It’s now remarkably easy to release to the world a cloud-based application programming interface (API) that provides some software function as a service. As a consequence, the cloud API space has become very densely populated, so that even if a particular API offers a service whose potential value is considerable, there are many other factors that play a role in determining whether or not that API will be commercially successful. If you’re thinking about entering the API marketplace with your latest and greatest idea, this post offers some entirely subjective advice on how you might increase the chances of your offering not being lost in all the noise.

The law has language at its heart, so it’s not surprising that software that operates on natural language has played a role in some areas of the legal profession for a long time. But the last few years have seen an increased interest in applying modern techniques to a wider range of problems, so I look here at how natural language processing is being used in the legal sector today.

The Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category.