Machine Perception refers to the added functionality in computer systems that enables reaction based on senses, similar to human perception. Computers now have the added capacity to see, hear, touch, and in some cases even smell. The goal of this functionality is to learn and react as a human would, so the computer can alert human operators to any impending issues and help troubleshoot.
Computer vision, sometimes called machine vision, refers to the way in which computers analyze and interpret images or videos. Obtaining and understanding images is a functionality used quite often in this digital revolution for facial recognition software and image classification through convolutional neural networks (CNN). Machine hearing is the computer's ability to decipher sounds, such as speech and music, and process the sound data. This is used for recording music and in voice recognition software in cars and on smartphones. Machine touch generally attempts to gain information based on tactile interaction with physical surroundings. This functionality is less widely used, as recreating a real-world physical reaction in an artificial intelligence (AI) capacity has not yet been fully realized. Similarly, machine smell, or olfaction, is still in its early stages. The intended use of machine olfaction is for chemical analysis and necessary alerts.
Machine learning refers to the overall data analysis that improves over time as it "learns", but machine perception specifically involves the human senses and their capacity to receive and process information. Whether the incoming data is a face or an image or a string of music notes, object recognition and analysis are improving daily. As each new set of data adds on, the system as a whole becomes more appropriately reactive and even predictive. Fully realizing the benefits of machine learning requires analysis through all human senses and how they continuously learn, grow, and react to incoming information.