In machine learning, there’s a long term trend towards automating work that used to be done manually. For instance, ML engineers used to spend a lot of time eng

In machine learning, there’s a long term trend towards automating work that used to be done manually. For instance, ML engineers used to spend a lot of time engineering “features” which captured salient aspects of the input data. Nowadays, we generally use Deep Learning to learn effective features. That pushed the problem to designing DNN architectures, which subsequently led to the rise of AutoML and NAS (Network Architecture Search) methods to save us the trouble. And so on. We still have to provide ML agents with some kind of objective or reward signal which drives the learning process, but again, it would save human effort and make the process of learning more dynamic and adaptable if we can make machines learn useful goals and objectives on their own.