Design is a universal principle present in all things, animate and inanimate, as explained by Adrian Bejan's constructal law. At every scale of matter, time and organization there are design processes at work that recognize differences and exploit those differences to accomplish constructive work. These design processes are embodied as natural machines that do the work of intelligent construction at micro scales that become visible at macro scales as patterns, terrains and networks. Alex Wissner-Gross has conceived of intelligence as “an engine for maximizing future freedom of action.” His theory posits that two factors contribute to the quality of intelligence in a given system, the length of time frame and the number of entities within the system that are considered as part of this maximization. There is a bridge between Wissner-Gross’s intelligent agents and the evidence of design in Bejan’s constructal law in the form of emergent networks that maximize flow. Human technology has become better and better over time at creating natural machines of its own. Particularly in the areas of software applications and social networks it is apparent that these human domains have become areas of inquiry relevant to all areas of animate and inanimate natural phenomena. Artificial intelligence, machine learning, natural language processing, social media as well as the rendering of software applications themselves on diverse environments of devices all provide instructive analogies that illuminate these fundamental design principles. From this perspective, heat transfer is an app that has users that include snowflakes. Not all design processes are equal and the consideration of the failures of natural machines is as instructive as their successes. Computational linguistics, for instance, can quantify miscommunication as well as predict patterns of language usage. The degree of success of a design process, of a natural machine, should be discernible through the shape and configuration of the networks it engenders.
Presented on April, 8 2014 | Duke Network Analytics Center
Referenced in http://onforb.es/1ur7qOz
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