Making AI’s arcane neural networks accessible

Data scientists remain in hot demand, but they will give up more of their core functions this year and beyond to automated tools

Making AI’s arcane neural networks accessible
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We’re only a few weeks into the new year, but already we’re seeing signs that automated machine learning modeling, sometimes known as autoML, is rising to a new plateau of sophistication.

Specifically, it appears that a promising autoML approach known as “neural architecture search” will soon become part of data scientists’ core toolkits. This refers to tools and methodologies for automating creation of optimized architectures for convolutional, recurrent, and other neural network architectures at the heart of AI’s machine learning models.

Neural architecture search tools optimize the structure, weights, and hyperparameters of a machine learning model’s algorithmic “neurons” in order to make them more accurate, speedy, and efficient in performing data-driven inferences. This technology has only recently begun to emerge from labs devoted to basic research in AI tools and techniques. The research literature shows that neural architecture search tools have already outperformed manually designed neural nets in many AI R&D projects.

Commercialization coming to neural architecture search

Within the burgeoning autoML space, neural architecture search is showing signs of early commercialization.

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