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Automating Invention is Robert Plotkin's blog on the impact of computer-automated inventing on the future of invention and patent law.
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- Robert Plotkin Interview in MIT Technology Review
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July 18, 2005
A Whole New Mind by Daniel Pink
A Whole New Mind by Daniel Pink is well worth reading if you're interested in the topics covered by this blog. Pink's argument is that holistic thinking, and a variety of skills associated with it, will become increasingly economically valuable in the coming "Conceptual Age." A relatively small portion of the book is dedicated to substantiating this claim. Most of the book focuses on describing the "six senses" -- the set of aptitudes that you will need to succeed in the Conceptual Age -- and on providing practical ways for individuals to sharpen those senses. (The six senses are Design, Story, Symphony, Empathy, Play, and Meaning.)
Pink identifies three drivers of the Conceptual Age: Abundance, Asia, and Automation. He draws a useful analogy between the defeat of the iconic John Henry by an automated steam drill and the defeat of chess grand master Garry Kasparov by the IBM supercomputer Deep Blue. Pink concludes the analogy:
Last century, machines proved they could replace human backs. This century, new technologies are proving they can replace human left brains.
I agree. But Pink's conclusion doesn't go far enough. Artificial creativity is proving increasingly able to replace human right brains. For example, human programmers were still required to program the incarnation of Deep Blue that defeated Kasparov. Kasparov may have his revenge when Deep Blue's programmers are put out of work by a genetic algorithm that evolves winning chess playing strategies. Although we're not there yet, Moshe Sipper and his colleagues have made some great strides.
In the final analysis, my extended analogy is still consistent with Pink's general thesis -- that people will need to develop higher-level conceptual skills in the coming century to remain competitive. Deep Blue's programmers' best bet for keeping their jobs in the long term is to learn how to write genetic algorithms that produce chess-playing code, rather than continuing to fine tune their skills at writing chess-playing code itself.
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