AR

See also: Machine learning terms

Introduction

Machine learning is the study of teaching machines how to learn from data and make decisions based on that information. Recently, one area of machine learning that has seen great growth in popularity is augmented reality (AR). AR refers to the integration of computer-generated graphics or information into real life scenarios.

Definition of Augmented Reality

Augmented reality (AR) is a technology that overlays digital information onto the physical world. It can be experienced through various devices like smartphones, tablets and wearables. AR allows users to interact with digital content in real-time so it appears as part of their environment.

Applications of AR in Machine Learning

AR has many applications in machine learning. One popular use for AR is gaming, where players can catch virtual creatures that appear to be part of the real world by using their device's camera and sensors to create a superimposed virtual universe on top of the physical one.

Another application of AR is education. Augmented reality can create immersive learning experiences, enabling students to interact with virtual objects and environments that appear to be part of the physical world. This makes teaching more captivating and interactive for learners.

AR is also being utilized in marketing and advertising. AR technology enables interactive advertisements that let customers try out products virtually before they make a purchase. For instance, furniture retailers can utilize AR to show customers how a particular piece would look in their home before they commit to buying it.

How Does AR Work in Machine Learning?

The fundamental concept behind AR is to utilize sensors, cameras, and algorithms to detect and track a user's position in the physical world. This data is then used to construct a virtual realm overlaid onto reality. The algorithms employed in AR are machine learning algorithms trained on vast datasets of images and video.

The machine learning algorithms utilized in AR are built upon computer vision. Computer vision is a field of study that seeks to teach machines how to interpret and comprehend images and video. In AR, these AI systems are trained to recognize objects and surfaces in the physical world, enabling them to overlay digital information onto those physical entities.

Examples of AR in Machine Learning

One example of AR in machine learning is navigation. AR allows for the overlaying of digital information onto the real world, making it simpler for users to explore their environment. For instance, an AR app could display directions overlaid onto a user's environment in real time.

Another application of AR in machine learning is medicine. Here, AR can be employed to display medical information like anatomy and procedures superimposed onto the physical world. This kind of interaction allows students to work with virtual models of human bodies during training sessions.

Explain Like I'm 5 (ELI5)

Augmented reality is a method for overlaying computer images or information onto the real world, creating the illusion that they are physically present. This is accomplished with cameras and sensors in devices like smartphones and tablets, combined with machine learning algorithms which learn from what it observes and make sure these computer images fit within its environment. Augmented reality can be employed in many different contexts such as games, education, and even medicine.