It’s one thing to be able to align the players on a soccer field, quite another to play the game itself. Aligning the players requires only a fixed, finite knowledge of each player’s initial positioning, whereas playing a game of soccer requires one to assess and dynamically react to an exponential and ever-changing set of potentialities. Much like soccer, human language is an intricately-structured and ever-changing game. This fact has long posed difficulties for teams and businesses aiming to both create and integrate efficient and effective AI solutions. Yet, an important distinction is now beginning to emerge in language intelligence technologies, one that is aligning focus and opening doors to more dynamic and potent AI solutions.
NLU versus NLP
Natural Language Processing (NLP) represents a machine’s capabilities to organize language in a way that is comprehensible and coherent; a machine’s ability
to assemble language according to known patterns so that it can be interpreted by a computer. NLP structures words and numbers, applies grammatical rules, and can execute data and statistical calculations where required. It does not, however, “understand” why it is performing the tasks listed above or to what end. NLP simply performs its programmed functions (the scope of which continue to increase) much like a coach first arranges his/her players on a soccer field before the actual game begins.
Natural Language Understanding (NLU) concerns the game itself--it goes beyond the mere “setup” or positioning of language and extends into the realm of developing adaptive strategies. As its name suggests, NLU brings forth the machine capability of understanding language. Of machines—by way of devices like semantic algebra and hierarchical ordering—being able to contextually discern and apply meaning with regard to language. Not just one process, Natural Language Understanding is the coalescence of many small algorithms all working in concert. This means that as new, more targeted algorithms are written and imparted, NLU’s capacity to dynamically engage and react to linguistic information is, correspondingly, enhanced. The benefits of such a technology are immense and have been evidenced already in the midst of the Covid-19 pandemic.
The Covid-19 pandemic has brought with it an ongoing barrage of advisories, restrictions, best-practice guidelines and updates issued by medical professionals and state and federal government agencies. The swift-moving nature of the coronavirus paired with our unfamiliarity with, and only slowly developing understanding of, its pathology presents a clear case for the usefulness of NLU technologies. Those life science companies currently utilizing NLU possess a degree of adaptability and implementational capability far beyond those without it. NLU is able to perpetually monitor for medical and governmental updates, guidelines, or breakthroughs, deem their merit or applicability, and subsequently make necessary data updates to ensure that doctors and nurses are providing their patients with the most informed, up-to-date care. Data alterations that once would have had to be manually integrated are done so in a fraction of the time by NLU systems.
In an age driven by data, where rapid change is the rule rather than the exception, it makes eminent sense to have language intelligence technologies capable of answering the ever-growing sets of challenges organizations and institutions face. NLU is one such technology.
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