For half a century, computer scientists have been working on systems for discovering lawful patterns in letters, numbers, words and images. The research has expanded into the computational study of the process of scientific discovery, producing such well-known AI programs as BACON and DENDRAL. However, autonomous discovery systems have been rarely used in the real world. While many factors have contributed to this, the most chronic difficulties seem always to fall into two categories: (1) the representation of the prior knowledge that people bring to their tasks, and (2) the awareness of new context.

Many difficult scientific discovery tasks can only be solved in interactive ways, by combining intelligent computing techniques with intuitive and adaptive user interfaces. It is inevitable that human intelligence is used in scientific discovery systems. For example, the human eyes can capture complex patterns and relationships, along with detecting the exceptional cases in a data set. The human brain can easily manipulate perceptions (shape, color, balance, time, distance, direction, speed, force, similarity, likelihood, intent and well-being) to make decisions. This process consists of perception and communication and it is often ubiquitous and autonomous. We refer to this kind of intelligence as ambient intelligence (AmI).

Ambient intelligence is about human interaction with information in a way that permits humans to spot interesting signs in massive data sources – building tools that capitalize on human strengths and compensate for human weaknesses to enhance and extend discovery capabilities. For example, people are much better than machines at detecting patterns in a visual scene, while machines are better at manipulating streams of numbers.