NCA-GENM Question 41
Select 3A team is tasked with designing a multimodal AI model for a smart city application that integrates video, audio, and text data to monitor traffic conditions in real-time. As part of the project, the team wants to ensure the model is energy-efficient and trustworthy. Which of the following approaches should they prioritize?
- A
Use model quantization techniques to reduce computational requirements.
- B
Incorporate explainability methods to provide insights into the model’s decisions.
- C
Rely solely on large-scale, pre-trained models without fine-tuning.
- D
Optimize the model for hardware acceleration on GPUs.
- E
Avoid implementing data privacy measures to prioritize faster processing.
Show answer and explanation
Correct answers: A, B, D
Explanation
Designing energy-efficient and trustworthy multimodal AI models requires a combination of techniques. Model quantization and GPU optimization improve energy efficiency, while explainability methods enhance trust by providing transparency. Avoiding privacy measures or relying solely on pre-trained models without adaptation fails to align with the goals of energy efficiency and trustworthiness.
- A. Correct.
Model quantization reduces the precision of calculations, which can significantly lower computational resource usage and improve energy efficiency without major sacrifices in performance.
- B. Correct.
Explainability methods, such as SHAP or LIME, are critical for building trust in AI models by allowing users to understand the rationale behind predictions.
- C. Incorrect.
Relying solely on large-scale, pre-trained models without fine-tuning can lead to inefficiency and lack of domain-specific optimization, which is counterproductive for both energy efficiency and trustworthiness.
- D. Correct.
Hardware acceleration on GPUs allows the model to perform computations more efficiently, leveraging specialized hardware capabilities to reduce energy consumption.
- E. Incorrect.
Avoiding data privacy measures undermines trustworthiness, as protecting user data is a crucial component of building reliable AI systems.