NCA-GENM Question 149
Select 4A team is working on optimizing a multimodal AI model to improve its energy efficiency, trustworthiness, and accuracy. Which of the following approaches would directly contribute to achieving this goal?
- A
Applying hyperparameter tuning to adjust learning rates and batch sizes.
- B
Utilizing transfer learning to leverage pre-trained model weights.
- C
Increasing the model size to capture more complex relationships.
- D
Conducting rigorous testing to identify potential biases and errors.
- E
Reducing the number of training epochs to lower computational cost.
- F
Incorporating supervised training with high-quality labeled data.
Show answer and explanation
Correct answers: A, B, D, F
Explanation
Optimizing multimodal AI models for energy efficiency, trustworthiness, and accuracy involves techniques that enhance the model's performance while minimizing resource consumption. Approaches such as hyperparameter tuning, transfer learning, rigorous testing, and supervised training are crucial for achieving these objectives. Conversely, increasing model size and reducing training epochs can negatively impact energy efficiency and accuracy, respectively.
- A. Correct.
Hyperparameter tuning can optimize the model's training process, improving both energy efficiency and accuracy by finding the best configuration.
- B. Correct.
Transfer learning enables the reuse of pre-trained weights, reducing computational requirements and improving performance with less training effort.
- C. Incorrect.
Increasing the model size may capture complex relationships but typically requires more computational resources, making it less energy-efficient.
- D. Correct.
Rigorous testing helps ensure trustworthiness by uncovering biases and errors, which is essential for reliable multimodal AI applications.
- E. Incorrect.
Reducing the number of training epochs may save energy but risks underfitting, reducing the model's accuracy and trustworthiness.
- F. Correct.
Supervised training with high-quality labeled data enhances model accuracy and reliability, contributing to trustworthy AI.