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AI and Natural Language Processing Applications

“Can machines think?” –that’s how Turing began his famous article on Computing Machinery and Intelligence in 1950. For Turing, this was a tough question that should be reformulated to something like  “Can machines understand and generate natural language, making them indistinguishable from humans in a conversation?”. For Turing, artificial intelligence and natural language go hand to hand.

We understand natural language as the language humans use to communicate with each other, both written and spoken. Society and technology have advanced a good deal since Turing's article back in the 1950 ’s and computers have progressed incredibly and impacted many fields. Artificial Intelligence and natural language processing (NLP) being two of them. Back in the day, it seemed almost movie-like that a machine could understand the natural language but, even when they are still very far from perfection, computers have reached a level that allows applying NLP in several situations. 

As Natural Language Processing is still being researched and tested, the future applications and challenges are coming to light. Let's review some of them. 


Applications of AI & Natural Language 

Computer interfaces: If we get machines to “speak” our language, the first and most obvious application is to communicate with computers using this language. Probably the most famous example is Siri, Apple’s virtual assistant. Siri was launched as an app in 2010, and in November 2011, it became an integral part of the iPhone 4S. Currently, an improved version of Siri runs in all Apple devices. We're also seeing the popularity of Amazon's Alexa into this application. These interfaces are able to understand our language, translate it to text, sift through their data and follow actions. A great first step to the future of the Internet of Things (which is the next topic!).

Internet of Things (IoT): The power of speech recognition enables smart devices to translate our commands and make IoT products feasible. Machine translation of Natural Language allows us to turn the light on, watch our security cameras and control our room temperature. 

Text translation: The beginnings of NLP are closely related to text translations. As early as in the 1940’s, there was already a machine capable of translating a few sentences from Russian to English. More than 70 years later, there are still many problems with automatic text translations, due to the complexity of natural languages. However, nowadays there are several apps and online programs available that translate from and to almost every language with increasing accuracy.

Chatbots: Chatbots are software applications that can simulate a conversation with a person using a messaging app and it is one of the most advanced forms of interaction between humans and machines. They apply Natural Language Processing (NLP) and are able to answer questions and even solve issues. It is being used for customer service in many businesses. 


Web crawlers & search engines: Web crawlers are scripts that automatically browse the Internet looking for data. Search engines such as Google, use these bots to find new and updated pages, index them, and show them to their users in order of relevance. Back in the day, the first search engines used to look only for the keywords that appeared in each page, today, they employ better NLP systems allowing them to get more accurate results. Search is now available based not in how many times a word repeats in a given page and other similar metrics, but also trying to understand what’s the context and the relevance of that text.

Data analysis: Only a small amount of all the data is available in structured formats, i.e. databases or spreadsheets. Most of the data we generate each day are text, words and natural language. This means that analytic systems that can't understand natural language will miss the biggest part of what they should be analyzed –which is as much as 80%. With the introduction of NLP in big data processing, the possibilities for improved analysis are increased. 

 

Many of these applications are some we've already experienced in our regular life and it's pretty incredible still for some of us how we can use our voice to control so many things. 

However, there still some downfalls or roadblocks in these advancements. The possibility for AI to help machines process natural language comes with challenges, some of which include context irregularities, ambiguity, voice inflection and the inability of recognizing non-verbal queues. 

As technology continues to evolve, the future of NLP will probably move towards jumping the current barriers and taking big leaps in better understanding human communication in general. We'll have to watch closely at what applications will come. 
 

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