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I don’t know anything about the underlying algorithms for the examples you rattled off, but deep learning trains a graph of neuron weights such that they are st

I don’t know anything about the underlying algorithms for the examples you rattled off, but deep learning trains a graph of neuron weights such that they are statistically optimized to minimize error in computed output labels for some domain of input data. Very much “applied computational statistics”.

YeGoblynQueenne 7y ago
The examples I gave are classic AI algorithms that are very easy to look up on wikipedia. They do not compute any statistics. I'm not sure what you mean about "neuron weights that are statistically optimised". Modern-era, deep neural nets train their weights with backpropagation, which is basically an application of the chain rule, from calculus. They do not use statistics for that. For example, calculating the mean of a set of values or calculating the pearson correlation coefficient of two variables are computations typical in statistics. Could you please clarify what you mean by (applied) "computational statistics", so that I don't have to double-guess you? Edit: Do you really not know what a SAT solver is? Not to be rude but if that is the case, from where do you draw your confidence about the correct terminology to use for AI?
- tnecniv 7y ago
  He means that neural networks are applied statistics in that they solve a statistical regression problem. It's not conceptually different from classical methods of regression like least squares. The phrase "statistically optimized" is certainly a funky one, but regression is certainly as much a part of statistics as the two problems you mentioned.
  - deleted 7y ago
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  - YeGoblynQueenne 7y ago
    That doesn't sound like what the OP was saying.