Summary

In this chapter, we introduced NLP by tracing the origins of AI, how it evolved over the last few decades, and how the application of AI became mainstream with the significant advances made with ML algorithms. We reviewed some examples of these algorithms, along with an example of how they can be used. We then pivoted to AI trends and saw how AI adoption grew exponentially over the last few years and has become a key technology in accelerating enterprise business value.

We read a cool example of how ExxonMobil uses Alexa at their gas stations and delved into how AI was created to mimic human cognition, and the broad categories of their applicability, such as text, speech, and vision. We saw how AI in natural language has two main areas of usage NLU for voice-based uses and NLP for deriving insights from text.

In analyzing how enterprises are building NLP models today, we reviewed some of the common challenges and how to mitigate them, such as digitizing paper-based text, collecting data from disparate sources, and understanding patterns in data, and how resource-intensive these solutions can be.

We then reviewed NLP industry trends and market segmentation and saw with an example how important NLP was and still continues to be during the pandemic. We dove deep into the philosophy of NLP and realized it was all about converting text to numerical representations and understanding the underlying patterns to decipher new meanings. We looked at an example of this pattern with how SALB could impact the global economy.

Finally, we reviewed the technology implications in setting up NLP training and the associated challenges. We reviewed the three layers of the AWS ML stack and introduced AWS AI services that provided pre-built models and ready-made intelligence.

In the next chapter, we will introduce Amazon Textract, a fully managed ML service that can read both printed and handwritten text from images and PDFs without having to train or build models and can be used without the need for ML skills. We will cover the features of Amazon Textract, what its functions are, what business challenges it was created to solve, what types of user requirements it can be applied to, and how easy it is to integrate Amazon Textract with other AWS services such as AWS Lambda for building business applications.