>In Learning Transferable Architectures for Scalable Image Recognition, we apply AutoML to the ImageNet image classification and COCO object detection dataset... AutoML was able to find the best layers that work well on CIFAR-10 but work well on ImageNet classification and COCO object detection. These two layers are combined to form a novel architecture, which we called “NASNet”.
[https://research.googleblog.com/2017/11/automl-for-large-sca..., November 2017]
In contrast AutoML is, as the nytimes article describes, "a machine-learning algorithm that learns to build other machine-learning algorithms". More specifically, from the Google blogpost about AutoML:
>In our approach (which we call "AutoML"), a controller neural net can propose a “child” model architecture, which can then be trained and evaluated for quality on a particular task...Eventually the controller learns to assign high probability to areas of architecture space that achieve better accuracy on a held-out validation dataset, and low probability to areas of architecture space that score poorly.
[https://research.googleblog.com/2017/05/using-machine-learni..., May 2017]
Quoc, Barret, and others have been working on ANN-architecture-design systems for a while now (see: https://arxiv.org/abs/1611.01578), and AutoML specifically was done before announcing NASNet. Saying that NASNet is "the actual research behind AutoML" is drawing the causal arrow backwards.
In programming we have tons of automation as well and we haven't ditched the programmer yet. Programming is auto-cannibalizing itself since its inception, each language automating more of our work. Even in ML, 10 years ago it was necessary to create features by hand. This required a lot of expertise. Today it's been automated by DL, but we have more AI scientists than ever and the jobs are even better paid.
So I don't think meta-learning is a fluff idea, and we don't have to fear it replacing humans yet. Instead, it will make AI more robust. The only minus I see is that it requires a lot of compute, but we can rent that from the cloud (make an architecture search for a few thousand dollars), we don't need to fork millions of dollars like the big labs who own their hardware. And we don't need this kind of intensive DL all the time, just once maybe, for a project. After we find the best architecture and hyperparameters, we can use that and train normally. By collating meta-learning data across many projects, we can make training faster and cheaper, reusing insight gained before.
Feature engineering is actually still the hardest part of most ML tasks, because it can not be optimized by a simple grid search like the hyperparameters of a model.