We are living through an historic era in computing. As the price of data storage and processing continues to plummet, we are moving closer to a world where exhaustive search makes sense for certain types of intelligent systems. Signal and image processing are two related domains that benefit from this ubiquity of data storage and computing power. In this paper, a new, more collaborative, approach to solving signal and image processing tasks is built from the ground up to take into account the reality of this new age of data and computing superfluity. Starting with the mature field of ensemble methods and moving to the more-recently introduced field of meta-algorithmics, systems can be designed which are by nature to specifically incorporate new machine-learning technologies. These are more robust, more accurate, more adaptive, and ultimately less costly to build and maintain than the traditional machine-learning approaches. Applications to image and signal processing will then be discussed. Combined, these examples illustrate a new meta-architectural approach to the creation of machine intelligence systems.
