Finding patterns equals creating models? Not quite. At least not if "model" means anything that could count as even weak AI.
Finding correlations is not the same thing as having a model or theory to explain why the correlations exist.
In particular, if the data set does not contain all relevent info, you cannot know what the casuality is between two correlated items. The old example is: "Both ice cream sales and drownings increase in certain months. Does this mean that eating ice cream leads to drowning?"
On the other hand, sometimes you don't care about "understanding". The stock market guys just want to find patterns and hope they hold true (predict) long enough to make money.
Didn't check the patent for details, but the MULTICS probe debugger had conditionals, functions, and I think hooks from the program being debugged to the debugger. Hmmm, I wonder if functions created within the debugger would be found by the dynamic linker... (MULTICS is/was an OS from the '70s; UNIX is a play on the MULTICS name.)
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Finding patterns equals creating models? Not quite. At least not if "model" means anything that could count as even weak AI.
Finding correlations is not the same thing as having a model or theory to explain why the correlations exist.
In particular, if the data set does not contain all relevent info, you cannot know what the casuality is between two correlated items. The old example is: "Both ice cream sales and drownings increase in certain months. Does this mean that eating ice cream leads to drowning?"
On the other hand, sometimes you don't care about "understanding". The stock market guys just want to find patterns and hope they hold true (predict) long enough to make money.
Didn't check the patent for details, but the MULTICS probe debugger had conditionals, functions, and I think hooks from the program being debugged to the debugger. Hmmm, I wonder if functions created within the debugger would be found by the dynamic linker... (MULTICS is/was an OS from the '70s; UNIX is a play on the MULTICS name.)