Introduction to Generative AI by Numa Dhamani and Maggie Engler

In a world where AI is no longer a distant concept but an integral part of our lives, understanding the nuances of generative AI models has become essential. Whether you’ve marveled at ChatGPT’s witty responses or witnessed DALL-E’s ability to create surreal art, you’ve probably already brushed against the transformative power of these technologies. Introduction to Generative AI, co-authored by Numa Dhamani and Maggie Engler, is your compass in navigating this complex terrain.


Out Now Only On Manning.com


 

Who is this Book For?

Introduction to Generative AI speaks to a diverse audience intrigued by the complexities of AI and its generative models. Whether you’re an AI enthusiast eager to explore the technology’s capabilities, a tech professional aiming to delve deeper into generative AI’s intricacies, or a student/educator seeking to bridge theory and application, this book offers valuable insights.

While a basic understanding of machine learning and natural language processing can enhance your experience, the book is designed to be accessible to those without a technical background.

By engaging with Introduction to Generative AI, you’ll:

  • Develop a profound understanding of how large language models (LLMs) function and how to harness their potential.
  • Grasp the ethical and societal considerations tied to generative AI, enabling you to contribute to informed discussions.
  • Gain insights into both the opportunities and challenges of LLMs, empowering you to make informed choices in various contexts.

This book serves as a comprehensive guide, enriching your understanding of generative AI regardless of your prior knowledge, making it an essential read for anyone eager to navigate this evolving technological landscape.

Let’s take a snapshot look at some of the introductory lessons found within Introduction to Generative AI.

 


Join our newsletter to stay up-to-date on new releases and Deals of the Day!


 

Limitations and Risks of Large Language Models: Unveiling the Complexities

Large Language Models (LLMs) have emerged as remarkable tools, capable of achieving unprecedented success across a multitude of tasks. However, this success is accompanied by significant risks and limitations that demand close scrutiny. In this exploration, we delve into the intricacies of these challenges, ranging from biases inherent in training data to the unpredictability of LLM outputs and the ecological footprint of their energy consumption.

 

Limitations in Training Data and Bias Issues:

At the core of LLM development lies the colossal amount of text data on which these models are trained. To ensure the generation of natural-looking language, copious volumes of human-written content are essential. While sources like Wikipedia and Google Books offer high-quality data, the inclusion of less moderated content, such as from social media sites like Reddit, poses a dilemma. Although these sources enhance the model’s understanding of various tokens, they also introduce the risk of objectionable speech and biases. The model might inadvertently reproduce offensive language, misinformation, or extremist ideologies, reflecting the patterns present in the training data. Moreover, subtler forms of bias can infiltrate LLMs, mirroring societal inequalities.

 

Limitations in Controlling Machine Outputs:

The fluency with which LLMs generate text conceals a fundamental limitation: they lack true comprehension of the content. This drawback becomes evident in the form of “hallucinations” — instances where LLMs assert false or misleading information. These errors can arise from training data inaccuracies or contexts outside the model’s known sequences. The challenge is exacerbated by the vast number of possible responses generated by the model, with only a fraction being accurate. Addressing hallucinations poses a significant hurdle, as their reduction requires intricate strategies that balance generative capabilities with factual correctness.

 

Sustainability of Large Language Models:

The name “Large Language Models” accurately reflects their substantial size and resource consumption. Training these models involves massive datasets, hundreds of billions of parameters, and significant computing power. Training LLMs on specialized chips like GPUs or TPUs requires renting vast computing resources, leading to substantial financial investments. Beyond monetary costs, the environmental impact is a concern, with estimates suggesting that the training of models like GPT-3 emits substantial carbon dioxide. The accessibility of these resources also poses challenges, potentially leaving smaller players at a disadvantage compared to multinational corporations. Efforts to make LLMs more accessible and energy-efficient are ongoing but untested.

 


Understanding the limitations and risks inherent in Large Language Models is paramount for their responsible and ethical deployment. By acknowledging biases introduced by training data, grappling with the challenge of controlling machine outputs, and addressing the environmental implications, we pave the way toward a more informed and cautious integration of LLMs into our technological landscape. Through rigorous exploration and proactive solutions, we can harness the potential of LLMs while mitigating their inherent drawbacks.

In this article, we’ve explored some of the lessons found within Introduction to Generative AI, and we’ve focused on what to watch our for. Now go check out the book to learn the rest—What major LLMs are available, how to use them effectively, and more.