Instinctive computing is a computational simulation of biological and cognitive instincts. It is a meta-program of life, just like universal gravity in nature. It profoundly influences how we look, feel, think, and act. If we want a computer to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have primitive instincts. The original paper proposes a ‘bottom-up’ approach that is focused on human basic instincts: forage, vigilance, reproduction, intuition and learning. They are the machine codes in human operating systems, where high-level programs, such as social functions can override the low-level instinct. However, instinctive computing has been always a default operation. Instinctive computing is the foundation of Ambient Intelligence as well as Empathic Computing. It is an essential part of Human Computing.
What is the fundamental difference between a machine and a living creature? Instinct!
Instincts are the internal impulses, such as hunger and sexual urges, which lead humans to
fulfill these needs. Freud stated that these biologically based energies are the fundamental driving forces of our life. They act everyday to protect us from danger and keep us fit and healthy. However, we are often barely aware of them.
Perhaps the most striking things for us are hidden in our cells. Recent biological studies
suggest that mammalian cells indeed possess more intelligence than we can imagine.
For example, the cell movement is not random. It is capable of immensely complex
migration patterns that are responses to unforeseeable encounters. Cells can 'see', for
example, they can map the directions of near-infrared light sources in their environment
and direct their movements toward them. No such 'vision' is possible without a very
sophisticated signal processing system.
Instinctive computing is a computational simulation of biological and cognitive
instincts. It actually started fifty years ago. Norbert Weiner studied computational
models of Gestalt, self-reproduction and learning. According to him, these functions are a
part of the holistic communication between humans, animals and machine, which he
called it ‘Cybernetics’. In parallel, John von Neumann proposed the cellular automata
model to simulate self-reproduction . The model constitutes finite state cells interactingwith one another in a neighborhood within a two-dimensional space.
The conceptual machine is far ahead of its time. Due to the limitations in hardware, people had forgottenthe idea for several decades until the 1970’s: Conway rediscovered it in his article “Game of Life” . In the model, an organism has its instinctual states, birth, movement, eating and death. Interesting patterns emerge from cell interactions such as blooming, oscillation or extinction. Wolfram further proves that many simple cellular interactions can produce very complex patterns, including chaos. He argues that interactive algorithms are more important than the mathematical equations . The spatial and temporal interaction among entities is the key to understanding their complexity.
Today, computational cellular automata have become a powerful tool to reveal the natural human algorithms, from microscopic cellular morphology to mass panic movement in subway stations. Instinct is a meta-program of life, just like universal gravity in nature. It profoundly influences how we look, feel, think, and act. If we want a computer to be genuinely intelligent and to interact naturally with us, we must give computers the ability to
recognize, understand, even to have primitive instincts. In this paper, we will review the
recent work in this area, the architecture of an instinctive operating system, and potential
applications.
Fullpaper by Dr.Yang Cai here: http://www.cmu.edu/vis/media/pdf/Cai-Instinctive-Computing-Feb-26-V6.pdf
