AIML 500
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AI Timeline

Introduction

AI has rapidly evolved from early theories to transforming entire industries — this timeline captures the journey in one glance.

Description

This AI timeline was created by a team of four students as part of a group assignment for our AI course. The goal was to visually document the evolution of AI in a way that's both accessible and informative.

We split the project into historical segments and explored how foundational ideas, technologies, and social impact unfolded over time. The result is a timeline that reflects our research collaboration and shared understanding of AI’s growth.

Objective

Our objective is to understand AI’s evolution by identifying key milestones, analyzing their historical context, and reflecting on how AI has shaped the modern world. The project also helped us build collaboration, research, and digital presentation skills.

Process

Our group followed a straightforward process. We first divided the timeline into key historical phases of AI and assigned each member to research a specific period or topic. Everyone searched online for relevant breakthroughs and milestones.

After collecting our findings, we uploaded and organized them in Microsoft Teams. We then used Mermaid.js to create a clean and readable timeline visualization, keeping the implementation simple while focusing on clarity.

Tools/Technologies Used

  • Google Search – Used to research historical milestones, terminology, and foundational concepts in AI.
  • Microsoft Teams – Used as the primary collaboration platform to share drafts, divide sections, and consolidate content from all team members.
  • VS Code + Markdown – Used for lightweight implementation and formatting of the timeline structure before rendering.
  • Mermaid.js – A markdown-based diagram tool used to render the timeline visually with minimal setup.

Value Proposition

This timeline offers a simple and accessible way to understand the evolution of Artificial Intelligence. By presenting historical milestones in a clear chronological structure, it helps learners quickly grasp how key ideas and technologies have developed over time.

Rather than overwhelming viewers with technical complexity, this project focuses on clarity and structure—making it suitable for classroom use, peer learning, or anyone new to the field of AI. Its collaborative nature also highlights how group work can simplify complex topics through thoughtful division of tasks.

Final Artifact

Timeline Details

Stage 1: The Dawn and Golden Stage (1950s – Early 1970s)

By Chun Kit Kwong

  • 1950 – Alan Turing proposes the Turing Test
  • 1956 – Term "AI" coined at Dartmouth Conference
  • 1958 – John McCarthy creates Lisp
  • 1961 – Unimate, first industrial robot deployed
  • 1966 – ELIZA simulates a human therapist
  • 1970 – SHRDLU processes natural language commands

Stage 2: The First and Second AI Winters (1974–1990)

By Neng Xiong

  • 1974 – First AI winter due to stagnation
  • 1980 – Rise of Expert Systems (e.g., XCON)
  • 1987 – Second AI winter triggered by collapse of Expert Systems
  • 1990 – Shift toward machine learning methods (KNN, SVM, Decision Trees)

Stage 3: Rise of Machine Learning and Statistical AI (1990s–2010s)

By Tingting Wang

  • 1997 – Deep Blue defeats Garry Kasparov
  • 1998 – LeNet CNN for handwriting recognition (Yann LeCun)
  • 2006 – Geoffrey Hinton popularizes deep learning
  • 2011 – IBM Watson wins Jeopardy!
  • 2012 – AlexNet wins ImageNet competition

Stage 4: Deep Learning and Cloud Computing Era (2012–Present)

By Lu Han

  • 2014 – GANs proposed by Ian Goodfellow
  • 2015 – Deep Q-Networks by DeepMind
  • 2016 – AlphaGo defeats Lee Sedol
  • 2018 – Google releases BERT
  • 2019 – OpenAI releases GPT-2
  • 2020 – GPT-3 & AI commercialization via cloud
  • 2022 – ChatGPT released, LLMs enter public awareness
  • 2023 – Multimodal AI (text + image + audio) matures

Timeline Details

Through this timeline project, I developed a much clearer understanding of the long and layered history of artificial intelligence. I was surprised to learn that many core ideas in AI - like natural language processing and neural networks - were already being explored as early as the 1950s. Programming languages like LISP and early rule-based systems such as ELIZA helped shape foundational directions for AI. I also realized how progress in AI has not been linear: between the 1970s and 1990s, development stagnated, and it wasn’t until breakthroughs in hardware and deep learning around the 2010s that AI became truly powerful in practice. Tools like GPT-3 only recently made AI more widely accessible and visible to the general public, which made me reflect on how long it takes for foundational research to reach practical application.

In terms of group work and overall execution, our team focused on clear division of tasks and effective communication. We started with brainstorming sessions, after which each member worked on a specific portion of the timeline. The project flowed smoothly without major obstacles, largely because we adhered to the “keep it simple, stupid” (KISS) principle and used tools that were easy to manage and visually effective. This experience reminded me that simplicity - when paired with thoughtful design - can lead to clarity and impact. Overall, I found the project both informative and rewarding, as it not only deepened my knowledge but also gave me a positive collaborative experience.

Timeline Diagrams