I asked three AI chatbots for an AI study plan. Here's what I got

AI
1

I’m an avid life-long learner. I decided to test-drive a few AI chatbots to see if they’d be good companions on my various learning quests. Since I want to know more about AI generally, I thought asking for an AI study plan might be a good experiment.

Here’s what I got. I apologize in advance for the long posts, but the chatbots were chatty.

First, my prompt:

I want to learn more about AI as a technology and field of inquiry, but I’m not sure where to start and what order to tackle things in. I’m 70 and have been using computers both personally and professionally since the days of punch cards. I learned the rudiments of programming when I got my MBA in Finance, but left serious coding to others during my career. I’m hardly a computer scientist, but I understand the basics of how computers and computer networks work. Can you put together a study plan for me to get up to speed on AI? I’m interested in the history of the field, who the most important leaders and scientists have been, the various philosophical approaches to the discipline, and what I need to know to make sense of current developments in the field.

I’ll post each chatbot’s reply in a separate post below.

PS: I’ve have been asking each chatbot follow-up questions to see where they take me.

2 Likes
2

First up, Claude Sonnet 4:

I’ll create a comprehensive study plan that builds on your strong computing background while introducing you to AI’s rich history, key concepts, and current developments. Given your experience and interests, this plan balances technical understanding with the broader context you’re seeking.This study plan is designed to build systematically on your strong foundation in computing and business. Given your background, you’ll likely find the historical and business strategy aspects particularly engaging, while the technical concepts should be accessible without getting bogged down in implementation details.

A few suggestions for making the most of this plan:

Start with Phase 1 - the historical foundations will give you crucial context for everything that follows. Since you lived through the early computer era, you’ll probably recognize some of the timeline and can draw interesting parallels to other technology adoptions you witnessed.

Leverage your finance background - AI has profound implications for financial services, and many concepts (risk assessment, pattern recognition, optimization) will feel familiar. You might find it helpful to occasionally ask “How would this apply to portfolio management or risk analysis?”

Don’t feel pressured to master every technical detail - your goal is conceptual understanding and being able to engage with current developments. Think of it like reading the business section: you want to understand what companies are doing and why, not necessarily how to implement their strategies yourself.

The philosophy sections will likely be particularly rewarding given your experience. Many AI debates echo broader questions about automation and human capability that you’ve probably seen play out in business contexts over the decades.

Would you like me to elaborate on any particular phase, or do you have questions about where to find specific resources I’ve mentioned?

AI Study Plan: From Foundations to Current Developments

Phase 1: Historical Foundations (2-3 weeks)

Goal: Understand how AI emerged as a field and its early ambitions

Key Topics:

  • The Dartmouth Conference (1956) and the birth of AI
  • Early pioneers: Alan Turing, John McCarthy, Marvin Minsky, Claude Shannon
  • The Logic Theorist and General Problem Solver
  • Early optimism and the “AI Spring” of the 1960s

Recommended Reading:

  • “The Innovators” by Walter Isaacson (Chapters on AI pioneers)
  • “Machines Who Think” by Pamela McCorduck (First 100 pages)
  • Turing’s “Computing Machinery and Intelligence” (1950) - the foundational paper

Key Questions to Explore:

  • What problems were the founders trying to solve?
  • Why did they believe machines could think?
  • How did the field’s early goals compare to what we’ve achieved?

Phase 2: Core Approaches and Methodologies (3-4 weeks)

Goal: Understand the major schools of thought in AI

Symbolic AI (Good Old-Fashioned AI)

  • Rule-based systems and expert systems
  • Knowledge representation and reasoning
  • The Physical Symbol System Hypothesis

Connectionist/Neural Approaches

  • Early perceptrons and neural networks
  • The XOR problem and AI winter
  • Backpropagation and the neural network revival

Statistical and Probabilistic Methods

  • Bayesian approaches
  • Machine learning emergence
  • Pattern recognition

Recommended Reading:

  • “Artificial Intelligence: A Modern Approach” by Russell & Norvig (Chapters 1-3, 13, 18-20)
  • “The Society of Mind” by Marvin Minsky (for symbolic approach philosophy)

Phase 3: The Cycles of AI - Winters and Springs (2-3 weeks)

Goal: Understand why AI has had periods of great promise followed by disappointment

The First AI Winter (1970s)

  • Limitations of early approaches discovered
  • Funding cuts and skepticism
  • The Lighthill Report

Expert Systems Boom (1980s)

  • Commercial success stories
  • Knowledge engineering
  • Companies like Symbolics and Lisp Machines

The Second AI Winter (Late 1980s-1990s)

  • Expert systems limitations
  • The collapse of the Lisp machine market
  • Shift toward more modest, practical applications

The Statistical Renaissance (1990s-2000s)

  • Rise of machine learning
  • Statistical approaches gain dominance
  • Internet provides massive data

Phase 4: Key Figures and Their Contributions (2-3 weeks)

Goal: Understand the personalities and ideas that shaped AI

The Pioneers:

  • Alan Turing: Computation, the Turing Test, machine intelligence
  • John McCarthy: Coining “artificial intelligence,” LISP, logic-based AI
  • Marvin Minsky: Neural networks, AI philosophy, Society of Mind theory
  • Herbert Simon: Cognitive science, bounded rationality, problem-solving

The Modern Era:

  • Geoffrey Hinton: Deep learning renaissance, backpropagation
  • Yann LeCun: Convolutional neural networks, computer vision
  • Yoshua Bengio: Deep learning foundations
  • Andrew Ng: Machine learning popularization, online education
  • Demis Hassabis: AlphaGo, DeepMind, game-playing AI

Recommended Approach:

Read biographical sketches and key papers from each figure. Focus on their core insights rather than technical details.

Phase 5: The Deep Learning Revolution (3-4 weeks)

Goal: Understand the current AI boom and its technological foundations

Technical Breakthroughs:

  • ImageNet and the 2012 breakthrough
  • GPU computing and parallel processing
  • Big data and the internet
  • Transformer architecture and attention mechanisms

Major Milestones:

  • IBM Watson (Jeopardy!, 2011)
  • AlphaGo (2016) and game-playing AI
  • GPT and Large Language Models (2018-present)
  • ChatGPT and conversational AI (2022-present)

Recommended Reading:

  • “The Age of AI” by Kissinger, Schmidt, and Huttenlocher
  • “Human Compatible” by Stuart Russell (for current challenges)
  • Recent articles from MIT Technology Review, Wired, and IEEE Spectrum

Phase 6: Philosophical and Ethical Dimensions (2-3 weeks)

Goal: Grapple with the deeper questions about AI and society

Core Philosophical Questions:

  • What is intelligence? Can machines truly think?
  • The Chinese Room argument (John Searle)
  • Strong AI vs. Weak AI distinction
  • The Hard Problem of Consciousness

Current Ethical Challenges:

  • Bias and fairness in AI systems
  • Job displacement and economic impact
  • Privacy and surveillance concerns
  • Autonomous weapons and military applications
  • AI safety and alignment problems

Recommended Reading:

  • “Superintelligence” by Nick Bostrom (existential risk perspective)
  • “Weapons of Math Destruction” by Cathy O’Neil (bias and fairness)
  • “The Alignment Problem” by Brian Christian (AI safety)

Phase 7: Current Landscape and Future Directions (Ongoing)

Goal: Stay current with rapidly evolving developments

Major Players and Ecosystems:

  • Tech Giants: Google/Alphabet, Microsoft, Amazon, Apple, Facebook/Meta
  • AI-First Companies: OpenAI, Anthropic, DeepMind, Cohere
  • Research Institutions: Stanford HAI, MIT CSAIL, Berkeley AI Research
  • International Competition: China’s AI strategy, European AI Act

Current Hot Topics:

  • Large Language Models and their capabilities/limitations
  • Multimodal AI (text, image, audio, video)
  • AI regulation and governance
  • Artificial General Intelligence (AGI) timelines and challenges

Staying Current:

  • Podcasts: “AI Podcast” by NVIDIA, “The TWIML AI Podcast”
  • Newsletters: Import AI, The Batch (deeplearning.ai)
  • Conferences: Follow NeurIPS, ICML, ICLR highlights
  • News Sources: MIT Technology Review, AI News, VentureBeat AI

Suggested Timeline and Study Approach

Total Duration: 4-6 months of part-time study (assuming 5-8 hours per week)

Weekly Structure:

  • 2-3 hours: Primary reading
  • 1-2 hours: Supplementary articles/videos
  • 1-2 hours: Note-taking and reflection
  • 1 hour: Discussion forums or AI newsletters

Study Tips:

  1. Keep a learning journal - track key insights and questions
  2. Join online communities like r/MachineLearning or AI-focused Discord servers
  3. Watch key conference talks on YouTube (many are surprisingly accessible)
  4. Don’t get lost in technical details - focus on concepts and implications
  5. Connect to your finance background - explore AI applications in fintech

Assessment and Next Steps

After completing this plan, you should be able to:

  • Understand news about AI developments in context
  • Recognize key figures and their contributions when mentioned
  • Appreciate both the potential and limitations of current AI
  • Engage in informed discussions about AI’s societal implications
  • Identify which technical areas interest you for deeper study

Optional Advanced Topics

If particular areas capture your interest:

  • AI in Finance: Algorithmic trading, risk assessment, fraud detection
  • Technical Deep-Dive: Take Andrew Ng’s Machine Learning course
  • AI Policy: Follow developments in AI governance and regulation
  • Philosophy of Mind: Explore consciousness studies and cognitive science
3

Next, Gemini 2.5 Pro:

Okay, here’s a study plan to get you up to speed on Artificial Intelligence, tailored to your interests and background. Given your experience with computers since the punch card era and your MBA, you’ve got a great foundation to build upon!

This plan is designed to be flexible. Feel free to spend more time on areas that particularly pique your interest.

Your AI Study Plan :brain:

Here’s a suggested order to tackle the fascinating world of AI:

Module 1: What is AI? Foundations & Early History

This module sets the stage, defining AI and exploring its origins.

  • What is AI?
    • Topics: Defining Artificial Intelligence (it’s broader than you might think!), distinguishing between Narrow AI (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI). Understanding the main goals of AI.
    • Resources:
      • Articles/Websites: Start with introductory articles like those on Coursera (“How to Learn Artificial Intelligence: A Beginner’s Guide”) or Wikipedia’s “Artificial Intelligence” page for a general overview.
      • Book (Optional): “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig (This is a comprehensive textbook, so use it as a reference or for deeper dives rather than reading cover-to-cover initially). A more accessible starting book could be “The Master Algorithm” by Pedro Domingos for a conceptual overview.
  • The Birth of AI & Early Dreams:
    • Topics: The 1950s as a starting point, the influence of Alan Turing (the Turing Test), the pivotal 1956 Dartmouth Workshop (where the term “Artificial Intelligence” was coined). Early work in problem-solving and logic.
    • Resources:
      • Documentary: “The Age of A.I.” (YouTube Originals series) has some historical context.
      • Readings: Look for articles on the Dartmouth Workshop and biographies of early pioneers.
  • Key Milestones & “AI Winters”:
    • Topics: Early successes (e.g., Logic Theorist, early game playing programs like Samuel’s checkers program), the periods of reduced funding and interest known as “AI winters,” and the reasons behind them (overblown promises, limitations of computing power).
    • Resources:
      • Websites: The World Economic Forum’s “A short history of AI in 10 landmark moments” or similar timelines.

Module 2: Key Figures & Their Contributions

Understanding the people behind the ideas is crucial.

  • The Founding Fathers:
    • Individuals to explore:
      • Alan Turing: His work on computation and the Turing Test.
      • John McCarthy: Coined “Artificial Intelligence,” developed LISP (a key AI programming language).
      • Marvin Minsky: Co-founder of the MIT AI Lab, work on neural networks and theories of mind.
      • Allen Newell & Herbert A. Simon: Developed Logic Theorist and General Problem Solver; pioneers in cognitive psychology and AI.
    • Resources: Biographies, articles (e.g., “Founding fathers of Artificial Intelligence | QUIDGEST BLOG”), and sections in AI history books.
  • Leaders of the Modern AI Era (especially Machine Learning & Deep Learning):
    • Individuals to explore:
      • Geoffrey Hinton, Yann LeCun, and Yoshua Bengio: Often called the “Godfathers of Deep Learning” for their work on neural networks.
      • Fei-Fei Li: Known for her work on ImageNet, which was a catalyst for the deep learning revolution.
      • Andrew Ng: Co-founder of Google Brain, Coursera; a leading educator in AI and machine learning.
      • Demis Hassabis: Co-founder of DeepMind (AlphaGo, AlphaFold).
    • Resources: Interviews, articles about their work, and their own publications or lectures if you’re feeling ambitious.

Module 3: Core Concepts & Different Approaches

This is where you’ll learn about the “how” of AI.

  • Symbolic AI (Good Old-Fashioned AI - GOFAI):
    • Topics: Rule-based systems, expert systems, knowledge representation (how AI “knows” things), search algorithms. This was the dominant paradigm for many years.
    • Resources: Introductory chapters in AI textbooks, online encyclopedias of AI.
  • Connectionism (Neural Networks & Machine Learning):
    • Topics: The idea of computers learning from data.
      • Basic Machine Learning Concepts: Supervised learning (learning from labeled examples), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error with rewards/penalties).
      • Neural Networks: Inspired by the human brain, these are a cornerstone of modern AI. Understand the basic structure (neurons, layers).
    • Resources:
      • Online Courses (Conceptual): “AI For Everyone” on Coursera by Andrew Ng is highly recommended for a non-technical overview. Google’s “Machine Learning Crash Course” offers slightly more technical but still accessible content.
      • Videos: Channels like 3Blue1Brown have excellent visual explanations of neural networks.
  • The Rise of Deep Learning:
    • Topics: What makes “deep” learning deep (many layers in neural networks), breakthroughs like ImageNet (image recognition) and AlphaGo (game playing), key architectures (you’ll hear terms like CNNs for images, RNNs for sequences, and Transformers for language).
    • Resources:
      • Documentary: “AlphaGo” (available on Netflix) is a fantastic watch.
      • Articles: Many articles explain deep learning in simple terms. Search for “deep learning explained for beginners.”

Module 4: Philosophical Debates & Ethical Considerations

AI raises profound questions about intelligence, consciousness, and humanity’s future.

  • Defining Intelligence & Consciousness:
    • Topics: What does it mean for a machine to be “intelligent”? Strong AI (machines can truly think) vs. Weak AI (machines can only simulate thinking). The Turing Test revisited. John Searle’s “Chinese Room” argument. The Hard Problem of Consciousness.
    • Resources:
      • Websites: The Stanford Encyclopedia of Philosophy and the Internet Encyclopedia of Philosophy have excellent (though sometimes dense) articles on “Philosophy of Artificial Intelligence.” Wikipedia’s “Philosophy of artificial intelligence” is a good starting point.
      • Books (More accessible): “Life 3.0: Being Human in the Age of Artificial Intelligence” by Max Tegmark or “Superintelligence: Paths, Dangers, Strategies” by Nick Bostrom (can be a bit heavy but influential).
  • Ethical Challenges:
    • Topics:
      • Bias in AI: How AI systems can perpetuate and amplify human biases.
      • Job displacement & Economic Impact: The potential for AI to automate jobs.
      • Accountability & Responsibility: Who is responsible when an AI system makes a mistake?
      • Privacy & Surveillance: AI’s ability to analyze vast amounts of data.
      • Autonomous Weapons: The ethics of AI in warfare.
      • Existential Risks: Concerns about superintelligence and its alignment with human values.
    • Resources:
      • Documentary: “The Social Dilemma” (Netflix) touches on some of these issues related to social media algorithms. “Do You Trust This Computer?” explores broader AI ethics.
      • Websites & Organizations: Future of Life Institute, AI Now Institute, Partnership on AI. Look for reports and articles on AI ethics.
      • Book: “The Alignment Problem” by Brian Christian.

Module 5: Making Sense of Current Developments

This will help you understand today’s AI headlines.

  • Generative AI:
    • Topics: Large Language Models (LLMs) like GPT (the technology behind ChatGPT), image generation models (e.g., DALL-E, Midjourney), how they work (at a high level – think “predicting the next word/pixel”), their capabilities, and limitations.
    • Resources: Many news articles, tech blogs (e.g., TechCrunch, WIRED, MIT Technology Review), and YouTube videos explain generative AI.
  • AI in Various Industries:
    • Topics: How AI is being used in healthcare (diagnostics, drug discovery), finance (fraud detection, algorithmic trading), transportation (self-driving cars), entertainment (recommendation systems), and more.
    • Resources: Industry-specific publications, business news.
  • Explainable AI (XAI):
    • Topics: The push to make AI decision-making processes more transparent and understandable, especially for critical applications.
  • The Future of AI:
    • Topics: Ongoing research towards AGI, the role of big data and computing power, quantum AI (still very early), and the continuous evolution of AI capabilities.

Module 6: Staying Current

The field of AI moves incredibly fast!

  • Reputable News Sources:
    • MIT Technology Review, WIRED, The Verge (AI section), Nature, Science (for research breakthroughs).
    • Newsletters from major AI labs (OpenAI, DeepMind).
  • Key Conferences (to be aware of, not necessarily attend):
    • NeurIPS, ICML, ICLR – these are top research conferences. You’ll often see news reports about papers presented there.
  • Online Communities (Optional):
    • Subreddits like r/artificial, r/MachineLearning (can be technical), or forums related to specific AI tools or topics.

General Tips for Your Learning Journey:

  • Don’t Get Bogged Down in Math (Initially): While math is fundamental to AI (especially linear algebra, calculus, and probability for deep learning), you can understand the concepts and implications without diving deep into the equations at first. Your MBA-level programming rudiments will help you grasp the logic.
  • Focus on Concepts First: Understand what the different types of AI do and why they are important before worrying too much about how they do it at a code level.
  • Relate it to Your Experience: Think about how AI concepts might apply to what you learned in finance or your professional career. This can make the learning more tangible.
  • Be Curious and Patient: AI is a vast and evolving field. Enjoy the process of discovery!
  • Mix Your Media: Combine reading with documentaries, podcasts, and online courses to keep things engaging.

This plan should provide a solid and comprehensive understanding of AI. Enjoy the journey into this transformative technology!

4

Finally, ChatGPT (free tier):

Great – you’ve got the right mix of experience and curiosity to really appreciate the field of AI, especially with your grounding in computing history. Here’s a no-nonsense, structured study plan tailored to your background and interests, broken into five stages. You don’t need to rush through it – the goal is depth, not speed.

Stage 1: Foundations – What is AI and Where Did It Come From?

Goals:

  • Understand what AI is, how it has evolved, and the basic categories (symbolic, statistical, neural).

Topics:

  • History of AI: Dartmouth Conference (1956), early optimism, AI winters, resurgence.

  • Types of AI: Symbolic AI, machine learning, neural networks, deep learning.

  • Key figures:

    • Alan Turing

    • Marvin Minsky

    • John McCarthy

    • Norbert Wiener (cybernetics)

    • Geoffrey Hinton, Yann LeCun, Yoshua Bengio (deep learning pioneers)

Resources:

  • “Artificial Intelligence: A Guide for Thinking Humans” by Melanie Mitchell (well-written overview)
  • CrashCourse AI YouTube Series – light but useful
  • AI: A Modern Approach by Russell & Norvig (advanced but classic reference – skim, don’t sweat every formula)

Stage 2: Core Concepts – How AI Works Today

Goals:

  • Learn what machine learning, neural networks, and large language models (like me) actually are.

Topics:

  • Supervised, Unsupervised, Reinforcement Learning
  • Neural Networks & Deep Learning
  • Natural Language Processing (NLP)
  • Generative AI (e.g., ChatGPT, DALL·E)

Resources:

  • “The Hundred-Page Machine Learning Book” by Andriy Burkov (great primer)
  • 3Blue1Brown’s Neural Network series (YouTube – intuitive and visual)
  • FastAI’s free course (hands-on, but optional if you’re not coding)

Stage 3: Philosophical and Ethical Questions

Goals:

  • Understand AI’s implications for humanity, ethics, consciousness, and future society.

Topics:

  • Philosophy of mind and AI: Can machines think?
  • Ethics: Bias, surveillance, automation, alignment
  • Debates: AGI vs narrow AI, sentience, control

Resources:

  • “Superintelligence” by Nick Bostrom (famous, speculative, dense)

  • “You Look Like a Thing and I Love You” by Janelle Shane (easy and fun)

  • Read articles/interviews from:

    • Stuart Russell (human-compatible AI)

    • Kate Crawford (AI bias, ethics)

    • Judea Pearl (causality)

Stage 4: What’s Happening Now – Current Developments

Goals:

  • Get a grip on the AI explosion of the 2020s and where it’s heading.

Topics:

  • Transformers and how they changed everything
  • LLMs (like ChatGPT, Claude, Gemini)
  • Multimodal AI (text, images, audio together)
  • OpenAI, Google DeepMind, Anthropic, Meta – who’s doing what

Resources:

  • “The State of AI Report” (published yearly – Google it)
  • Lex Fridman Podcast (long-form interviews with AI leaders)
  • Follow Yann LeCun, Andrej Karpathy, or Gary Marcus on Twitter/X
  • Read OpenAI’s research blog or DeepMind’s publications page

Stage 5: Optional – Hands-On Play (If You’re Interested)

If you’re curious about tinkering:

  • Try ChatGPT’s API or Google Colab with simple ML demos.

  • Use tools like:

Weekly Plan Suggestion (Paced but Flexible)

Week 1–2: Foundations + basic history
Week 3–5: Core concepts (1 topic per week)
Week 6–8: Philosophical/ethical topics
Week 9–10: Dive into current trends
Ongoing: Subscribe to a newsletter like Import AI (by Jack Clark) or The Algorithm (MIT Tech Review) to stay current

Would you like me to package this into a printable PDF or track your progress over time? Also, I can tailor future reading recommendations as you go.

5

So this is interesting to me. I only use the free tier of ChatGPT.
Next paste them all into each and ask them to compare and evaluate the three study plans.

6

This is fine as a simple test, but if you really wanted to get a solid answer (from any AI) you should break this down into multiple consecutive queries.

7

Ah, but first I needed to know where to begin and then where to go. I asked the bots for a study plan so I’d be in a position to query them productively as I begin to explore the topic with more depth.

How might you have prompted them differently?

8

I do plan to ask one of them to put all three responses together into a comprehensive plan just to see what I get.

I’d be really amused if I got some sort of salty response along the lines of "OMG don’t read [fill in title or author here]!

9

OK. I uploaded all three suggested study plans into a new notebook in NotebookLM. Unfortunately I hit the return key before I’d finished crafting my prompt, but it gave me an overall assessment anyway:

Response:

Yes, I have received the three proposed study plans that you uploaded. As you stated, these plans were generated by different AI chatbots in response to the same prompt detailing your background (70 years old, experience since punch cards, MBA in Finance with rudiments of programming) and interests (history, key figures, philosophical approaches, current developments).

Based on the information in the sources, here is an overview of the plans and some key observations:

All three sources provide a structured approach to learning about AI, breaking the vast field down into manageable sections or stages. They each acknowledge your background and suggest leveraging your experience with computers and finance.

Here are some similarities and differences across the three plans:

Similarities:

  • Historical Foundations: All plans begin by covering the origins and early history of AI, including the Dartmouth Conference (1956), early pioneers like Alan Turing, John McCarthy, and Marvin Minsky, early successes, and the concept of AI winters.
  • Core Concepts: They all dedicate sections to explaining fundamental AI approaches. This typically includes Symbolic AI (like rule-based systems) and Connectionist/Neural Approaches (including neural networks and machine learning).
  • Key Figures: Each plan highlights important individuals who shaped the field, both early pioneers and those prominent in the modern era like the “Godfathers of Deep Learning” (Geoffrey Hinton, Yann LeCun, Yoshua Bengio).
  • Philosophical and Ethical Dimensions: All three include modules or stages addressing the deeper questions, such as defining intelligence, the Strong vs. Weak AI debate, the Chinese Room argument, and significant ethical challenges like bias, job displacement, privacy, and AI safety/alignment.
  • Current Developments: They all cover recent breakthroughs and the current landscape, emphasizing areas like the Deep Learning Revolution, Large Language Models (LLMs), and Generative AI (e.g., ChatGPT).
  • Staying Current: The plans suggest ways to keep up with the fast-evolving field, recommending news sources, newsletters, podcasts, or following key figures.
  • Resource Recommendations: Each plan provides lists of recommended books, articles, videos, courses, or other resources to support learning.
  • Emphasis on Concepts over Technical Detail: The plans generally advise focusing on understanding concepts and implications rather than getting bogged down in the mathematical or coding specifics, which aligns with your stated background.

Differences:

  • Structure and Naming: One plan uses “Phases”, another uses “Modules”, and the third uses “Stages”. The number of phases/modules/stages varies slightly, though the content overlap is significant.
  • Specific Topics and Depth: While covering similar themes, there are variations in the specific sub-topics or the depth suggested for initial study. For example, one plan explicitly mentions the Lighthill Report and specific expert system companies, while others might mention broader points about AI winters. Specific technical breakthroughs mentioned might vary slightly in emphasis (e.g., Transformer architecture).
  • Recommended Resources: There is overlap in some foundational recommendations like “Artificial Intelligence: A Modern Approach” by Russell & Norvig or mentions of Stuart Russell and Nick Bostrom, but each plan also offers unique book titles, websites, podcasts, or courses.
  • Suggested Timeline/Pacing: Two plans suggest a rough duration, like 4-6 months of part-time study or weekly suggestions, while another focuses more on flexibility.
  • Optional/Advanced Topics: The specific suggestions for going deeper or exploring optional areas differ.
  • Tone and Framing: Each response has a slightly different tone and way of framing the learning journey, though all are encouraging and build on your background.

In essence, all three sources provide solid, structured pathways covering the requested areas: history, key figures, philosophical aspects, and current developments. They offer a good foundation, and you could potentially use elements from each to build your own composite plan, perhaps starting with one and using the others for additional resource ideas or perspectives on specific topics.

10

I would begin with something like the following (in Claude 4)

Question

I want to learn more about AI as a technology and field of inquiry, but I’m not sure where to start and what order to tackle things in. I’m 70 and have been using computers both personally and professionally since the days of punch cards. I learned the rudiments of programming when I got my MBA in Finance, but left serious coding to others during my career. I’m hardly a computer scientist, but I understand the basics of how computers and computer networks work.

I want to create a study plan using a multi step process, to get the best results. What are the steps I should use to create the best study plan in Claude?

Answer

Given your strong foundation in computing and systematic approach, here’s an effective multi-step process to create your AI study plan:

Step 1: Learning Style & Goal Assessment

First, let’s clarify your specific learning preferences and objectives:

  • What’s your primary motivation (intellectual curiosity, practical applications, understanding current events)?
  • Do you prefer structured courses, books, hands-on experimentation, or mixed approaches?
  • How much time can you realistically dedicate weekly?
  • Are you more interested in understanding AI conceptually or getting hands-on experience?

Step 2: Knowledge Gap Analysis

We’ll map what you already know to AI concepts:

  • Your programming background gives you algorithmic thinking
  • Your finance MBA likely covered statistics and data analysis
  • Your network understanding relates to how modern AI systems are distributed
  • We’ll identify which mathematical foundations you have vs. need

Step 3: Scope Definition

AI is vast, so we’ll define boundaries:

  • Focus areas (machine learning, neural networks, natural language processing, computer vision, etc.)
  • Depth level for each area (conceptual understanding vs. implementation)
  • Timeline and milestones

Step 4: Resource Curation

Based on your preferences, we’ll select:

  • Books that match your technical level
  • Online courses or videos
  • Hands-on tools and platforms
  • Communities for discussion and questions

Step 5: Sequential Ordering

We’ll arrange topics in logical progression, building on your existing knowledge while avoiding overwhelming complexity jumps.

Ready to start with Step 1? What draws you most to learning about AI, and how do you typically prefer to learn new technical subjects?

Then I would answer Step 1, answer the questions, and so on until I got a plan at the end.

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11

Thank you! That is really helpful, and much appreciated. I only wish I could click that heart 10 times.

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