(These are excerpts from my book "Intelligence is not Artificial")
Intermezzo and Trivia: the Original App
At the same time the real achievements of the machine are sometime neglected. I am not terribly impressed that computers can play chess. I am much more impressed that computers can forecast the weather, since the atmosphere is a much more complex system than the game of chess. The media have always devoted more attention to the game of chess because its rules are easier to explain to the general public, whereas the rules that guide air flow and turbulence are rather exotic.
However, it turns out that weather forecasting was the original
"app".
Weather forecast was the "mission impossible" of the early computers. The first weather forecast using a computer simulation dates back to March 1950, to the early history of electronic computers. The computer was an ENIAC and it took just about 24 hours to calculate the weather forecast for the next 24 hours. Weather forecasting was a particularly interesting application of electronic computing for John Von Neumann. In fact, it was "the" application originally envisioned for the machine that Von Neumann designed at Princeton's Institute for Advanced Studies (IAS),
using ideas from ENIAC inventors John Mauchly and Presper Eckert,
the machine that introduced the "Von Neumann architecture" still used today. Mathematicians had known for a while that solving this problem, i.e. modeling the air flow, required solving a nonlinear
system of partial differential equations  Lewis Richardson had published the milestone study in this field, "Weather Prediction by Numerical Process" in 1922  and that is why mathematicians thought this was an avantgarde problem; and that's why Von Neumann felt that solving it with a computer would not only help the community of meteorologists but also prove that the electronic
computer was no toy. The ENIAC program, however, used an approximation devised
by Jule Charney in 1948 ("On the scale of atmospheric motions"). A computer model for the general circulation of the atmosphere had to wait until 1955, when Norman Phillips, also at Princeton, presented his equations at the Royal Meteorological Society, and fed them into the IAS computer Maniac i ("The general circulation of the atmosphere",
1955). Meanwhile, the ability to predict the weather was dramatically improved
in 1957 when the first satellite was launched.
By 1963 a Japanese scientist at UCLA, Akio Arakawa, had tweaked Phillips' equations and written a Fortran program on an IBM 709, with help from IBM's Large Scale Scientific Computation Department in San Jose ("Computational Design for LongTerm Numerical Integration of the Equations of Fluid Motion", 1966). IBM was obviously ecstatic that their computer could be used to solve such a strategic problem as predicting the
weather. It was the Fortran programming language's baptism of fire, as the 709
was the first commercial computer equipped with a Fortran compiler. At this
point it looked like it was just a matter of waiting for computers to get
faster. Alas, in the same year that Arakawa produced the first meaningful
weather forecast, Edward Lorenz proved that the atmosphere belongs to the class of system now known as "chaotic" ("Deterministic Nonperiodic Flow", 1963): there is a limit to how accurately one can predict their behavior. In fact, as computers grow exponentially faster due to Moore's law, weather forecast models have not become exponentially more
accurate. Robin Stewart has shown that "despite this exponential increase in computational power, the accuracy of forecasts has increased in a decidedly linear fashion" ("Weather Forecasting by Computers", 2003). Even today meteorologists can only give us useful forecasts of up to about a week.
Note that, unlike chess and machine translation, this problem is not currently solved by using statistical analysis. It is solved by observing the current conditions and applying physical laws (as derived by those pioneering scientists). Statistical analysis requires an adequate sample of data, and a relatively linear behavior. Weather conditions, instead, are never the same, and the nonlinear nature of chaotic systems like the atmosphere makes it very easy to
come up with grotesquely wrong predictions. This does not mean that it is
impossible to predict the weather using statistical analysis; just that it is
only one method out of many, a method that has been particularly successful in
those fields where statistical analysis makes sense but was not feasible before
the introduction of powerful computers. There is nothing magical about its
success, just like there is nothing magical about our success in predicting the
weather. Both are based on good oldfashioned techniques of computational
mathematics.
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