AI-900: Computer Vision for Plastic Bottle Recycling Machine

Learn why computer vision is the best type of Al workload for building a plastic bottle recycling machine.

Question

A company wants to build a recycling machine for plastic bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items. Which type of Al workload should the company use?

A. Natural language processing
B. Conversational Al
C. Anomaly detection
D. Computer vision

Answer

D. Computer vision

Explanation

The correct answer is D. Computer vision. Computer vision is a type of Al workload that enables machines to see, identify, and process images in the same way that human vision does, and then provide appropriate output. Computer vision can be used for various tasks such as face recognition, object detection, optical character recognition, and more.

In this scenario, the company wants to build a recycling machine for plastic bottles that can automatically identify bottles of the correct shape and reject all other items. This requires the machine to be able to analyze the images of the items and determine if they are bottles or not. Therefore, computer vision is the most suitable type of Al workload for this task.

Natural language processing (NLP) is a type of Al workload that enables machines to understand and generate natural language, such as speech and text. NLP can be used for various tasks such as sentiment analysis, machine translation, chatbots, and more. However, NLP is not relevant for this scenario, as the machine does not need to process any natural language input or output.

Conversational Al is a type of Al workload that enables machines to interact with humans using natural language, such as voice or text. Conversational Al can be used for various tasks such as virtual assistants, customer service, and more. However, conversational Al is not relevant for this scenario, as the machine does not need to have any dialogue with humans.

Anomaly detection is a type of Al workload that enables machines to identify unusual patterns or outliers in data, such as fraud, errors, or defects. Anomaly detection can be used for various tasks such as cybersecurity, quality control, and more. However, anomaly detection is not relevant for this scenario, as the machine does not need to detect any anomalies in data..

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