Recently, the winds of change have started to blow in the field of Artificial Intelligence (AI) – particularly with the development of Natural Language Processing (NLP).
When first conceived, experts were unable to programme AI to interpret human language. As such, businesses have historically assigned AI programmes with the task of informing their decision-making, based on highly structured data. The task of making any cognitive or creative decisions, however, was out of bounds for AI, given that these conclusions rely on a nuanced understanding of language.
However, recent NLP breakthroughs have allowed computers to comprehend more than just basic data. In short, NLP experts have been able to teach computers to better understand ‘unstructured’ data, thus improving their ability to derive meaning from human language.
Obviously, the technology has not yet been perfected, but its efficacy in the world of business is growing by the day.
The Tech behind NLP
The main difficulty facing experts in the past was that the programming language used to train AI was highly structured, making it very difficult for machines to adapt to the unstructured nature of the human lexicon.
Although these developments are still in the nascent stages, advancements in machine and supervised learning have now made it possible for AI to emulate the ways in which humans understand and respond to language. By reducing language to its smallest semantic units, like letters and simple clauses, experts can create rule-based algorithms that enable AI programs to increase their understanding of language the more that they are exposed to it.
NLP in the World of Business
Consequently, NLP AI programs can now comprehend language more accurately than ever, and businesses are implementing these technologies to streamline their operations and organizational productivity.
Right now, a prime use for NLP in the business arena is its ability to monitor customer sentiment and help firms respond quickly to negative feedback. By inferring emotion from emails, Tweets and generic feedback, NLP can organize feedback into positive, negative and neutral categories, allowing marketing and customer service employees to access insightful metrics that can transform their consumer feedback management. In turn, this can enable firms to improve their products and services. However, these technologies still require substantial human oversight to avoid the risk of miscategorising all-important customer responses that may require urgent attention.
Meanwhile, Human Resources can improve productivity by offloading the more time-consuming aspects of recruitment to an AI programme. Recruiters could use NLP to automatically screen applications based on key words or experiences, meaning they will have to spend less time sifting through unsuitable candidates. Without this, some HR professionals might struggle to deliver the personalized support that employees need to feel valued and appreciated.
Naturally, as with any technology still in its nascency, there is the opportunity for mistakes to arise and for things to slip through the net. Specifically, the fluid complexity of human language presents some serious challenges for businesses when implementing NLP. Currently, AI technologies have issues understanding human language in context – the likes of irony and sarcasm, for example, present machines with difficulties. Certainly, the task of understanding a positive phrase that is used to connote the opposite meaning is something that even humans struggle to grapple with, particularly when conveyed in writing.
More generally, ambiguous language can prove problematic for NLP – with synonyms, homonyms and misspellings all to consider, intention can fall through the net for even the most expansive machine learning algorithms. The impact of such mistakes cannot be understated – the intention of a writer/speaker might be lost on a machine and could massively skew results of sentiment analysis or job application screening, for example.
As such, there is still much to be improved in the field of NLP, despite some increasingly useful applications in business. Indeed, the feat of achieving a human-like understanding of the language of AI has been deemed an ‘AI-complete’ problem by many experts. In other words, the task of unravelling language is equal to that of solving issues with AI itself.
Where Could NLP Go Next?
That’s not to say that organizations would not benefit from being early adopters. When planning for the future, business leaders must keep abreast of new innovations in this field to remain relevant and competitive. In particular, data analysis could become a lot easier with the introduction of NLP assistants (similar to Alexa), which can be used to automatically find key numbers within large data sets, simply by inputting a command. As a result, analysts can spend far less time manually searching for key statistics.
Investing in a technology that is still in its very early stages can be daunting, and business leaders may be sceptical about the practical uses of NLP. However, this should not stop firms from experimenting with innovative ways of improving their productivity and streamlining their operations.
By Nikolas Kairinos, CEO and Founder, Soffos.
Inspired by the Socratic Method, Soffos creates technology that automatically ingests files, stores knowledge in a proprietary format and disseminates it to learners via conversational AI.