Artificial intelligence (AI) is designed to automate manual tasks and processes. As the leader in computer vision solutions for the service industry, we at TechSee work with large organizations to help them utilize AI to automate service for their products. Since everyone begins a journey with the first step, we present a list of the core terms to help you understand how visual AI automation works. Welcome to our computer vision primer!
Computer vision is an AI that can automatically see and understand images. Creating a computer vision model generally involves collecting pictures and videos, labeling the photos and the different parts of the image, and using this data to train a computer to recognize an object, component, or status. Today’s no-code computer vision platforms like VI Studio use machine learning and deep learning algorithms to make training and deploying computer vision AI models easier and faster than ever. VI Studio uses a sampling of images labeled and tagged by humans to train computer vision AI models. These models can perform tasks such as image recognition, object detection, and segmentation.
Below is a brief explanation of some standard terms used in this space. We hope this information helps you understand and navigate this new and exciting technology.
OCR:
Optical Character Recognition (OCR) is a technology that allows computers to read and convert printed or handwritten text into machine-readable formats or, more simply, turn text in a picture into digital text. An example would be reading a mix of numbers and letters on the serial number of a specific electronic instrument or even a receipt for a sale. An OCR application can read this text so that a CRM or support database can automatically identify the relevant product.
OCR is a critical component of computer vision applications, such as document scanning, automated data entry, and ID recognition. In the visual AI platform, OCR models integrate pre-trained models. For example, an OCR model trained to identify and extract an IP address from a screenshot will automatically find and identify an IP address. In this example, the OCR will only remove the relevant text while disregarding all other text in the screenshot as noise.
Classification:
Classification is a machine-learning technique that categorizes data into groups or classes. In the context of computer vision, classification models are trained to recognize and label images into different categories through tagging. For example, a classification model could be trained to identify items such as routers, refrigerators, coffee machines, and even forms or applications. Users can train custom classification models in a visual AI platform by providing labeled image datasets or using pre-trained models already trained on specific categories.
In VI Studio, we use classification models to identify specific makes and models of objects visually. For example, tagging a sampling of photos as Linksys MX8503 Atlas WiFi 6E routers will allow AI to create a classification model that can be used to help customers identify which model router they have as part of a support flow.
Sub-component:
A sub-component is a smaller, modular unit that can be combined with other components to create more complex systems. In the context of a visual AI platform, sub-components are pre-built models or features that can be integrated into custom models to add additional functionality.
In VI Studio, we use sub-component analysis to identify each of the parts of the object and their status. For example, when classification analysis recognizes the Linksys MX8503 Atlas WiFi 6E, the sub-component analysis will tell you which LEDs are lit up, what color they are, and what plugs are in the correct ports. This information is critical when automating service, as it can automatically diagnose issues and visually guide users to resolution.
Temporal analysis:
Temporal analysis is the process of analyzing and understanding changes in data over time. In the context of computer vision, temporal analysis is used to track changes in image data over time, such as changes in object position, shape, and color, for example, if the LEDs on a device are blinking. Temporal analysis can also be used to analyze changes in video data, such as tracking the movement of people or objects in a video. In visual AI platforms, temporal analysis can be performed using pre-built models or custom models trained to analyze image or video data changes over time.
Another function of temporal analysis is identifying when a particular LED on a device blinks and if it is blinking quickly or slowly. This information lets the AI better understand a router’s status and automatically guides the user.
No-Code AI Model Builder:
A no-code AI model builder is a tool that allows users to create custom artificial intelligence models without writing any code. The no-code model builder in the visual AI platform enables users to create custom models using a drag-and-drop interface. They can select pre-built components, configure model parameters, and train the model using their own data. The no-code model builder makes it easy for users with limited programming experience to create custom computer vision models through intuitive front-end applications.
Bring AI out of the lab and into the real world with TechSee’s no-code VI Studio. VI is natively embedded across TechSee’s visual service platform and can be easily added to any business application via API. Possible applications include unboxing, troubleshooting, and job verification. To learn more, contact us.
