Automating Aspects of Machine Learning

A topic I brought up as a key part of the future of machine learning at our recent UC analytics summit.  And as we might expect, Google is working on it.  From the recent Google Research Blog.   This kind of problem will need lots of data to explore it, so expect companies like Google, Amazon, Apple  and Microsoft to have the data to do it.  Continuing to watch this.  As the internal architecture of AI is fine tuned by ML methods, it will become more powerful. 

Using Machine Learning to Explore Neural Network Architecture

Posted by Quoc Le & Barret Zoph, Research Scientists, Google Brain team ... 

At Google, we have successfully applied deep learning models to many applications, from image recognition to speech recognition to machine translation. Typically, our machine learning models are painstakingly designed by a team of engineers and scientists. This process of manually designing machine learning models is difficult because the search space of all possible models can be combinatorially large — a typical 10-layer network can have ~1010 candidate networks! For this reason, the process of designing networks often takes a significant amount  .... " 

As an example, they show the complexity and effort needed to develop their Googlenet Architecture.