Enterprise hits and misses - automating deep learning and handling software audits

Quotage: "We are still decades from Star Trek-style artificial general intelligence that could pass the Turing test or outperform humans on a gamut of unrelated cognitive tasks. In the meantime, a promising compromise would be the ability to automate model selection and tuning based on the problem and available data, and then select the best options from a portfolio of deep learning software each designed for different applications." Thus the theme of Marko's useful offering, which gets into the practicalities of "automated algorithm selection," where the proper algorithm for a narrower use case is machine-determined. I can see why Marko argues that helping companies sort the right algorithms could make up for lack of internal data science expertise. More APIs and "metadata taxonomies" are needed.