The Second Half of the Chessboard

rice

Legend has it that the king of India (or Persia) rewarded the inventor of chess with anything he wanted.  The inventor of chess ask for one grain rice on the first square of the board, two grains of rice on the second square of the board, four grains on the third, eight on the fourth, and so on, doubling each time for each square of the chessboard.  The king agreed, unsuspecting of the request, but after doing the math, he realized that is enough rice to cover all of Europe and Asia.

18,446,744,073,709,551,616 grains of rice.

This is an example of exponential growth.  The reason exponential growth can be deceptive is because the numbers don’t seem very large or significant until after a certain period in time.  Then they become ridiculous and unmanageable.

Moore’s Law

Another example of exponential growth is Moore’s law.  Moore’s law specifically refers to the doubling of transistors in a computer chips every one and a half year.  Basically, computers are getting faster exponentially.  This trend is not limited to computer processors but can also apply to technology in general.

1997:  A computer can beat the best human at chess.
2007: A computer can drive a car.
2011:  A computer can be the best human at Jeopardy.

We are approaching a time period that some people consider to be “the second half of the chessboard”.  A bunch of Star Trek technology already exists in our everyday life: automatic doors, touch screens, voice command, wireless communication.  I predict that with the advances in computer intelligence the next big thing is going to be the universal translator.  We can already see with robot cars that machine intelligence has evolved beyond just shoveling information around on the internet to actually having an impact on the real physical world.

Do Androids Dream of Electric Sheep?

Computers are becoming increasingly good at handling under structured data, meaning they can solve more abstract problems.  A computer’s ability to solve problem is no longer bounded by one algorithm or set of rules.  Instead, computers are now able to solve more complex problems by changing and adapting to different situations.  The application in computer vision has benefited immensely from this advance in technology.  A good example of how far this technology has come is Google’s deep dream.  It is a visualization of how a computer execute object recognition, similar to how a human looks at a cloud and can see different objects.

deep dream

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