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Chapter 1: NLP in the Business Context and Introduction to AWS AI Services
Natural language processing, or NLP, is quite popular in the scientific community, but the value of using this Artificial Intelligence (AI) technique to gain business benefits is not immediately obvious to mainstream users. Our focus will be to raise awareness and educate you on the business context of NLP, provide examples of the proliferation of data in unstructured text, and show how NLP can help derive meaningful insights to inform strategic decisions within an enterprise.
In this introductory chapter, we will be establishing the basic context to familiarize you with some of the underlying concepts of AI and Machine Learning (ML), the types of challenges that NLP can help solve, common pitfalls when building NLP solutions, and how NLP works and what it's really good at doing, with examples.
In this chapter, we will cover the following:
- Introducing NLP
- Overcoming the challenges in building NLP solutions
- Understanding why NLP is becoming mainstream
- Introducing the AWS ML stack
Introducing NLP
Language is as old as civilization itself and no other communication tool is as effective as the spoken or written word. In their childhood days, the authors were enamored with The Arabian Nights, a centuries-old collection of stories from India, Persia, and Arabia. In one famous story, Ali Baba and the Forty Thieves, Ali Baba is a poor man who discovers a thieves' den containing hordes of treasure hidden in a cave that can only be opened by saying the magic words open sesame. In the authors' experience, this was the first recollection of a voice-activated application. Though purely a work of fiction, it was indeed an inspiration to explore the art of the possible.
Recently, in the last two decades, the popularity of the internet and the proliferation of smart devices has fueled significant technological advancements in digital communications. In parallel, the long-running research to develop AI made rapid strides with the advent of ML. Arthur Lee Samuel was the first to coin the term machine learning, in 1959, and helped make it mainstream in the field of computer science by creating a checkers playing program that demonstrated how computers can be taught.
The concept that machines can be taught to mimic human cognition, though, was popularized a little earlier in 1950 by Alan Turing in his paper Computing Machinery and Intelligence. This paper introduced the Turing Test, a variation of a common party game of the time. The purpose of the test was for an interpreter to ask questions and compare responses from a human participant and a computer. The trick was that the interpreter was not aware which was which, considering all three were isolated in different rooms. If the interpreter was unable to differentiate the two participants because the responses matched closely, the Turing Test had successfully validated that the computer possessed AI.
Of course, the field of AI has progressed leaps and bounds since then, largely due to the success of ML algorithms in solving real-world problems. An algorithm, at its simplest, is a programmatic function that converts inputs to outputs based on conditions. In contradiction to regular programmable algorithms, ML algorithms have learned the ability to alter their processing based on the data they encounter. There are different ML algorithms to choose from based on requirements, for example, Extreme Gradient Boosting (XGBoost), a popular algorithm for regression and classification problems, Exponential Smoothing (ETS), for statistical time series forecasting, Single Shot MultiBox Detector (SSD), for computer vision problems, and Latent Dirichlet Allocation (LDA), for topic modeling in NLP problems.
For more complex problems, ML has evolved into deep learning with the introduction of Artificial Neural Networks (ANNs), which have the ability to solve highly challenging tasks by learning from massive volumes of data. For example, AWS DeepComposer (https://aws.amazon.com/deepcomposer/), an ML service from Amazon Web Services (AWS), educates developers with music as a medium of instruction. One of the ML models that DeepComposer uses is trained with a type of neural network called the Convolutional Neural Network (CNN) to create new and unique musical compositions from a simple input melody using AutoRegressive (AR) techniques:
Figure 1.1 – Composing music with AWS DeepComposer and ML
A piano roll is an image representation of music, and AR-CNN considers music generation as a sequence of these piano roll images:
Figure 1.2 – Piano roll representation of music
While there is broad adoption of ML across organizations of all sizes and industries spurred by the democratization of advanced technologies, the potential to solve many types of problems, and the breadth and depth of capabilities in AWS, ML is only a subset of what is possible today with AI. According to one report (https://www.gartner.com/en/newsroom/press-releases/2019-01-21-gartner-survey-shows-37-percent-of-organizations-have, accessed on March 23, 2021), AI adoption grew by 270% in the period 2015 to 2019. And it is continuing to grow at a rapid pace. AI is no longer a peripheral technology only available to those enterprises that have the economic resources to afford high-performance computers. Today, AI is a mainstream option for organizations looking to add cognitive intelligence to their applications to accelerate business value. For example, ExxonMobil in partnership with Amazon created an innovative and efficient way for customers to pay at gas stations. The Alexa pay for gas skill uses the car's Alexa-enabled device or your smartphone's Alexa app to communicate with the gas pump to manage the payment. The authors paid a visit to a local ExxonMobil gas station to try it out, and it was an awesome experience. For more details, please refer to https://www.exxon.com/en/amazon-alexa-pay-for-gas.
AI addresses a broad spectrum of tasks similar to human intelligence, both sensory and cognitive. Typically, these are grouped into categories, for example, computer vision (mimics human vision), NLP (mimics human speech, writing, and auditory processes), conversational interfaces (such as chatbots, mimics dialogue-based interactions), and personalization (mimics human intuition). For example, C-SPAN, a broadcaster that reports on proceedings at the US Senate and the House of Representatives, uses Amazon Rekognition (a computer vision-based image and video analysis service) to tag who is speaking/on camera at each time. With Amazon Rekognition, C-SPAN was able to index twice as much content compared to what they were doing previously. In addition, AWS offers AI services for intelligent search, forecasting, fraud detection, anomaly detection, predictive maintenance, and much more, which is why AWS was named the leader in the first Gartner Magic Quadrant for Cloud AI.
While language is inherently structured and well defined, the usage or interpretation of language is subjective, and may inadvertently cause an unintended influence that you need to be cognizant of when building natural language solutions. Consider, for example, the Telephone Game, which shows how conversations are involuntarily embellished, resulting in an entirely different version compared to how it began. Each participant repeats exactly what they think they heard, but not what they actually heard. It is fun when played as a party game but may have more serious repercussions in real life. Computers, too, will repeat what they heard, based on how their underlying ML model interprets language.
To understand how small incremental changes can completely change the meaning, let's look at another popular game: Word Ladder (https://en.wikipedia.org/wiki/Word_ladder). The objective is to convert one word into a different word, often one with the opposite meaning, in as few steps as possible with only one letter in the word changing in one step.
An example is illustrated in the following table:
Figure 1.3 – The Word Ladder game
Adapting AI to work with natural language resulted in a group of capabilities that primarily deal with computational emulation of cognitive and sensory processes associated with human speech and text. There are two main categories that applications can be grouped into:
- Natural Language Understanding (NLU), for voice-based applications such as Amazon Alexa, and speech-to-text/text-to-speech conversions
- NLP, for the interpretation of context-based insights from text
With NLU, applications that hitherto needed multiple and sometimes cumbersome interfaces, such as a screen, keyboard, and a mouse to enable computer-to-human interactions, can work as efficiently with just voice.
In Stanley Kubrick's 1968 movie 2001: A Space Odyssey, (spoiler alert!!) an artificially intelligent computer known as the HAL 9000 uses vision and voice to interact with the humans on board, and in the course of the movie, develops a personality, does not accept when it is in error, and attempts to kill the humans when it discovers their plot to shut it down. Fast forward to now, 20 years after the future depicted in the movie, and we have made significant progress in language understanding and processing, but not to the extreme extent of the dramatization of the plot elements used in the movie, thankfully.
Now that we have a good understanding of the context in which NLP has developed and how it can be used, let's try examining some of the common challenges you might face while developing NLP solutions.
Overcoming the challenges in building NLP solutions
We read earlier that the main difference between the algorithms used for regular programming and those used for ML is the ability of ML algorithms to modify their processing based on the input data fed to them. In the NLP context, as in other areas of ML, these differences add significant value and accelerate enterprise business outcomes. Consider, for example, a book publishing organization that needs to create an intelligent search capability displaying book recommendations to users based on topics of interest they enter.
In a traditional world, you would need multiple teams to go through the entire book collection, read books individually, identify keywords, phrases, topics, and other relevant information, create an index to associate book titles, authors, and genres to these keywords, and link this with the search capability. This is a massive effort that takes months or years to set up based on the size of the collection, the number of people, and their skill levels, and the accuracy of the index is prone to human error. As books are updated to newer editions, and new books are added or removed, this effort would have to be repeated incrementally. This is also a significant cost and time investment that may deter many unless that time and those resources have already been budgeted for.
To bring in a semblance of automation in our previous example, we need the ability to digitize text from documents. However, this is not the only requirement, as we are interested in deriving context-based insights from the books to power a recommendations index for a reader. And if we are talking about, for example, a publishing house such as Packt, with 7,500+ books in its collection, we need a solution that not only scales to process large numbers of pages, but also understands relationships in text, and provides interpretations based on semantics, grammar, word tokenization, and language to create smart indexes. We will cover a detailed walkthrough of this solution, along with code samples and demo videos, in Chapter 5, Creating NLP Search.
Today's enterprises are grappling with leveraging meaningful insights from their data primarily due to the pace at which it is growing. Until a decade or so, most organizations used relational databases for all their data management needs, and some still do even today. This was fine because the data volume need was in single-digit terabytes or less. In the last few years, the technology landscape has witnessed a significant upheaval with smartphones becoming ubiquitous, the large-scale proliferation of connected devices (in the billions), the ability to dynamically scale infrastructure in size and into new geographies, and storage and compute costs becoming cheaper due to the democratization offered by the cloud. All of this means applications get used more often, have much larger user bases, more processing power, and capabilities, can accelerate their pace of innovation with faster go-to-market cycles, and as a result, have a need to store and manage petabytes of data. This, coupled with application users demanding faster response times and higher throughput, has put a strain on the performance of relational databases, fueling a move toward purpose-built databases such as Amazon DynamoDB, a key-value and document database that delivers single-digit millisecond latency at any scale.
While this move signals a positive trend, what is more interesting is how enterprises utilize this data to gain strategic insights. After all, data is only as useful as the information we can glean from it. We see many organizations, while accepting the benefits of purpose-built tools, implementing these changes in silos. So, there are varying levels of maturity in properly harnessing the advantages of data. Some departments use an S3 data lake (https://aws.amazon.com/products/storage/data-lake-storage/) to source data from disparate sources and run ML to derive context-based insights, others are consolidating their data in purpose-built databases, while the rest are still using relational databases for all their needs.
You can see a basic explanation of the main components of a data lake in the following Figure 1.5, An example of an Amazon S3 data lake:
Figure 1.4 – An example of an Amazon S3 data lake
Let's see how NLP can continue to add business value in this situation by referring back to our book publishing example. Suppose we successfully built our smart indexing solution, and now we need to update it with book reviews received via Twitter feeds. The searchable index should provide book recommendations based on review sentiment (for example, don't recommend a book if reviews are negative > 50% in the last 3 months). Traditionally, business insights are generated by running a suite of reports on behemoth data warehouses that collect, mine, and organize data into marts and dimensions. A tweet may not even be under consideration as a data source. These days, things have changed and mining social media data is an important aspect of generating insights. Setting up business rules to examine every tweet is a time-consuming and compute-intensive task. Furthermore, since a tweet is unstructured text, a slight change in semantics may impact the effectiveness of the solution.
Now, if you consider model training, the infrastructure required to build accurate NLP models typically uses the deep learning architecture called Transformers (please see https://www.packtpub.com/product/transformers-for-natural-language-processing/9781800565791) that use sequence-to-sequence processing without needing to process the tokens in order, resulting in a higher degree of parallelization. Transformer model families use billions of parameters with the training architecture using clusters of instances for distributed learning, which adds to time and costs.
AWS offers AI services that allow you, with just a few lines of code, to add NLP to your applications for the sentiment analysis of unstructured text at an almost limitless scale and immediately take advantage of the immense potential waiting to be discovered in unstructured text. We will cover AWS AI services in more detail from Chapter 2, Introducing Amazon Textract, onward.
In this section, we reviewed some challenges organizations encounter when building NLP solutions, such as complexities in digitizing paper-based text, understanding patterns from structured and unstructured data, and how resource-intensive these solutions can be. Let's now understand why NLP is an important mainstream technology for enterprises today.
