CT-AI · Question #77
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed…
The correct answer is A. The difficulty of defining criteria for improvement before the model can be accepted. Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance…
Question
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real-world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing. Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
Options
- AThe difficulty of defining criteria for improvement before the model can be accepted.
- BThe fast pace of change did not allow sufficient time for testing.
- CThe unknown nature and insufficient specification of the operating environment might have
- DThere was an algorithmic bias in the Al system.
How the community answered
(21 responses)- A86% (18)
- B5% (1)
- D10% (2)
Explanation
Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
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