NCA-GENM Question 40
Select 4A team is tasked with designing and deploying a multimodal AI model for a smart city application that processes video, audio, and sensor data. The goal is to ensure the model is energy-efficient and trustworthy. Which of the following approaches should the team prioritize during development?
- A
Use model pruning and quantization techniques to reduce the computational demands of the model.
- B
Implement explainability tools to provide insights into the model's decision-making process.
- C
Avoid pre-trained models and develop all components from scratch to ensure full control over the architecture.
- D
Optimize the model for multi-GPU processing to speed up training while reducing energy consumption.
- E
Incorporate federated learning to ensure data privacy and security during training.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
To design and deploy energy-efficient and trustworthy multimodal AI models, teams should focus on techniques like model pruning and quantization to reduce energy consumption, implement explainability tools to ensure transparency, use hardware optimizations like multi-GPU processing, and adopt methods like federated learning to enhance data privacy and security. Avoiding pre-trained models is inefficient and unnecessary, as they often provide a solid and optimized foundation for new applications.
- A. Correct.
Model pruning and quantization are essential techniques for reducing the size and computational requirements of AI models, making them more energy-efficient.
- B. Correct.
Explainability tools enhance trustworthiness by allowing users and stakeholders to understand how the model operates and makes decisions.
- C. Incorrect.
Avoiding pre-trained models is counterproductive as they often provide energy-efficient and well-optimized starting points for multimodal AI, saving time and resources.
- D. Correct.
Optimizing for multi-GPU processing is a valid strategy to improve training efficiency and reduce energy use by leveraging hardware capabilities effectively.
- E. Correct.
Federated learning is an important approach for ensuring data privacy and security, which are critical components of building trustworthy AI models.