Course Introduction

With the speed at which these tools are being adopted by industry and end users, being left behind is a growing fear for many. This course will provide a timeline of developments that have led to the creation of AI Chatbots such as ChatGPT, Google's Gemini, Microsoft's Co-pilot, and the number of open-source large Language models (LLMs).

This course is part of the Learning Prompt Crafting series. We will cover each development and provide a quick overview, explaining how each relates to using AI Chatbots and how we can leverage this when Prompt Crafting.

Lesson Objectives and Outcomes

• Stage for a journey through the history and technological advancements that have culminated in the development of Large Language Models (LLMs) and Chatbots like ChatGPT

• Apply the various advancements to creating more effective Prompt Crafting for ChatGPT and other AI tools

Course Structure

Each lesson is using, generally, the following format

• Listing of the Outcomes of the lesson

• Introduction to the topic

• Detailed overview of the topic

Introduction - Set the stage for our journey

The history of Artificial Intelligence takes us from the foundations of machine learning to the sophisticated building of today's chatbots and large language models (LLM).

In this Mini-Course, "The Evolution of AI: From Machine Learning to Advanced Chatbots," we review pivotal moments and groundbreaking technologies that have shaped the AI landscape as we know it today.

By looking into the past, we can appreciate the complexity and sophistication of the tools at our disposal, revealing AI's immense potential for our future.

The significance of AI development cannot be overstated. It influences every aspect of our digital lives and revolutionizes how we interact with technology. From the early days of machine learning in the 1950s to the neural network breakthroughs in the 1980s and onto the era of natural language processing and deep learning, each step has been instrumental in advancing our capabilities.

As we trace the evolution through the development of transformers and the introduction of Large Language Models like GPT-3, we arrive at the present, where chatbots and LLM models engage with us in natural, often indistinguishably human-like conversations.

Key Milestones In The Evolution Of AI Infographic

The term "artificial intelligence" was coined by Dartmouth College's John McCarthy, who organized the first AI conference in 1956, marking the beginning of AI research1. However, not only were the early years challenging, but as we will see, AI systems development also has a history of not living up to grand expectations.

The 1960s saw the development of expert systems2, which in the 1970s led to a focus on natural language processing3 and computer vision. In the 1980s, neural networks4 gained prominence, and parallel distributed processing became a popular AI approach. The 1990s saw significant advancements in AI, with the advent of machine learning algorithms and improved computing power.

Using system AI Chatbot in computer or mobile applications to artificial intelligence chatbots automatically respond to online messages and intelligent services to help students.

Why Learning AI Matters - Ethics and AI

AI is a rapidly growing field with the potential to greatly benefit society. However, it is crucial to address the ethical and societal concerns associated with its development.

One major concern is the concentration of decision-making power a few technology companies hold. These companies can shape AI technologies and algorithms, impacting how AI systems function and the potential benefits they bring. This concentration of power raises questions about the fairness and inclusivity of AI decision-making and the potential for bias in its outcomes.

Carnegie Mellon University's Block Center for Technology and Society 5 is a group exploring how technology, including AI, can be utilized for social good. The center takes a multidisciplinary approach, bringing together experts from various fields to examine technology's ethical and societal implications.

Through research, education, and policy engagement, the center aims to ensure that technological advancements are aligned with societal values and serve the public interest.

It is important to familiarize ourselves with AI's operating principles not only to leverage its capabilities but also to navigate the ethical considerations it presents.

It is our responsibility to ensure that AI benefits society. We must recognize the current situation and actively participate in shaping AI's future to ensure its alignment with societal values.

As AI continues to evolve and integrate into daily life, it is imperative we acquire knowledge on the ethical considerations surrounding its use. This ensures that we harness its potential in a responsible manner that aligns with societal values.

AI Today

Today, AI has become an integral part of our lives. It powers virtual assistants, recommendation systems, and self-driving cars. Deep learning, a subset of machine learning, has revolutionized the field. With the availability of big data and advancements in processing power, AI has made significant strides in image recognition, work life, and robotics.

As educators, we will feel the effects of AI as it evolves to become a crucial part of our student's future, enhancing our daily lives and shaping our future.

Footnotes

1. Dartmouth workshop - Wikipedia - The Dartmouth Summer Research Project on Artificial Intelligence was a 1956 summer workshop widely considered to be the founding event of artificial intelligence as a field.

2. Expert system - Wikipedia - An expert system is a computer system emulating the decision-making ability of a human expert.

3. Natural language processing - Wikipedia - Primarily concerned with allowing computers to support and manipulate human language.

4. Neural network - Wikipedia - A neural network is an artificial mathematical model used to approximate nonlinear functions. While early artificial neural networks were physical machines, today, they are almost always implemented in software.

5. Block Center for Technology and Society - Carnegie Mellon University (cmu.edu) - Take a look at this organization as its mandate, "The Block Center seeks out results-oriented projects that align with our three focus areas: how emerging technologies will alter the future of work, how AI and analytics can be harnessed responsibly for social good, and how innovation in these spaces can be more inclusive and improve quality of life for all."