Public Master Outline: Public Master Outline.docx.
Below is an outline for a key resource to learn how to think about and use LLMs and related AI tools. It serves as a taste of what this resource represents.
When I first learned about Ethan Mollick, he described creative and impressive ways to use LLMs and AI tools. Moving forward, this will be a key source shaping the philosophical foundations of my use and understanding of AI.
You can purchase the book at: https://www.amazon.com/Co-Intelligence-Living-Working-Ethan-Mollick/dp/059371671X
Here is the summary from Amazon:
From Wharton professor and author of the popular One Useful Thing Substack newsletter Ethan Mollick comes the definitive playbook for working, learning, and living in the new age of AI
Something new entered our world in November 2022 — the first general purpose AI that could pass for a human and do the kinds of creative, innovative work that only humans could do previously. Wharton professor Ethan Mollick immediately understood what ChatGPT meant: after millions of years on our own, humans had developed a kind of co-intelligence that could augment, or even replace, human thinking. Through his writing, speaking, and teaching, Mollick has become one of the most prominent and provocative explainers of AI, focusing on the practical aspects of how these new tools for thought can transform our world.
In Co-Intelligence, Mollick urges us to engage with AI as co-worker, co-teacher, and coach. He assesses its profound impact on business and education, using dozens of real-time examples of AI in action. Co-Intelligence shows what it means to think and work together with smart machines, and why it's imperative that we master that skill.
Mollick challenges us to utilize AI's enormous power without losing our identity, to learn from it without being misled, and to harness its gifts to create a better human future. Wide ranging, hugely thought-provoking, optimistic, and lucid, Co-Intelligence reveals the promise and power of this new era.
Co-Intelligence by Ethan Mollick - Chapter 1 Outline
Chapter 1: CREATING ALIEN MINDS
Main Arguments
Defining AI and Its Evolution:
AI has various meanings and has evolved significantly over time. Mollick emphasizes the difference between AI's portrayal in popular culture (like The Terminator) and its real-world applications.
Quote: "Talking about AI can be confusing, in part because AI has meant so many different things and they all tend to get muddled together." (p. 17)
Historical Fascination with Intelligent Machines:
The fascination with machines that can mimic human thought dates back centuries, exemplified by the Mechanical Turk in the 18th century.
Quote: "We’ve long had a fascination with machines that can think... The machine, also known as the Mechanical Turk, beat Ben Franklin and Napoleon in chess matches and led Edgar Allan Poe to speculate on the possibility of artificial intelligence upon seeing it in the 1830s." (p. 18)
Theoretical Foundations:
Claude Shannon’s Theseus mouse and Alan Turing’s imitation game laid foundational theories for AI.
Quote: "The thought experiment was the imitation game, where computer pioneer Alan Turing first laid out the theories about how a machine could develop a level of functionality sufficient to mimic a person." (p. 19)
Early AI Boom and Bust Cycles:
The initial rapid progress in AI research led to high expectations, but unmet promises resulted in periods of disillusionment known as AI winters.
Quote: "Progress was initially rapid as computers were programmed to solve logic problems and play checkers... But hype cycles have always plagued AI, and as these promises went unfulfilled, disillusionment set in, one of many 'AI winters'." (p. 20)
Machine Learning and Supervised Learning:
The 2010s marked a significant shift to machine learning techniques for data analysis and prediction, primarily using supervised learning.
Quote: "The latest AI boom started in the 2010s with the promise of using machine learning techniques for data analysis and prediction." (p. 21)
Predictive AI in Industry:
Companies like Amazon utilized predictive AI for demand forecasting, logistics optimization, and enhancing operational efficiency.
Quote: "These predictive AI technologies may have found their ultimate expression at the retail giant Amazon, which deeply embraced this form of AI in the 2010s." (p. 22)
Limitations of Early AI Systems:
Early AI systems struggled with "unknown unknowns" and data they had not encountered before, limiting their adaptability.
Quote: "However, these types of AI systems were not without limitations... they struggled with predicting 'unknown unknowns,' or situations that humans intuitively understand but machines do not." (p. 23)
Transformers and Attention Mechanisms:
The 2017 paper "Attention Is All You Need" introduced the Transformer architecture, revolutionizing how AI processes human language.
Quote: "This paper proposed a new architecture, called the Transformer, that could be used to help a computer better process how humans communicate." (p. 24)
Large Language Models (LLMs):
LLMs like GPT-3 and GPT-4 represent a new era of AI, capable of generating coherent and contextually appropriate text through token prediction.
Quote: "These new types of AI, called Large Language Models (LLMs), are still doing prediction, but rather than predicting the demand for an Amazon order, they are analyzing a piece of text and predicting the next token." (p. 26)