Swiss data services company Unit8 highlights the key analytics trends that we will see accelerating in 2022 in its "Advanced Analytic Trends Report". The report compiles feedback of industry leaders from Merck, Credit Suisse, and Swiss Re, on using mega models in top-tier companies. Mega models (e.g GPT-3, Wu Dao 2.0, etc.) show impressive performance yet are extremely costly to train. Only a few companies are able to compete in this space, nonetheless, the availability of these mega models opens the possibilities to new applications. There is still a major challenge around quality control before these are broadly adopted in a business environment but they already assist developers in writing snippets of code.

The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.