Overview of Generative AI and Data Science Machine Learning
- Unveiling the Realm of AI Technologies: A Glimpse into the Augmented Future
- Generative AI and Language Models
- LLMs and Their Applications
- LLMs and Customer Support
- Development, Optimization, Localization, and Personalization Based on LLMs
- Unveiling the Power of Clustering and Topic Modeling
- Enhancing Customer Support Through Hybrid AI: LLMs Meet Clustering and Topic Modeling
- Endnotes
This chapter is from the book
This chapter is from the book
This chapter is from the book
You may grow old and trembling in your anatomies, you may lie awake at night listening to the disorder of your veins, you may miss your only love, you may see the world about you devastated by evil lunatics, or know your honour trampled in—to learn. Learn why the world wags and what wags it. That is the only thing which the mind can never exhaust, never alienate, never be tortured by, never fear or distrust, and never dream of regretting. Learning is the only thing for you. Look what a lot of things there are to learn.
—T.H. White
Welcome to the most technical chapter of our journey into the AI Revolution. Unlike the other chapters in this book, this chapter is a deep dive into the history and details of the data science technology that powers today’s AI Revolution in customer service and support. In this chapter, we explore concepts such as generative AI, machine learning, various language models, reinforcement machine learning, and prompt engineering—among other topics—in detail. This chapter goes into the technical details of the foundation upon which the AI that is changing customer support is built.
However—and this is important—you don’t have to read this chapter to get the most out of the following chapters. You may have technical colleagues who know or will learn about the topics in this chapter, or you may have partners or vendors to help you build and deploy AI solutions. You don’t have to know this level of detail to move forward in leading your organization through the deployment of AI. We recognize that not everyone will feel comfortable navigating this more complex territory, and that’s okay—it won’t matter for the rest of the book.
However, we felt that we would be remiss if we didn’t cover this technology in some detail as a foundational component of this book. This chapter will not supplant the myriad of courses, papers, books, theories, algorithms, and other details in this fast-moving technology. This chapter is worth a skim to understand the underlying developments driving the AI Revolution and what to explore in more detail if you are interested.
If you’re a customer service and support professional eager to leverage AI in your organization but less versed in technical jargon—please know this chapter is not a prerequisite for the valuable insights and steps outlined in the other chapters in this book. It’s here to provide a deeper understanding for those who wish to explore further. Skipping it won’t diminish your ability to apply AI effectively within your role.
The rest of the book is designed with you in mind, focusing on practical applications, deployment strategies, and real-world scenarios that don’t require a deep technical background to understand. However, if you want to be the leader who queries your team or a vendor on their understanding and application of reinforcement learning from human feedback (RLHF), you might want to investigate to understand more.
Whether you decide to brave this chapter or flip past it and go directly to the next, rest assured that this chapter is not required reading to enable a successful AI deployment! Happy reading, wherever you land next.
Unveiling the Realm of AI Technologies: A Glimpse into the Augmented Future
In 1947, Alan Turing gave a public lecture about computer intelligence—the original concept of artificial intelligence. In 1950, he proposed the Turing test, a criterion for machine intelligence based on natural language conversation. In 1956, John McCarthy coined the term artificial intelligence and organized the first conference on the topic at Dartmouth College.
In the 1970s and 1980s, AI research focused on developing rule-based systems that could encode human knowledge and reasoning in specific domains, such as medicine, engineering, and finance. These systems, known as expert systems, could perform tasks requiring human expertise, such as diagnosis, planning, and decision-making.
In the late 1980s and 1990s, the development of AI underwent a paradigm shift from relying on predefined rules to learning from data, enabling machines to achieve higher levels of intelligence and performance. Machine learning (ML) is a subfield of AI that enables machines to learn from data and improve their performance without explicit programming. Machine learning techniques include supervised, unsupervised, and reinforcement learning, which can be applied to various problems, such as classification, clustering, regression, and control.
In the 2000s and 2010s, AI experienced a major breakthrough with the advent of deep learning, a subset of machine learning that uses multiple layers of artificial neural networks to learn from large amounts of data. 2015 was a big year in AI history; a five-game Go match was hosted between the European champion Fan Hui and AlphaGo, a computer Go program developed by DeepMind. AlphaGo won all five games. Deep learning has enabled significant advances in various domains, such as computer vision, natural language processing (NLP), speech recognition, and robotics. Some notable deep learning models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models, such as BERT (Bidirectional Encoder Representations from Transformers)1 and GPT (Generative Pre-trained Transformer).2
In 2017, Google developed the transformer model and published a paper, “Attention Is All You Need.”3 Transformers opened a new chapter for the natural language processing field. Since then, companies and researchers worldwide have built large-scale language models based on the transformer architecture.
In the 2020s and beyond, AI is entering a new frontier of generative technologies, which aim to create novel and realistic content, such as images, texts, sounds, and videos. Generative technologies use deep learning models, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models (LLMs), to generate content that is indistinguishable from human-produced content. Generative technologies have various applications, such as art, entertainment, education, and communication.