The Future of Artificial Intelligence Rests on the Shoulders of Innovation in Natural Language Processing
The world of technology stands on a crucial precipice of change – a paradigm shift occurring through Artificial Intelligence applications in myriad fields. This begs asking a prime question that has been on the minds of several innovators in the tech industry: can a machine one day behave exactly like a human? And if this happens, could it mean achieving the pinnacle of machine learning capabilities or is there more to this picture?To answer these questions, we need to take a broad look at how Natural Language Processing (NLP), a branch of Artificial Intelligence, facilitates interactions between humans and machines. NLP is simply defined as an application of AI that enables machines to read, understand, and interpret human language. NLP is the bridge that mends the gap between humans and machines to facilitate interactions between one another with the help of computational linguistics.
History of Natural Language Processing in AI
The first proto-computer machines date back to the post-World War II era. By that time, Alan Turing had written an article, explaining theoretically the supposition about natural language generation and its automated interpretation. Punch cards were one of the first methods used to directly communicate with the earliest computers and understand their interpretation of data.
As technologies become more complex, language processing followed in the same direction. Tech scientists began to develop more complex language algorithms and techniques. At the beginning of this century, text translators, early versions of chatbots, and voice-to-text programs were key NLP technology applications that gathered pace. In recent times, we now have smart home systems, personal assistants, speech recognition, and real-time interactions with supercomputers that have changed language processing significantly. But one prime domain remains to be tackled completely that has just seen the light of innovations, which is unstructured data.
The Rise of Unstructured Data Changes Everything
We are all surrounded by tons and tons of data, most of it does not exist in recognizable structures. Added to this are the complexities and shortcomings associated with natural language such as recognizing multiple dialects, grammar, slang, and ambiguity in meaning. Before looking into the future of technologies such as AI, NLP, and other robotic processing automation activities, it is prudent to accept such inadequacies that exist in handling large amounts of data.
Digital workspaces are rising at a considerable pace, especially with growing consideration for remote working in post-pandemic times. Thus, data generation in different ways is expected to balloon up tremendously in this decade, calling out for robust language processing capacities in machines. Achieving wholesome human-like cognition abilities in machines to fulfill the dream of complete machine automation makes it necessary to take natural language processing in AI applications to the next phase. And an area that remains less explored is the processing of unstructured data.
Commercial organizations in today’s digital market require performing efficient indexing and searching in unstructured textual data. This is intended to help users to find required information through minimal effort. However, a change in document structures and data formats often occurs and these alterations might make it difficult to process natural language using AI techniques.
Natural Language Processing in AI to Develop Better Business Solutions
Tackling this quagmire does not seem much of an issue if the right natural language processing techniques are used to break down unstructured data into specific keywords and relatable domains. In this way, despite the lack of structure in datasets, such a particular NLP engine can recognize the intention with a high level of accuracy. This is certainly a breakthrough NLP technology that has recently seen increasing support and innovation in terms of commercial usage.
At Simplifai, we use this exact NLP application in their chief solution – the Digital Employee. Comprised of multiple proprietary products such as Simplifai Chatbot, Emailbot, and Documentbot, these Digital Employees use Natural Language Processing methods that can recognize the intention of data derived from users. These underpin utilizing such unstructured data-resolving capability of NLP in business verticals such as insurance, banking, financial organizations, e-commerce, and telecom. Such is the high rate of accuracy achieved by the Digital Employees, that it even has a very high chance of recognizing the sentiment of a user generating information.
Areas in Natural Language AI Applications that Need Exploring
Extracting useful data from unstructured text through next-gen NLP might have taken one more step towards better mimicking human intelligence through machines. While this is certainly not the pinnacle of machine learning as discussed before, it is indeed a strong leap towards machines thinking more independently and with more efficiency, thereby benefiting data-rich businesses. After all, if ever every machine passes the Turing test, that is when the zenith of NLP innovation could be achieved, which further might transform Artificial Intelligence into something more that is currently unknown to us.