In the world of machine learning, the allure often lies in the dazzling advancements: the latest models, cutting-edge techniques, and breakthrough success stories. It's comparable to admiring the tip of an iceberg without recognizing the substantial structure beneath the surface that keeps it afloat.
Using a machine learning model to classify an object (be it text, image, tabular data) with a label that the model has never seen before, Zero-Shot Classification, seems like an impossible task.
In our previous blog post, we introduced how we use similarity-based machine learning for accounting use cases. In this blog post, we will focus on how we use generative machine learning at Digits.
Early in 2022, Digits released Boost to help accountants save time by automating their work. Boost instantly spots inconsistencies in their client's ledger. Every second, Digits sifts through every single transaction and performs a deep analysis. Digits' Boost alerts accountants if it finds errors like transactions in unexpected categories and suggests categories for transactions with missing categories.