AI is rapidly penetrating various facets of our lives, from being our home assistant to fielding our queries as automated tech support. Various industry outlook reports project that AI will create millions of jobs (projection range between 200 and 500 million) worldwide by the year 2030. The majority of these jobs will require ML and NLP skills, and therefore it is imperative for engineers and technologists to upskill and prepare for the impending AI revolution and the rapidly evolving tech landscape.
NLP consistently features as the fastest-growing skill in demand by Upwork (largest freelancing platform), and the job listings with an NLP tag continue to feature prominently on various job boards. Since NLP is a subfield of ML, organizations typically hire candidates as ML engineers to work on NLP projects. You could be working on the most cutting-edge ideas in large technology firms or implementing NLP technology-based applications in banks, e-commerce organizations, and so on. The exact work performed by NLP engineers can vary from project to project. However, working with large volumes of unstructured data, preprocessing data, reading research papers on the new development in the field, tuning model parameters, continuous improvement, and so on are some of the tasks that are commonly performed. The authors, having worked on several NLP projects and having followed the latest industry trends closely, can safely state that it's a very exciting time to work in the field of NLP.
You can benefit from learning about NLP even if you are simply a tech enthusiast and not particularly looking for a job as an NLP engineer. You can expect to build reasonably sophisticated NLP applications and tools on your MacBook or PC, on a shoestring budget. It is not surprising, therefore, that there has been a surge of start-ups providing NLP-based solutions to enterprises and retail clients.
A few of the exciting start-ups in this area are listed as follows:
- Luminance: Legal tech start-up aimed at analyzing legal documents
- NetBase: Real-time social media feed analytics
- Agolo: Summarizes large bodies of text at scale
- Idibon: Converts unstructured data to structured data
This area is also witnessing brisk acquisition activities with larger tech companies acquiring start-ups (Samsung acquired Kngine; Reliance Communications acquired chatbot start-up Haptik; and so on). Given the low barriers for entry and easily accessible open source technologies, this trend is expected to continue.
Now that we have familiarized ourselves with NLP and the benefits of gaining proficiency in this area, we will discuss the current and evolving applications of NLP.
