AutoML-Zero, resembles the process of evolution, with code improving every generation with little human interaction.Machine learning tools are “trained” to find patterns in vast amounts of data while automating such processes and constantly being refined based on past experience.
AutoML-Zero is designed to create a population of 100 “candidate algorithms” by combining basic random math, then testing the results on simple tasks such as image differentiation. The best performing algorithms then “evolve” by randomly changing their code.
The results—which will be variants of the most successful algorithms—then get added to the general population, as older and less successful algorithms get left behind, and the process continues to repeat. The network grows significantly, in turn giving the system more natural algorithms to work with.
AutoML is a tool from Google that automates the process of developing machine learning algorithms for various tasks. It’s user-friendly, fairly simple to use, and completely open-source. Best of all, Google‘s always updating it.
It is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space.
Despite the vastness of this space, evolutionary search can still discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, e.g. CIFAR-10 variants, where modern techniques emerge in the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging.
Moreover, evolution adapts algorithms to different task types: e.g., dropout-like techniques appear when little data is available.
In other words: Google‘s figured out how to tap evolutionary algorithms for AutoML using nothing but basic math concepts. The developers created a learning paradigm in which the machine will spit out 100 randomly generated algorithms and then work to see which ones perform the best.
After several generations, the algorithms become better and better until the machine finds one that performs well enough to evolve. In order to generate novel algorithms that can solve new problems, the ones that survive the evolutionary process are tested against various standard AI problems, such as computer vision.
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