AI vs ML vs DL: Technical Genealogy & Study Roadmap for Developers

1. Introduction: "Let's Add AI to Our App"

Early in my startup journey, my CEO came back from an investor meeting with sparkling eyes. "We need to add an AI feature. Investors love it. Come up with a plan by next week."

As a backend developer, my mind became a tangled mess. "AI? Does that mean I need to build a chatbot? A recommendation engine? Do I need to study TensorFlow? Is simple Excel statistics also AI? Can I just wrap the ChatGPT API?"

Googling only increased the confusion. Some articles said "Machine Learning algorithms," others "Deep Learning models," and some just "AI technology." This post is the "AI Genealogy for Non-Majors" and a "Practical Guide for Developers" looking to adopt AI. Let's look past the media hype and understand the core technology.

2. My Initial Confusion: Where is the Boundary?

The most confusing part was the "Boundary".

• "Is AlphaGo Deep Learning? Machine Learning? Or just AI?" (Answer: All three.)
• "Is my if (user.age > 19) return 'adult' code also AI?" (Answer: In a very broad sense, yes. It's Rule-based AI.)
• "To do Machine Learning, do I absolutely need Neural Networks?" (Answer: No. You can do it with simple statistics in Excel.)

Because the media mixes these terms indiscriminately, I thought they were three competing technologies. "Should I use ML or DL?" was a wrong question. The truth is, they are in a Subset Relationship.

3. The Aha Moment: Matryoshka Dolls

The best analogy for AI, ML, and DL is the Russian Matryoshka Doll.

Visualization of the Hierarchy

The biggest doll contains a medium doll, which contains a smaller doll. And recently, a very special tiny doll appeared inside.

1. AI (Artificial Intelligence): The outermost doll. "Any technique mimicking human intelligence." It includes simple logic and rules.
2. ML (Machine Learning): The medium doll inside AI. "Techniques that learn from data without explicit rules."
3. DL (Deep Learning): The small doll inside ML. "Techniques using Multi-layered Neural Networks to extract features automatically."
4. GenAI (Generative AI): The latest trend inside DL (Transformers). "AI that creates new content beyond simple classification."

Once I visualized this, everything clicked. "ChatGP is GenAI, which is a type of Deep Learning, which is a type of Machine Learning, which is a part of AI." It's not a competition; it's a Precision vs Cost trade-off.

4. Deep Dive: From Symbolic AI to Transformers

Layer 1: AI (Symbolic AI) - The 1950s Era

Early AI was called GOFAI (Good Old-Fashioned AI). It was basically "Humans coding every single rule manually."

• Example: Deep Blue (Chess). Developers wrote thousands of if-else rules: "If pawn is at A2, move to A3."
• Limitation: You cannot code rules for ambiguous tasks. How do you define "a cat" in code? "Has ears"? A dog has ears too. The real world is too complex for hard-coded rules.

Layer 2: Machine Learning (ML) - The 1980s Era

"Don't code the rules. Feed data and let the machine find the rules." (Software 2.0 concept).

Developers don't code "cat features." Instead, they feed 10,000 cat photos and 10,000 dog photos (Labeled Data). The machine uses statistical algorithms to find the boundary line (Hyperplane) that separates cats from dogs. This process is called Training.

• Algorithms: Decision Trees (20 Questions), Random Forest, SVM, K-Means Clustering, Linear Regression.
• Limitation: It required Feature Engineering. Humans still had to manually process data (e.g., edge detection filters, converting text to frequency vectors) to make it understandable for the machine. The model's performance depended heavily on the human engineer's domain knowledge.

Layer 3: Deep Learning (DL) - The 2010s Era

"Let the machine do the Feature Extraction too."

This uses ANN (Artificial Neural Networks) inspired by human brain neurons. It became possible thanks to Big Data and NVIDIA GPUs. You throw Raw Data (pixels, audio waves) at it, and the deep layers automatically learn features hierarchy: Layer 1 learns lines -> Layer 2 learns shapes -> Layer 3 learns eyes/noses -> Layer 4 learns faces. This is Representation Learning.

• CNN (Convolutional Neural Network): King of Image processing.
• RNN (Recurrent Neural Network): King of Sequence Data (Text, Voice). But it was slow and forgetful.

Layer 4: Transformer & GenAI - The 2017 Revolution

Google's "Attention Is All You Need" paper (2017) changed everything.

• The Problem with RNN: It read sentences word by word (Sequential). It was slow and couldn't remember the beginning of a long paragraph (Long-term Dependency problem).
• Transformer: It reads the entire sentence at once (Parallel processing) and calculates the mathematical relationship between every word pair (Self-Attention).
  • Self-Attention Example: In "The animal didn't cross the street because it was too tired," the model calculates that 'it' has a 90% relationship with 'animal' and 10% with 'street'.
• GPT (Generative Pre-trained Transformer): OpenAI trained a massive Transformer on the entire internet. It learned to predict the next word. It turns out, if you are really good at predicting the next word, you appear to understand logic and reasoning.

5. Practical Guide: RAG vs Fine-tuning

This is the #1 question from founders: "How do I teach AI about my company's data?" There are two ways.

1) RAG (Retrieval-Augmented Generation) - The "Open Book" Method

• Analogy: "Taking an Open-Book Exam." You don't memorize the textbook; you look it up when asked.
• Method:
  1. User asks a question.
  2. System searches your company's database (Internal Wiki, Notion) for relevant docs.
  3. System feeds those docs + the question to GPT-4.
  4. GPT-4 answers based only on the provided docs.
• Pros: Up-to-date (Real-time), Less Hallucination (Grounding), Cheap, Secure (Data doesn't train the model).
• Verdict: Use this for 99% of business cases (e.g., Customer Support Chatbot, Internal Search).

2) Fine-tuning - The "Private Tutor" Method

• Analogy: "Intensive Tutoring for a specific subject." You force the student to memorize the textbook.
• Method: Retrain a model's weights with your specific dataset.
• Pros: Learns specific Tone & Manner, specialized Jargon (Legal/Medical), or specific Output Formats (JSON/SQL).
• Cons: Expensive GPU costs, Hard to prepare high-quality datasets, Catastrophic Forgetting (Model forgets general knowledge), Knowledge is static (cutoff date).
• Verdict: Use only when RAG fails. E.g., "I need a medical AI that speaks in a very specific doctor persona."

6. Ethical AI Considerations

As engineers, we must look beyond functionality.

1. Bias: Models train on internet data, which contains human bias. If training data is biased (e.g., CEOs are mostly men), the AI will be biased. Amazon scrapped their AI recruiting tool because it discriminated against women.
2. Copyright: Who owns the image generated by Midjourney? The prompter? Midjourney? The artists whose art was scraped? This is a legal minefield.
3. XAI (Explainable AI): We need to know why AI made a decision. If an AI creates a medical diagnosis or denies a loan, saying "The neural network said so" is not acceptable. We need transparency.

7. AI Study Roadmap for Developers

"I want to become an AI Engineer. Where do I start?" Don't start with Math. Start with Code.

Level 1: The API Consumer (Prompt Engineer)

Goal: Build apps using OpenAI/Claude APIs.

• Skills:
  • Prompt Engineering: Zero-shot, Few-shot, Chain-of-Thought (CoT).
  • Tools: LangChain, Vercel AI SDK.
  • Project: A translator app, a summarizer, or a "Talk to PDF" app.
• Advice: This is where 90% of the value lies today. You don't need to know how the transformer works to drive the car.

Level 2: The Application Engineer (RAG Specialist)

Goal: Integrate private data with LLMs.

• Skills:
  • Vector Databases: Pinecone, Weaviate, ChromaDB, Supabase pgvector.
  • Embeddings: Understanding semantic search space (Cosine Similarity).
  • Local LLMs: Running Llama 3 on your laptop using Ollama.
• Project: Build an internal company wiki search chatbot that cites sources.

Level 3: The Model Tuner (Fine-tuner)

Goal: Customize models for specific tasks.

• Skills:
  • Hugging Face: The GitHub of AI models. Learn to browse and download models.
  • PEFT (LoRA/QLoRA): Efficient fine-tuning without massive GPUs.
  • Cloud GPUs: Renting A100s on Lambda Labs or AWS SageMaker.
• Project: Fine-tune a Llama model to speak like a pirate or generate SQL queries for your specific schema.

Level 4: The Researcher (Scientist)

Goal: Architect new models.

• Skills:
  • Math: Linear Algebra, Calculus, Statistics.
  • Frameworks: PyTorch, TensorFlow at a low level.
  • Papers: Reading ArXiv daily.
• Advice: Only go here if you love math and research. This is PhD territory.

8. Common Myths about AI

Myth 1: "AI will replace all developers."

Truth: AI will replace developers who don't use AI. It's a productivity booster (GitHub Copilot), not a replacement. It handles boilerplate, leaving you to focus on system architecture and complex business logic. Code generation is easy; Code maintenance and architecture is hard.

Myth 2: "You need a PhD to do AI."

Truth: To invent models like GPT-4, yes. To use them, absolutely not. Modern AI engineering is more about Systems Engineering (connecting APIs, managing context windows, optimizing latency) than math.

Myth 3: "More data is always better."

Truth: "Garbage In, Garbage Out." 1,000 high-quality, human-verified examples are worth more than 1,000,000 noisy scraped web pages. Data Quality > Data Quantity.

9. Summary

"CEO, for 'Spam Filtering', let's start with simple ML (Naive Bayes) or OpenAI Moderation API to save costs. For the 'Company Policy Chatbot', let's use RAG instead of Fine-tuning to prevent hallucinations."

Knowing the hierarchy allows you to choose the right tool for the job. Don't just use GenAI because it's hype. Pick the right doll. That is true engineering.