Automatic machine learning (AutoML) is a fast-growing area of deep learning.
In simple terms, AutoML seeks to automate the end-to-end process of applying machine learning to real-world problems.
Unlike other machine-learning techniques, AutoML requires relatively little human effort, which means companies might soon be able to utilize it without having to hire a team of data scientists.
AutoML-Zero is unique because it uses simple mathematical concepts to generate algorithms “from scratch,” as the paper states.
Then, it selects the best ones, and mutates them through a process that’s like Darwinian evolution.
AutoML-Zero first randomly generates 100 candidate algorithms, each of which then performs a task, like recognizing an image.
The performance of these algorithms is compared to hand-designed algorithms.
AutoML-Zero then selects the top-performing algorithm to be the “parent.” “This parent is then copied and mutated to produce a child algorithm that is added to the population, while the oldest algorithm in the population is removed,” the paper states.
The system can create thousands of populations at once, which are mutated through random procedures. Over enough cycles, these self-generated algorithms get better at performing tasks.
“The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters and is able to plug away 24/7 working on developing new algorithms”.