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July Edition: Hey Siri, What Do I Mean?

Natural Language Processing and the Future of AI

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Photo by Elina Krima from Pexels
Photo by Elina Krima from Pexels

By now, many of us have interacted with a device’s built-in assistant. Possibly on purpose or even by saying "Are you serious?" too close to an iPhone. Whether it’s Alexa, Siri, or whomever lives inside your preferred smart device; it’s using natural language processing (NLP) in conjunction with speech recognition to understand you, give you appropriate answers to your questions, and sometimes even learn. But what exactly is NLP? Quite simply, it’s a machine trying to parse, construct, and summarize text to speak or write like a human "naturally" would. NLP offers a practical use for artificial intelligence that many people utilize regularly.

So, how does it work? The key to NLP’s success lies within deep and recurrent neural networks. Deep neural networks (DNN) and recurrent neural networks (RNNs) are trained using tens of thousands of words and sentences to most accurately predict responses when tested on phrases that it has yet to see. You can read more about deep neural networks here. As with many Machine Learning algorithms, DNNs and RNNs have their flaws when used for predictions. For example, when you meant to type "I will have a bagel with cream cheese," your phone may have suggested that you’d like your bagel with cream flies or something similar to the keystrokes for cheese. As NLP improves, so will those predictions.

Regardless of whether you’re using a smart assistant, an automated phone menu, or just trying to type a message, NLP is used in many of the things we do every day.

Want to learn more about natural language processing? Here are some of our most popular articles on the subject!

Sophie Mann, Editorial Associate


A Practitioner’s Guide to Natural Language Processing (Part I) – Processing & Understanding Text

By Dipanjan (DJ) Sarkar – 31 min read

Proven and tested hands-on strategies to tackle NLP tasks


How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning

By Hamel Husain & Ho-Hsiang Wu – 13 min read

An end-to-end example of how to build a system that can search objects semantically.


Natural Language Processing for Fuzzy String Matching with Python

By Susan Li – 5 min read

When we compare hotel room price between different websites, we must make sure we are comparing apples to apples


Introduction to Natural Language Processing for Text

By Ventsislav Yordanov – 16 min read

After reading this blog post, you’ll know some basic techniques to extract features from some text, so you can use these features as input for machine learning models.


Your Guide to Natural Language Processing (NLP)

By Diego Lopez Yse – 13 min read

How machines process and understand human language


Natural Language Processing: From Basics to using RNN and LSTM

By vibhor nigam – 11 min read

A detailed introduction to all the concepts prevalent in the world of Natural Language Processing


Drake – Using Natural Language Processing to understand his lyrics

By Brandon Punturo – 8 min read

We know for a fact that Drake’s work is popular but why are the majority of his songs such a hit? Is it the production? Is it the marketing?


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We also thank all the great new writers who joined us recently K. Delphino, Divyanshu Marwah, Luke Vassor, Inneke Mayachita, Daniel Chen, Scott Rogowski, Do Lee, Gijs van den Dool, Mirko Savasta, Claire Salling, Thiago Carvalho, Todd Cook, Alex Shoop, Paul Stubley, Victoria Cheung, Maciej D. Korzec, Karan Praharaj, Apurva Joshi, Elod Pal Csirmaz, Ian Ho, Ugur Ertas, John McAllister, and many others. We invite you to take a look at their profiles and check out their work.


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