Summary: Artificial intelligence (AI) is gaining lots of traction today, and there often can be confusion as to what it is in comparison to machine learning, deep learning, computer vision and predictive analytics. You’ve probably heard companies and people referring to “machine learning and AI,” but that would be a misnomer. The short answer is that machine learning is AI. AI is the overarching field and machine learning and the other technologies identified below are simply different types of AI.
Machine learning: Is the form of AI that is able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in the data.
Deep learning: Deep learning is a deeper level of machine learning, that teaches computers to process data in a way that mimics the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.
Computer vision: Is a field of AI that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information.
Predictive analytics: Predictive analytics is often counted as a subservice of AI, yet it doesn’t always use AI. It is the process of using data to forecast future outcomes, by analyzing data or using AI models to find patterns that might predict future behavior.
The Integral Role of Machine Learning in Computer Vision
While computer vision and machine learning are separate forms of AI, computer vision often relies on machine learning techniques to derive insights from images and video and other visual representations of the world.
Consider some of the following ways that machine learning empowers effective computer vision:
Object Detection: Machine learning algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are used to detect and locate objects within images or video. These models can learn to recognize specific patterns and features that represent objects of interest. For example, in one project, Wovenware worked closely with its now parent company, Maxar Technologies, to develop computer vision models that would help to detect aircraft in airports around the world, based on images from satellite imagery.
Image Classification: As part of the computer vision data preparation process, machine learning models are used to classify images into predefined categories or labels, which enable accurate image recognition, such as identifying which objects represent trees, vehicles or buildings.
Semantic Segmentation: Machine learning models can segment images into different regions and assign semantic labels to each region. Semantic segmentation is a deep learning algorithm that associates a label or category with every pixel in an image. For example, it’s important for a self-driving car to be able to accurately identify other cars, pedestrians, traffic signs or objects in the road.
Object Tracking: Machine learning also can help to track objects’ movements across multiple frames in videos. This is used in applications like surveillance, autonomous vehicles, and augmented reality.
Facial Recognition: Machine learning models are used to recognize faces from images or video streams, enabling applications such as facial authentication and video surveillance.
Anomaly Detection: It also can be used to detect unusual or out-of-the-ordinary patterns in images or videos. This can be helpful in tasks such as fraud detection in forms, issues on the manufacturing line or for security surveillance.
Avoiding Bias in Computer Vision
Advancements in machine learning and computer vision are enabling a new level of data-driven insights, enabling us to see and interpret the world around us, but as with all forms of AI, it’s important to ensure that models are developed to be fair and unbiased in how they make decisions.
Data scientists and data teams need to consider where the data is coming from and if it is representative and diverse. Without a representative, diverse sample, AI algorithms can deliver incomplete or false outcomes. For example, in computer vision, if AI algorithms are only given data for one group of people, Caucasians for example, they may not be able to correctly identify people of other races. When gathering data and building models, the more diversity that is built into the data, the better, because it enables data scientists to develop more accurate algorithms. It’s also important to include bias testing of AI algorithms into your quality assurance practices.
Machine learning continues to be a key driver to computer vision applications, enabling them to learn and adapt from data and driving more accurate image and object recognition. Advancements in these forms of AI are enabling humans to derive actionable insights previously impossible by humans alone. They’re giving a whole new level of awareness to inform how we conduct urban planning, how we make safer environments and how we take care of the planet, among a variety of other applications that are changing the world.