Accelerating Change – A Good Read

Accelerating Change – A Good Read

Writing to you within hours of summer solstice, 2010 – we now have 2 1/2 years (approximately) to the time that has been targeted by multiple cultures as a “pivot point” in human experience.

The idea that we are accelerating in our experience on this planet is not new. Right now, this idea is receiving a great deal of attention – too much of which is “acceleration” of emotional content, and not an objective assessment.

In this sense, John Smart’s Brief History of Intellectual Discussion of Accelerating Change: Accelerating Universal Phases of Physical-Computational Change is the best overview discussion I have encountered so far. Smart identifies how the notions of “acceleration,” together with “phase changes” in human experience, have entered our collective consciousness and vocabulary. He gives credit to original thinkers, ranging from physicists and futurists to science fiction writers. Good reading lists, up through about 2005.

Smart and associates also participate in the Acceleration Studies Foundation.

Some sources for newer work include: Futures, an Elsevier journal; a recent article is on Linking Foresight and Sustainability, by Floyd and Zubevich.

One of the biggest questions that we have is: What is the supporting data? For example, is Moore’s Law still in effect?

Starting about ten years ago, with his online precis, The Law of Accelerating Returns, and more recently with his book, The Singularity is Near, Ray Kurzweil captured our collective attention with a stunning series of graphs portraying not only Moore’s law in effect, up through about 2000, but also some tantalizing data that suggested an “exponential acceleration on top of the exponential acceleration.”

Does this still apply?

We look at a recent breakthrough by Jean-Pierre Colinge and colleagues, on junctionless transistors, as an example.

Looking at the most recent Moore’s Law extrapolations, we find them continuing in work published in IEEE SpectrumMoore’s Law Meets Its Match and Outperforming Moore’s Law.

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