Distributive ideation

Artificial Ideation, Aid, or the use of artificial intelligence to help generate ideas is in the early stages of development and dependent on further advancement in hardware and software programs, including programmable processors, open data (cloud storage, and historical data sets) and deep learning and artificial neural networks. Yet Aid is attractive in that it has the potential for offering a more efficient ideation process, and both in terms of range and speed of new ideas for innovation. But training artificial intelligence models in huge data centres, here ideas centres, might restrict or hold back Aid serving local needs or conditions (data discrimination or input bias). This suggests a decentralised model of Aid, or distributive ideation that would rely on decentralised rather than centralised computing power to facilitiate and encourage ideation at, say, smartphone level. Although distributive ideation would have less processing power than the hardware accelerators used in data centres, it would facilitate bottom-up rather than top-down ideation encouraging wider participation and collaboration in the design process. Also, distributive ideation would consume less energy and therefore have a positive impact on reducing carbon emissions. (This blog was triggered by researchers in University of Cambridge's Department of Computer Science and Technology set out to investigate more energy-efficient approaches to training AI models.)