Most of us are already aware of how profoundly AI technology has changed our lives – in both personal and professional spheres. Whether it’s asking our virtual assistant for the weather forecast or solving a customer service problem with a chatbot, AI technology has evolved to both make our lives easier and mitigate challenges.
In the business world, AI recognition technology is being leveraged to solve complex challenges by turning images, objects, and people into business intelligence.
How? Keep reading.
Computer vision technology
Before we dive into how AI recognition technology is solving business challenges, let’s start from the beginning – with computer vision technology. At its core, computer vision is the technology that enables AI systems to ‘see’ the way humans do. It’s a field of science that enables computers to capture, process, interpret, and understand visually perceivable objects.
Businesses across a wide range of industries are leveraging computer vision technology from quality control to predictive analysis, and more.
As this technology has advanced, facial recognition programs have become some of the most well-known and often used tools, and several factors have combined to bring about a revolution in computer vision, including:
- More widely available hardware designed for computer vision and analysis
- Mobile technology with a built-in camera
- Algorithms that optimize the use of hardware and software capabilities
- More affordable and easily accessible computing power
How does this tech work?
While computer vision and image recognition are often conflated, they are not the same thing. Image recognition is all about images and identification, while computer vision can do a lot more.
There are many areas in which computer vision not only matches, but actually surpasses the visual abilities of humans, such as processing live events and recognizing objects and faces. There are three basic steps that enable computer vision:
- Image acquisition
Using video, photos, or 3D technology, computer vision acquires images in real-time for analysis.
- Processing of the Image
Typically, thousands of pre-identified or labeled images are fed to deep learning models to train them and from there, this process can be automated.
- Understanding the Image
The last step in computer vision is the understanding or sense making of the image or object being reviewed. It’s part of this step where the image or object is initially identified or classified.
Computer vision can be taken a step further than this process by using AI systems to perform actions that are based on the technology’s understanding or interpretation of the image or object.
The spectrum of AI recognition technology
AI-powered computer vision technology can provide a vast array of possibilities when it comes to helping businesses solve problems. This list is by no means exhaustive, but does include some of the more well-known types of AI recognition technology:
- Facial recognition. This is a sophisticated technology that detects objects and cannot only recognize a human face in an image, but can also identify a specific individual.
- Image reconstruction. With image reconstruction, an old or damaged image can be restored to its original form (or at least get very close) by repairing the corrupted versions of the image.
- Object tracking. As the name indicates, this type of computer vision tracks moving objects in a given scene. It’s typically used to track interactions after the detection of the initial object. This type of computer vision can be used for technology such as self-driving cars.
- Gesture detection. Gesture detection uses mathematical algorithms to interpret human gestures and is a promising Human-Machine Interaction technology. A perceptual user interface component allows the computer to capture and interpret human gestures as commands. This is useful for a variety of purposes, including interpreting sign language.
AI recognition use cases
Now that we have a basic understanding of computer vision technology and AI recognition, let’s explore a few ways that businesses across several industries are leveraging it to make smarter decisions and optimize their processes.
- Security systems and protocols. Technology like biometrics and facial recognition can be used for security purposes across an array of use cases. Securing smartphones is the most common example of facial recognition or biometrics being used for security purposes. Businesses can also use more sophisticated forms of facial recognition (such as unique physiological features) to keep their facilities safe. Additionally, the unique fingerprint patterns of an individual can be identified with deep learning and then used to control access to bank vaults, research labs, nuclear power plants, and other high-security areas. This can not only heighten a facility’s security, but free up time for humans to focus on more sensitive or strategic tasks.
- Customer experience. Businesses are always on the lookout for new, innovative, and personalized experiences for their customers. It can be a great way to stand out from the competition and keep customers coming back for more. For example, businesses can use facial recognition for their apps to welcome customers back quickly, without logging in, and immediately load their preferences for a streamlined and personalized experience. This can also be a boon for any business looking to improve their customer service. AI recognition can help reduce troubleshooting time by recognizing patterns of the most frequent issues and learning about customers’ behaviors and preferences based on past interactions.
- Data analysis. Collecting, analyzing, sorting, and making sense of data can be an onerous and time consuming task. While data analysts are highly trained and great at what they do, AI recognition technology can help take their role to the next level. AI technology can be “taught” to recognize patterns and trends in data faster and easier than the human brain, accelerating the time it takes to get from data ingestion to data insights. This also means less time spent parsing through data and more time for acting on critical data-driven insights.
The future of AI recognition
This is just the tip of the iceberg of possibilities for AI recognition technology for businesses. As research and innovation evolves, it will also become easier to train these technologies in the future to extract even more insights than they currently do and help optimize everything from business intelligence to sales to marketing.
At AscentCore, we stay on top of the latest technologies that will accelerate our clients’ digital transformation journeys. Our world-class team is ready to deliver exceptional solutions to solve your most complex challenges. Let’s talk.
