increase the dataset size without considering data quality.
- C
Leverage transfer learning to use pre-trained models for specific tasks.
- D
Perform rigorous testing to identify edge cases and potential biases.
- E
Use computational advancements, such as mixed-precision training, to reduce energy consumption.
Show answer and explanation
Correct answers: A, C, D, E
Explanation
Optimizing a multimodal AI model for energy efficiency, trustworthiness, and accuracy requires a combination of strategies. Hyperparameter tuning, transfer learning, and computational advancements directly enhance performance and efficiency. Rigorous testing ensures the model is reliable and unbiased. Conversely, increasing dataset size arbitrarily can harm performance unless data quality is carefully managed.
- A. Correct.
Hyperparameter tuning is a critical technique for refining model performance and improving accuracy while balancing computational efficiency.
- B. Incorrect.
Increasing dataset size without considering data quality may introduce noise or irrelevant data, which can negatively impact model performance.
- C. Correct.
Transfer learning allows the use of pre-trained models, which can save computational resources, improve training speed, and enhance performance for specific tasks.
- D. Correct.
Rigorous testing is essential to identify and mitigate biases, improve trustworthiness, and ensure the model performs well across various scenarios.
- E. Correct.
Using computational advancements like mixed-precision training can significantly reduce energy consumption without sacrificing accuracy.