Learn which AI workload is ideal for building an automated recycling machine that identifies cans based on shape. Prepare for the Microsoft AI-900 Azure AI Fundamentals certification exam.
Table of Contents
Question
Your company wants to build a recycling machine for cans. The recycling machine must automatically identify cans of the correct shape and reject all other items. Which type of Al workload should the company use?
A. Anomaly detection
B. Computer vision
C. Natural language processing
D. Conversational Al
Answer
B. Computer vision
Explanation
To build a recycling machine that automatically identifies cans of the correct shape and rejects other items, the best type of AI workload to use is computer vision.
Computer vision is a field of artificial intelligence that focuses on enabling computers to interpret and understand visual information from the world, such as images and videos. It includes techniques for acquiring, processing, analyzing and understanding digital images to extract meaningful information and make decisions based on that data.
For the recycling machine, computer vision algorithms can be trained on a dataset of images showing various can shapes as well as non-can objects. The model learns the visual features and patterns that distinguish cans from other items.
Once trained, the computer vision model can analyze the visual input from cameras on the recycling machine in real-time. It can classify each object as either a can or not a can based on its shape, allowing the machine to automatically sort the cans from other recyclables or trash.
The other AI workloads mentioned are not as suitable for this use case:
- Anomaly detection identifies rare items or events that differ significantly from the norm. It’s not designed for classifying common objects like cans.
- Natural language processing focuses on understanding human language, which is not relevant for visually identifying cans.
- Conversational AI enables machines to communicate with humans using natural language, which is also not applicable for this recycling machine scenario.
In summary, computer vision provides the ideal tools and techniques to build an AI system that can visually recognize and classify cans based on their shape, making it the correct choice for the automated recycling machine.
Computer vision is the appropriate AI workload for this scenario because it involves analyzing and identifying visual elements (such as the shape of cans) to differentiate between correct and incorrect items.
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