Fdaytalk Homework Help: Questions and Answers: An Accenture client wishes to use generative Al to enhance its business. The Client Account Lead reviews the client’s goals and recommends a boosting approach. What does boosting refer to in this case?
a) upgrading the client’s PCs to handle more complex Gen Al models.
b) building the client a fully customized Gen Al model from the ground up.
c) customizing an existing Gen Al model using the client’s own data.
d) purchasing a large, general-purpose Gen Al model without customization.
e) I don’t know this yet.
Answer:
This question is about a client wanting to use generative AI to enhance their business.
To solve this problem step by step, let’s understand “boosting” in relation to generative AI and the client’s goals:
Boosting: In machine learning, boosting refers to an ensemble technique where multiple weak models are combined to create a stronger model. However, in a broader business context, boosting can mean enhancing or improving existing capabilities.
Given Option: Step by step analysis
a) Upgrading the client’s PCs to handle more complex Gen AI models.
- This option is about improving the hardware capabilities but does not directly relate to the concept of boosting generative AI models.
b) Building the client a fully customized Gen AI model from the ground up.
- This option is about creating a new model from scratch, which is not typically referred to as “boosting.”
c) Customizing an existing Gen AI model using the client’s own data.
- This option aligns with the concept of boosting in the sense of enhancing or fine-tuning an existing model to better suit the client’s needs by using their specific data.
d) Purchasing a large, general-purpose Gen AI model without customization.
- This option involves buying an off-the-shelf model without any enhancements or customization, which does not fit the concept of boosting.
Final answer:
Based on the analysis, the correct answer is:
c) Customizing an existing Gen AI model using the client’s own data.
This approach allows the client to leverage a pre-existing generative AI model and “boost” its performance and relevance by fine-tuning it with their own data, making it more suitable for their specific business needs.
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