The grey matter
We strongly believe that people should not be forced to adapt to machines and their needs and working methods, rather, it should be the opposite. This is why we are passionate to make machines genuinely aware of their tasks and environment, and equip them with the ability to adapt to changing conditions and to draw independent conclusions. What is more, we want them to communicate with the user using means that are natural and intuitive to human beings. These ends require special means. This section is intended as an introduction to technologies we use to make that happen. Make yourself familiar with the basics.
Artificial Intelligence
Artificial intelligence (or AI) is both the intelligence of machines and the branch of computer science which aims to create it. Major AI textbooks define artificial intelligence as "the study and design of intelligent agents," where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. AI can be seen as a realization of an abstract intelligent agent (AIA) which exhibits the functional essence of intelligence. John McCarthy, who coined the term in 1956, defines it as "the science and engineering of making intelligent machines." Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") has not yet been achieved and is a long-term goal of AI research.
Read the complete Wikipedia article about Artificial Intelligence
Pattern Recognition
Pattern recognition is a sub-topic of machine learning. It can be defined as "the act of taking in raw data and taking an action based on the category of the data". Most research in pattern recognition is about methods for supervised learning and unsupervised learning. Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. This is in contrast to pattern matching, where the pattern is rigidly specified.
Read the complete Wikipedia article about Pattern Recognition
Machine Vision
Machine vision (MV System) is the application of computer vision to industry and manufacturing. Whereas computer vision is mainly focused on machine-based image processing, machine vision most often requires also digital input/output devices and computer networks to control other manufacturing equipment such as robotic arms. Machine Vision is a subfield of engineering that encompasses computer science, optics, mechanical engineering, and industrial automation. One of the most common applications of Machine Vision is the inspection of manufactured goods such as semiconductor chips, automobiles, food and pharmaceuticals. Just as human inspectors working on assembly lines visually inspect parts to judge the quality of workmanship, so machine vision systems use digital cameras, smart cameras and image processing software to perform similar inspections.
Computer Vision
Computer vision is the science and technology of machines that see. As a scientific discipline, computer vision is concerned with the theory for building artificial systems that obtain information from images. The image data can take many forms, such as a video sequence, views from multiple cameras, or multi-dimensional data from a medical scanner.