Models and Theories in Human-Computer Interaction/The Law of Accelerating Returns
The Law of Accelerating Returns (Brian Finn)
The Law of Accelerating Returns that was proposed by Ray Kurzweil takes the well-known Moore’s Law a step further to be applied to technology growth as a whole. Kurzweil discusses that the observation of technology evolution can appear linear to an observer of a short duration of time, much like two close points can appear linear even though their greater curve is actually exponential. This discrepancy in understanding of the proposed rate of technology advancement is referred to by Kurzweil as the “intuitive linear view”. He argues that there is clear data for the “historical evidential view” in such technology evolution trends as the speed of DNA sequencing, communication speeds and technology miniaturization. Kurzweil discusses that this theory changes the way we should talk about technology rate to consider the Law of Accelerating return; “ So we won’t experience 100 years of progress in the 21st century — it will be more like 20,000 years of progress (at today’s rate). .”
He applies his theory with predictions how technology will evolve to allow for computers that have the capacity of a human brain, then the integration of the human physical brain to the digital world, and lastly something called the Singularity where all human intelligence is unified into one super intelligent entity. Reverse engineering the brain to understand the computing mechanics is currently underway with techniques such as brain scanning and pattern recognition of brain activity. Although technology evolution has the momentum and track record to continue on its wild trajectory, Kurzweil touches upon possible conditions that can impede technology evolution. Such examples as a national economy and biological engineering can have complex social, ethical, and political influences on the rate of technological advancement.
Kurzweil, Ray. "The Law of Accelerating Returns". http://www.kurzweilai.net/the-law-of-accelerating-returns. Retrieved June 16, 2015.