The term “grok” was coined by Robert A. Heinlein in his 1961 book Stranger in a Strange Land and has since been adopted by technicians, programmers, and developers to mean understanding something intuitively and empathetically. To fully “grok” something, one must develop a relationship with it and communicate with it in a sympathetic way. The process of “grok” is important to optimizing and learning something in order to gain maximum strategic advantage. In the context of machine learning, an overparameterized neural network that has memorized the training data will suddenly learn to generalize, also known as “grokking.” This contradicts traditional statistical wisdom which recommends using underparameterized models to force learning the rule and generalize to new situations.
What is “Grokking”?
The term “grok” was first coined by author Robert A. Heinlein in his book, Stranger in a Strange Land. Grokking means to intuitively and empathetically understand something. It involves developing a relationship with something and communicating with it in a sympathetic way while enjoying the experience. Essentially, when you “grok” something, you are able to fully empathize with it and understand its aspect and function.
How is “Grokking” Used in Technology?
The words “grok” and “grokking” are commonly used among technologists, particularly programmers, and developers. The process of “grokking” is used to optimize and learn something to gain maximum strategic advantage. In other words, those who can “grok” something have a significant advantage over those who do not.
How does “Grokking” Apply to Neural Networks?
When training an overparameterized neural network (one with more parameters than the number of data points in your dataset) beyond the point where it has memorized the training data, the network suddenly learns to generalize. This is indicated by a rapid decrease in val loss, also known as “grok.”
While practitioners typically pause training on networks at the first sign of overfitting, traditional statistical wisdom recommends using underparameterized models to force the model to learn the rule. However, the process of “grokking” suggests otherwise – that training beyond the point of memorization can lead to a significant breakthrough in network performance.
In Consequently, the word “grok” and the process of “grokking” have become increasingly relevant in technology, particularly in relation to neural networks. It highlights the importance of understanding something empathetically and intuitively, and the benefits this can bring to problem-solving and strategic advantage.
