NCA-GENM Question 39
Select 3A data science team is developing a multimodal AI model that integrates text, image, and audio data for a customer service application. The team has been tasked with ensuring the model is energy-efficient and trustworthy. Which of the following actions should the team take to meet these goals?
- A
Leverage model quantization techniques to reduce computational requirements.
- B
Include synthetic data for testing to improve the model's generalizability and trustworthiness.
- C
Regularly evaluate the model's performance with a bias detection framework.
- D
Train the model on the largest dataset available without considering data quality.
- E
Implement sparsity techniques to reduce the number of active parameters in the model.
Show answer and explanation
Correct answers: A, C, E
Explanation
To create an energy-efficient and trustworthy multimodal AI model, the team should focus on techniques like model quantization and sparsity to improve computational efficiency. At the same time, evaluating the model's performance with a bias detection framework ensures that it operates fairly and reliably, which is critical for trustworthiness. Using unfiltered large datasets or poorly validated synthetic data may compromise these goals.
- A. Correct.
Quantization reduces the precision of model weights and activations, leading to energy-efficient computations without significant loss in accuracy, which aligns with energy-efficient goals.
- B. Incorrect.
Synthetic data can help improve generalizability in specific cases, but it may not directly address trustworthiness unless it is carefully validated for fairness and quality.
- C. Correct.
Regular bias detection ensures that the model is fair and trustworthy, addressing potential ethical concerns in real-world deployments.
- D. Incorrect.
Using the largest dataset without evaluating its quality may lead to inefficiencies and introduce biases, which could compromise both energy efficiency and trustworthiness.
- E. Correct.
Sparsity techniques reduce the number of active parameters in the model, which can directly lower energy consumption without significant loss in performance.