NCA-GENM Question 42
Select 3A team is tasked with developing a multimodal AI model for a healthcare application that processes both patient images and medical text. The team is focused on ensuring energy efficiency while maintaining trustworthy results. Which strategies should they adopt during the development and deployment of the model?
- A
Use model quantization techniques to reduce computational complexity without significantly impacting accuracy.
- B
Train the model using the largest dataset available without considering data quality or relevance.
- C
Implement explainable AI techniques to provide transparency in the model’s decision-making process.
- D
Deploy the model on energy-efficient hardware, such as NVIDIA GPUs optimized for AI workloads.
- E
Perform hyperparameter tuning to maximize the model's accuracy, disregarding its energy consumption.
Show answer and explanation
Correct answers: A, C, D
Explanation
To create energy-efficient and trustworthy multimodal AI models, developers must balance computational efficiency with transparency and effectiveness. Techniques like model quantization, explainable AI, and utilization of energy-efficient hardware contribute to this goal, especially in critical fields like healthcare.
- A. Correct.
Model quantization reduces the model size and computational requirements, leading to energy efficiency while preserving acceptable accuracy. This aligns with the goal of creating energy-efficient AI models.
- B. Incorrect.
Using the largest dataset available without considering its quality or relevance can lead to inefficiencies and potentially untrustworthy results, as the model may learn from noisy or irrelevant data.
- C. Correct.
Explainable AI techniques improve trustworthiness by making the model's decisions interpretable to users. This is essential in sensitive applications like healthcare.
- D. Correct.
Deploying the model on energy-efficient hardware, such as NVIDIA GPUs optimized for AI, ensures reduced energy consumption and high performance.
- E. Incorrect.
Maximizing accuracy without considering energy consumption directly contradicts the goal of building energy-efficient models.