One set of experiments from Google’s DeepMind group suggests that what researchers are terming “learning to learn” could also help lessen the problem of machine-learning software needing to consume vast amounts of data on a specific task in order to perform it well.https://www.technologyreview.com/s/603381/ai-software-learns-to-make-ai-software/
The researchers challenged their software to create learning systems for collections of multiple different, but related, problems, such as navigating mazes. It came up with designs that showed an ability to generalize, and pick up new tasks with less additional training than would be usual.
....Otkrist Gupta, a researcher at the MIT Media Lab...was inspired to work on the project by frustrating hours spent designing and testing machine-learning models. He thinks companies and researchers are well motivated to find ways to make automated machine learning practical.
“Easing the burden on the data scientist is a big payoff,” he says. “It could make you more productive, make you better models, and make you free to explore higher-level ideas.”