Historical Context and Evolution of Language Models (LMs)

There are several misconceptions surrounding LMs, notably the belief that they were invented by OpenAI. However, the idea of LMs is not just a few years old, it actually spans several decades. As illustrated in figure 1.2, the concept behind some LMs is quite intuitive: given an input sequence, the task of the model is to predict the next token:

To truly appreciate the sophistication of modern LMs, it's essential to explore the historical evolution and the diverse range of disciplines from which they draw inspiration, all the way up to the recent transformative developments we are currently witnessing.

Evolutions of LLMs Architectures

The development of language model architectures has undergone a transformative journey as shown in Figure 1.5, tracing its origins from simple word embeddings to sophisticated models capable of understanding and generating multimodal content. This progression is elegantly depicted in figure X about the "LLM Evolutionary Tree" that starts from foundational models before 2018, such as FastText, GloVe, and Word2Vec, and extends to the latest advancements like the LLaMA series and Google's Bard.

Let's look at this evolution in a bit more detail:

GPT Assistant Training Recipe

Before diving into the specifics of how GPT assistants like ChatGPT are developed, it's essential to understand the foundational elements and methodologies involved in training these advanced language models. The process includes several stages, each contributing to the model's ability to comprehend and generate human-like text.

The diagram in figure 1.7 illustrates the standard training recipe used to develop a GPT assistant, such as ChatGPT. This process is divided into four distinct stages, each crucial for evolving a basic neural network into an advanced AI capable of understanding and generating profound and convincing human-like text.

Let's start with the first and most computationally intensive stage which is for building the base model from internet scale data.

Decoding the Realities and Myths of LLMs

LLMs like OpenAI's GPT series have sparked widespread intrigue and debate across the tech world and beyond. While they are often seen as groundbreaking advancements, there are numerous misconceptions and exaggerated claims surrounding their capabilities and origins. This section aims to clarify these misunderstandings by exploring the historical development of LLMs, addressing common myths, and examining their real-world applications and limitations.

From their early statistical underpinnings to the sophisticated neural networks, we see today, as you've seen earlier in this chapter, the evolution of language models has been a collaborative and incremental process, contrary to the notion that they suddenly emerged from a single innovator or institution. Additionally, we will discuss the critical insights of Ada Lovelace, which remain profoundly relevant in understanding the fundamental nature of these models, as well as the limitations...

Objective-Driven AI

The concept of objective-driven AI, depicted in figure 1.14, proposed by AI pioneer Yann LeCun, represents a potential pathway towards more sophisticated forms of artificial intelligence, potentially leading to Artificial General Intelligence (AGI). This approach focuses on designing AI systems that can learn and plan to achieve specific objectives in complex environments, moving beyond mere pattern recognition to incorporate elements of reasoning, planning, and decision-making.

LeCun argues that for AI to reach the level of general intelligence, it must have the ability to learn models of the world that allow it to predict and manipulate its environment. This would involve not just responding to inputs based on learned data but actively seeking information and learning causality, thus developing a more profound, actionable understanding of its surroundings.

Human-Technology Augmentation

Historically, the development of technology has been driven by the desire to augment human capabilities as shown in figure 1.15, reduce labor, and solve complex problems. From the invention of the wheel to the creation of the internet, technological advancements have aimed to extend the physical and cognitive reach of humanity.

In the context of AI and LLMs, a primary goal for many developers is to augment human abilities rather than replace them (irrespective of the doom and gloom often presented in the media or by policymakers). AI systems are increasingly used to enhance decision-making processes, automate routine tasks, and provide insights that are beyond the scope of human capability due to data volume or complexity.

This section addressed common misconceptions and realities about LLMs, particularly how some policymakers use the purported existential risks of AI and the notion of AI taking over as distractions...

Summary

In this chapter, we've embarked on an exploration of LLMs, diving into their historical background, current capabilities, and the common misconceptions that surround these powerful tools. This journey through the development of LLMs not only highlights the technological breakthroughs that have shaped these models but also points toward future advancements and the challenges that lie ahead.

LLMs use an auto-regressive method to predict the next word in a sequence by considering previous words, but this approach has limitations. For instance, the likelihood of errors increases as the sequence lengthens because each prediction carries a chance of error that accumulates over time. Despite their impressive fluency, LLMs cannot truly plan or understand context as humans do, often producing responses that are a mere recombination of learned data without real insight. This is due to their training being limited to existing text, which prevents them from generating novel content or...