Machine perception has always played a central role in AI. The most commonly studied perception modalities are Computer Vision and Natural Language Processing, each of which is included an extensive research and communities.

In terms of computer vision, progress in the computer science allows machines to have sight. The difference is much like the difference between the taking picture and seeing them. Taking the picture requires mechanism  but having sight to a machine and analysing to what to see requires a great amount of quantitative and qualitative understanding and intelligent. Very similar to the eyes brain distinction; vision begins with the eyes but where it takes place is the brain. Naming objects, understanding emotions, relations, actions and intentions are now possible by the computer vision. Developed computer algorithms are able to  understand the context of the images and describe them in sentences.

Machine Learning is a key field that enables systems to automatically improve their performance at a task by observing relevant data. There is basic distinction between supervised and unsupervised learning which due to the problems. Supervised learning is possible only the whole data right answers were provided whereas the unsupervised one use for complex solving such as given a set of news articles found on the web, group them interest of articles about the same story or given a database of customer data, automatically discover market segments and categorise costumers into different market segments.