Systems | Development | Analytics | API | Testing

How to manage a legacy codebase and the “rare luxury” of starting from scratch; these are the topics we’ll be covering in this final part in our Q&A roundup series.

In my main position, as a data scientist at SIL International, I work on expanding language possibilities with AI. Practically this includes applying recent advances in Natural Language Processing (NLP) to low resource and multilingual contexts. We work on things like spoken language identification, multilingual dialogue systems, machine translation, and translation quality estimation.

Advances in the performance and capability of Artificial Intelligence (AI) algorithms has led to a significant increase in adoption in recent years. In a February 2021 report by IDC, they estimate that world-wide revenues from AI will grow by 16.4% in 2021 to USD $327 billion. Furthermore, AI adoption is becoming increasingly widespread and not just concentrated within a small number of organisations.

As data continues to grow at an exponential rate, our customers are increasingly looking to advance and scale operations through digital transformation and the cloud. These modern digital businesses are also dealing with unprecedented rates of data volume, which is exploding from terabytes to petabytes and even exabytes which could prove difficult to manage.