NCA-GENM Question 43
Select 4A team is developing a multimodal AI model for real-time image and text analysis in a power-constrained environment, such as edge devices. Which of the following actions would help ensure the model is both energy-efficient and trustworthy?
- A
Implement model quantization techniques to reduce computational overhead.
- B
Use a larger and deeper neural network to maximize accuracy.
- C
Incorporate explainability methods to provide insights into the model's decision-making process.
- D
Deploy the model without additional testing to accelerate time-to-market.
- E
Utilize pre-trained foundation models and fine-tune them for the specific task.
- F
Regularly monitor and test the deployed model for bias and performance issues.
Show answer and explanation
Correct answers: A, C, E, F
Explanation
Developing energy-efficient and trustworthy multimodal AI models requires techniques like model quantization to reduce energy consumption while maintaining performance. Trustworthiness is ensured by incorporating explainability methods, using pre-trained models to optimize resources, and continuously monitoring the model for bias or performance issues. Avoiding unnecessary complexity and ensuring thorough testing are critical for achieving these goals.
- A. Correct.
Implementing model quantization reduces the model's computational requirements, making it more energy-efficient without significantly compromising performance.
- B. Incorrect.
Using a larger and deeper neural network increases accuracy but comes at the cost of higher energy consumption, making it unsuitable for power-constrained environments.
- C. Correct.
Incorporating explainability helps ensure the model is trustworthy by providing transparency into its decision-making process.
- D. Incorrect.
Skipping additional testing increases the risk of deploying an untrustworthy model that may exhibit bias or performance issues in production.
- E. Correct.
Utilizing pre-trained foundation models reduces the need for resource-intensive training from scratch, improving energy efficiency while maintaining performance.
- F. Correct.
Regular monitoring and testing ensure the model maintains fairness, accuracy, and energy efficiency over time, addressing any issues that arise in deployment.