Amazon researchers say evolutionary approach improves the selection of AI models

KYLE WIGGERS | September 23, 2019

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Selecting an architecture is a critical step in crafting any AI model, but it’s easier said than done. Excepting those generated by “autoML” systems that work from a basic task outline, their design is informed by a combination of historical precedent, domain knowledge, and trial and error. Amazon researchers believe there’s a better way — namely one involving computational methods that generate tailored architectures. In a paper (“On the Bounds of Function Approximations”) presented last week at the International Conference on Artificial Neural Networks in Munich, they explore techniques that apply to any computational model, providing the model can compute the same functions a Turing machine can. (In this context, a Turing machine refers to a model defining an abstract machine that manipulates symbols according to rules.)

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