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Artificial Intelligence (AI) is the new buzzword in the minds of everyone. According to a study conducted by Mckinsey.com investments, AI could deliver businesses with $13 trillion in extra revenue by 2030. Needless to say, there is an increasing need for all professionals from every field to stay informed about AI and its potential. Since AI tools can already accomplish basic tasks without requiring human intelligence or interference, commercial tasks such as planning, recognizing financial patterns, problem solving and smooth communication are all being slowly deferred to AI tools. What is Machine Learning? Machine Learning (ML) is a subset of AI. ML enables machines to automatically collect, analyze and learn from vast amounts of information. These machines are hence able to constantly improve by learning from their past experiences without having to be explicitly programed at each step of the process. According to Business Insider, the streaming service provider Netflix was able to save $1 billion in 2016, thanks to its machine learning algorithm which eliminated the need for human involvement in the process of recommending TV shows and movies based on the watch history of their users. However, ML can be applied to solve more complicated real-world issues, thanks to NLP. What is NLP? Natural Language Processing (NLP) is a subset of machine learning. To understand how NLP works, we must first understand the purpose behind NLP. In this age of information, there is an endless amount of information accessible for commercial and governmental use on social media websites, news articles, etc. If we combine these with the data that businesses and governments officially collect, businesses and governments will be able to predict and solve various issues in a faster and more cost-effective manner. However, since machines cannot understand text, they aren’t able to gather anything of value from these large datasets. The solution to this problem is NLP. NLP is a form of computer programming that gives a machine the ability to understand, examine, manipulate, and possibly create human language. Considering its potential, NLP is still in its very early stages. Some basic examples of NLP being used to solve real-life problems include: Natural Language Generation: The process of generating text video data employed by companies like YouTube and EureScribe. Interpreting and answering audio questions: IBM’s Watson and Amazon’s Alexa are good examples. Auto-Correct and Grammar checking Auto-Prediction of Google search results Spam Filters: Gmail screens spam mails automatically using NLP algorithms Information Retrieval: Google uses NLP to retrieve Search Results from millions of sources How NLP and Machine Learning Work Together There are two approaches to NLP – rule-based and one that involves Machine Learning. Deciphering all the possible connotations of a rule-based approach is not enough. A permanent solution can only be achieved via Machine Learning. As mentioned in the examples above, ML is already being applied to Natural Language Processing algorithms and text analytic processes to better understand the meaning and context of text documents. Although a lot of progress has already been made in the field of NLP, there’s still a lot of room for improvement. The collaboration of these two fields is a form of “narrow” artificial intelligence. By combining the two, systems will soon be able to understand even more complicated text-based data such as social media comments, medical papers, legal, financial reports, survey responses and regulatory documents. Essentially, the role of ML in NLP is to make text analyzing software self-dependent. With further advancements, NLP programs will be able to improve and accelerate the process of converting unstructured text into operational data to gain useful insights that can be applied to solve real-world problems. Challenges Thwarting Progress in NLP Undoubtedly, converting unstructured text data into machine-readable data is a complex endeavor. Human languages are complex and multi-layered. The English language alone has over 13 million words. A significant percentage of these words are either related to each other or have different meanings in different situations (synonyms, idioms, etc.). For example, if a user enters - “lodges in Missouri” in a database, the NLP algorithm has to infer the various other meanings of the word ‘lodge’ and provide search results that include the words– motel, accommodation, hotels, etc. To have a definitive self-learning and self-dependent NLP solution, further research needs to go into teaching machines how human languages work. Machines need to be capable enough to infer certain fundamental aspects of grammar and syntactics. Based on this understanding, they can analyze specific patterns. ML helps NLP programs to gradually improve the machine’s ability to understand words as per the grammatical context they are presented in. Machines must first master the syntactical and semantical layers involved in human languages. Only then can they have a definitive systematic approach to understanding textual data without any risk of mistakes. A Bright Future The challenges of human language are what bring together the various subsets of AI together. By integrating processes such as ML and NLP, not only will we be able to receive better solutions for real-world problems, we will also be able to make progress in other fields such as Deep Learning. The NLP market is relatively new. At the moment it is mostly occupied by scores of startup companies. Until now, there aren’t many experienced enterprise software vendors offering high levels of NLP functionality or adaptability to help businesses find permanent solutions, the new decade is set to be defined by even more rapid progress.

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