The accurate prediction of tide levels is very important for navigation in and around sea ports and other coastal areas. The study of tides has evolved though the work of Isaac Newton and Pierre-Simon Laplace to Sir William Thomson, Sir George Darwin, and others in order to produce a practical approach for predicting tide levels based on the movement of the Moon and Sun along with geographical details of the location of interest. As the science of tides advanced through this time the resulting calculations became increasingly time-consuming and error-prone.
In the late nineteenth century the first tide-predicting analogue computer was developed and built by Thomson with help of Edward Roberts and Alexander Légé. Over the next several decades a number of improved models were built. In 1910 the Tide-Predicting Machine No. 2, or “Old Brass Brains”, was delivered, allowing the numerical simulation of a 37-term Fourier Series estimate for tide levels. The machine stayed in service until 1965, when it was replaced by digital computation.
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For many years Byte Magazine provided articles and promotions primarily aimed at small computer users. In June 1979 they published a special edition on Artificial Intelligence, including the first part of an article by James Albus, an engineer at the US National Bureau of Standards (now the NIST). Dr. Albus’ article, A Model of the Brain for Robot Control, explored the nature of an animal brain and its responses to stimuli, and how that could be simulated with a computer. His discussion of the analysis of the structure of the cerebellum and how it could be modeled is a good introduction to what is now known as Machine Learning.
Dr. Albus provided a BASIC program suitable for running on a small computer of the time that simulated muscle control. He suggested trying different parameters to explore the nature of the system to get a sense of how motion in the human body is managed. It would be many years, however, before personal computers would be powerful enough to fully realize the potential of this approach to AI.
I graduated from Engineering at a time when slide rules were just being replaced by calculators as being acceptable for exam use. I discovered that I had a talent for using computers to solve engineering problems, which led to a career.
I have worked in the IT business for many decades, spanning mainframes to mobile. I have seen a lot of technologies come and go with many reappearing in a different guise. Having witnessed many Silver Bullet ideas fail to fire, I remain optimistic.
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