Module 1: What Is AI Really?

1. Module Introduction: What Is AI Really?

Description

This is the first module of the AI Fundamentals guide and the starting point for everything that comes after. Here you won't code or look at formulas: you'll organize ideas. Many people talk about "AI", "machine learning" and "models" without being clear about what each one is; over the next lessons you'll clarify those definitions and place artificial intelligence in time and in the real world.

The goal is for you to have a solid conceptual base before diving into Machine Learning, neural networks, Transformers and LLMs. Without this base, the following modules would be just scattered terms; with it, you'll be able to follow the thread of why LLMs exist, what problem they solve and how they connect to the work of an AI Engineer.

This module is designed to be read in about 2 hours (reading + final exercise). It requires no installations or configuration; only attentive reading and a bit of reflection at the end to apply what you learned to products you already use. If after this module someone asks you "what is AI?" or "how is it different from machine learning?", you'll be able to answer precisely and without mixing everything together.

Why this module is different from other "intro to AI" courses: Many introductory courses are either too superficial (just "AI is when machines think") or too technical (they jump straight into math or code). This module sits in the middle: it gives you precise definitions and a clear map (AI, ML, DL, GenAI; narrow vs general) with no formulas or programming. That prepares you to read documentation, follow a bootcamp or make decisions about your career without getting stuck in vagueness or overwhelmed with details you'll see later on.

Analogy: the map before the trip

Think of this module as the map you look at before a trip. It's not the trip itself (that's modules 2 through 8: Machine Learning, neural networks, Transformers, LLMs, APIs, AI Engineering, design). But without the map, when you start hearing "Transformer", "LLM", "OpenAI API" or "RAG", you won't know where you are or where you're going. With the map, each new term fits into a place: "a Transformer is a neural network architecture", "an LLM is a large language model, which is a type of Deep Learning used to generate text", "the OpenAI API is a way to access an LLM without training it yourself". This module gives you that map: definitions, history, types and current state. The trip (code, APIs, design) comes after.

Context: where are we in the guide?

This guide has 8 modules across 3 phases. Each phase has a clear goal:

Phase 1 — Conceptual foundations (Modules 1-3): You're here. In this phase you organize concepts: what AI is, what Machine Learning is, what neural networks are (without math). There's no code; only definitions, history and classifications. The goal is that when we talk about "Transformers" or "LLMs" in Phase 2, they aren't scattered words but concepts you've already placed on the map.
Phase 2 — Modern architecture (Modules 4-6): Transformers, LLMs (GPT, Claude, etc.) and the API ecosystem. Here you get into how the models that dominate today's conversation work and how they're accessed (APIs, providers, costs). Everything you learn in Module 1 (definitions, types, current state) helps you not get lost.
Phase 3 — Application and role (Modules 7-8): What AI Engineering is, how it differs from ML/Data Science, and a first AI system design. Here you connect everything to the real role: what an AI Engineer does, what they don't do, and how a system that uses AI is designed in practice.

Module 1 is the entry point. It doesn't assume you know anything about AI; only that you want to understand it well in order to build with it later (or decide whether the AI Engineer role interests you). If you already have some context (for example, you've used ChatGPT or heard about "machine learning"), all the better; but it's not required.

Why the 3-phase structure matters: Phase 1 (where you are) gives you the vocabulary and the map; without that, Phase 2 (Transformers, LLMs, APIs) would be a list of acronyms with no context. Phase 2 gives you the "how they work" and the "how to access them"; without that, Phase 3 (AI Engineering, system design) would be abstract. Phase 3 connects everything to the real role: what an AI Engineer does, what they don't do and how a system that uses AI is designed. Each phase builds on the previous one; that's why this first module is fundamental.

Why this module is the foundation (concrete examples): When you read "training vs inference" in Module 2, you'll already be clear that "learning from data" (ML) is different from "using an already-trained model" (inference); without the definitions in this module, that distinction can seem trivial but it's central for an AI Engineer (you don't train GPT, you use it). When you read "LLM" or "large language model" in Module 5, you'll already know it's a type of Deep Learning used to generate text (GenAI) and that it's narrow AI; without this module, "LLM" would be a black box. When you read "AI Engineer vs ML Engineer" in Module 7, you'll already be clear on what ML is and what AI is; without this module, the comparison would be confusing. In short: this module isn't "theory you'll never use"; it's the map that keeps you from getting lost in the following modules.

Module goal

By the end of this module you'll be able to:

Define with precision what AI, ML, Deep Learning and Generative AI are, and not lump them all into the same bag. In practice: when you read an article or a job posting that says "machine learning" or "generative models", you'll know what they mean and how they relate to "artificial intelligence".
Place in time the recent history of AI (Turing, winters, the current boom) to understand why today we talk so much about LLMs. In practice: you won't think "it all started with ChatGPT"; you'll have context for why large language models are today's spearhead and what came before.
Classify types of AI (narrow vs general, weak vs strong) and use it in conversations or reading. In practice: when someone talks about "AGI" or "general AI", you'll know it's a long-term concept and that everything that exists today in products is "narrow AI".
Describe the present of the field: what's reasonable to expect in 2024-2026 and what's myth or exaggeration. In practice: you'll be able to read news or announcements with a critical eye and not confuse demos with reality or "revolution" with incremental improvement.
Recognize AI in day-to-day life in products you use (apps, assistants, recommendations, etc.). In practice: the module's final exercise makes you list at least 5 products, classify them (AI, ML, GenAI when applicable) and reflect on what the AI does in each one and what limits it has.

All of that is conceptual. There's no code or programming exercises; there are reflection and classification exercises that reinforce the ideas. If your goal is to program with AI APIs or build systems, that comes in the bootcamp and in other modules of the guide; here we only lay the foundations so you don't walk in empty-handed when you reach those topics.

Prerequisites

For this module all you need is:

Curiosity about technology and about how "intelligent" systems work. You don't need to know how to program or to have ever used an LLM.
Willingness to read attentively (2–3 hours total for the module). The lessons are dense with concepts; if you skim them, you'll lose the thread of the definitions and classifications.
Optional: have on hand products you use (Netflix, Spotify, ChatGPT, voice assistants, email, maps, etc.) for the final exercise. You don't have to list 5 from day one; you can do the exercise in two rounds (first 2–3 products, then complete up to 5).

You don't need:

Prior programming. This module has no code.
Advanced math. There are no formulas or algebra; only ideas and classifications.
Knowledge of Machine Learning or statistics. Everything you need to follow the module is explained here.
To have ever used an LLM or an AI API. If you've already used ChatGPT or similar, you'll have more mental examples; if not, it doesn't matter, the examples in the text and in the exercise are enough.

If you already have training in another discipline (for example, philosophy, law, design) and want to understand AI in order to work with technical teams or make decisions, this module is designed for you too: the level is accessible and the focus is conceptual.

If you have X, all the better (but not required): If you've already used ChatGPT, Claude or similar, you'll have mental examples of "generative model" and "LLM"; that helps in lesson 02 (GenAI) and in 05 (current state). If you've already read something about "machine learning" or "neural networks", lessons 02 and 03 will go faster; even so, don't skip 02 because many people mix up AI and ML without realizing it. If you already work in tech (development, product, data), you'll have more examples of products with AI for exercise 06; if not, the examples in the text (Netflix, Gmail, Spotify, etc.) are enough.

If you don't have Y, it doesn't matter: You don't need to program, know advanced math, or have ever used an API. You don't need to know what a "token" or an "embedding" is; that comes in later modules. You don't need to have a formed opinion on ethics or regulation; here we only give the map of what AI is and what types there are; ethics and regulation build on that map but aren't developed here.

Module roadmap: the 6 lessons

Below is the map of the 6 lessons, what you'll see in each and what you'll gain by finishing it.

#	Lesson	What you'll see	What you'll gain	Approx. duration
01	Module introduction	Context of the guide, module goal, roadmap, limits	Clarity about what this module covers and how it fits into the full guide	15 min
02	Definitions of AI	AI vs ML vs DL vs GenAI: untangling the confusion	A precise map of terms; you'll be able to explain the difference in one sentence and not mix up concepts	25 min
03	A brief history of AI	From Turing to ChatGPT; winters and booms	Temporal context: why today we talk about LLMs and what came before (rules, expert systems, ML, DL)	25 min
04	Types of AI	Narrow vs General, Weak vs Strong, other classifications	Criteria to classify systems and not confuse what exists today (narrow) with the hypothetical (AGI)	20 min
05	AI in 2024-2026	Current state: what's possible, what's not, what's coming	A realistic view of the present: what to expect from products and news, and what's myth or exaggeration	25 min
06	Exercise: AI all around you	Identifying and classifying AI in everyday products	Practical application: at least 5 products listed, classified and reflected on with the module's concepts	30 min

Module total: ~2 hours (reading + exercise). If you want to go deeper with each lesson's additional resources, you can add 30–60 more minutes.

How to gauge your own pace: The durations in the table are approximate (medium reading pace, no long breaks). If you read more slowly or want to take notes, it could be 2.5–3 hours. If you already have context and are only reviewing, it could be 1–1.5 hours. What matters isn't speed but that by the end you can meet the "Evidence of success" and the "Checklist before moving to Module 2" you'll see below.

Estimated time per lesson (detail): Lesson 01 (this one) usually takes 15–20 minutes if you read calmly. Lesson 02 (definitions) is dense; set aside 25–30 minutes and use the "Quick comparison" table as a reference. Lesson 03 (history) reads fast if you don't stop at dates; 20–25 minutes. Lesson 04 (types) is short but important so you don't confuse narrow with general; 15–20 minutes. Lesson 05 (current state) has many lists (what's possible, what's not, trends); 25–30 minutes. Lesson 06 (exercise) depends on you: listing 5 products and classifying them can take 20–30 minutes; if you also write the reflection and the 3 cases in detail, 40–50 minutes. In total, 2–2.5 hours is a good range for the first time.

Why this lesson order

The lessons are ordered deliberately:

Definitions (02) before history (03): To follow the history you need to know what we're talking about when we say "AI" or "ML". If you read the history without the definitions, the terms get mixed up.
History (03) before types (04): The "narrow vs general" or "weak vs strong" classification makes sense once you know there were eras of great optimism and others of skepticism, and that today we're in a boom centered on large models (very capable narrow AI).
Types (04) before current state (05): In "AI in 2024-2026" we talk about what's possible today and what's not; for that you need to be clear that everything current is narrow AI and that AGI doesn't exist. Without lesson 04, lesson 05 could give the impression that "anything is possible".
Current state (05) before the exercise (06): In the exercise you'll identify real products and reflect on what AI can do today that it couldn't years ago. That requires having read lesson 05 (what's possible, what's not, trends). Without 05, the reflection in exercise 06 can stay vague ("AI has improved") instead of concrete ("5 years ago it didn't have coherent text generation or images from descriptions; today it does, thanks to large models and APIs").

If you already have experience and want to go straight to a topic (for example, only definitions or only current state), you can skip ahead; but the first time it's recommended to read in order so you don't lose the thread.

Connection with the rest of the guide

What you learn in this module is used like this in the following ones:

Module 2 (Machine Learning): When we talk about "learning from data", "supervised vs unsupervised" or "training vs inference", you'll already be clear that ML is a subset of AI and that not all AI is ML. That avoids the typical confusion of using "AI" and "ML" as synonyms. Also, the "training vs inference" distinction is central for an AI Engineer: we don't cover it in detail in this module, but the idea that systems "learn" and then "are used" is already prepared.
Modules 4 and 5 (Transformers and LLMs): The history (lesson 03) and the definitions (lesson 02) give you context: why Transformers appeared, what problem they solve compared to recurrent networks, and why large language models dominate today's conversation. Without this module, "Transformer" or "LLM" would be black boxes; with it, you have a framework (AI → ML → DL → large models for language and generation).
Module 6 (APIs): You'll understand that when you "call a model" (for example, via OpenAI or Anthropic) you're using a specific type of AI (an LLM, which is narrow AI and often GenAI) within a broader ecosystem (providers, prices, open source vs proprietary models). The definitions and types in this module help you not confuse "the model" with "all of AI" or with "AGI".
Module 7 (AI Engineering): You'll know where an AI Engineer's work sits on the map: integrating models (narrow AI) into products, not training models from scratch or researching AGI. Lessons 04 (types) and 05 (current state) are the basis for understanding the role and its limits.
Module 8 (AI system design): When you design your first AI system (for example, a Q&A over documentation), you'll explicitly use the concepts of this module: what type of AI it is (narrow), what it does (generates answers, possibly with RAG), and what limits it has (hallucinations, context, cost). Without the definitions and the current state, the design would be vague.

In short: if someone asks you "what is AI?" or "how is it different from machine learning?", after this module you'll be able to answer precisely. And when in later modules you read "ML model", "neural network", "LLM" or "generative model", you'll be clear which layer of the map they belong to.

Connection summary (reference table):

When in a later module...	You'll use from this module...
Module 2: "learning from data", "training vs inference"	Definitions (ML as a subset of AI); the idea that systems "learn" and then "are used"
Modules 4-5: "Transformer", "LLM", "language model"	Definitions (DL, GenAI); history (why Transformers appeared); types (narrow AI)
Module 6: "OpenAI API", "provider", "model"	Definitions and types (what type of AI an LLM is); current state (what's possible today)
Module 7: "AI Engineer", "integrating models"	Types (narrow AI); current state (what an AI Engineer does in today's ecosystem)
Module 8: "designing an AI system"	Everything: definitions, types, current state (what's possible, what limits it has)

Limits: what this module does NOT do

So you don't expect something we're not going to give, here's what this module does not cover and, where applicable, where it is covered:

It doesn't teach programming or how to use AI APIs. That comes in the AI Engineering Bootcamp (for example, Week 2: Python + LLM Access) and in guides like "Python + REST APIs for AI" or "LLM Access Strategies". Here we only lay the conceptual foundations.
It doesn't get into math (linear algebra, calculus, statistics). The guide is conceptual; there are no formulas or derivatives. If in the future you want to go deeper into how neural networks work at a mathematical level, there are dedicated courses and books; this module stays at the idea and the high-level architecture (which you'll see in Modules 3, 4 and 5 without formulas).
It doesn't go deep into ethics or regulation beyond mentioning them. Topics like bias, privacy, military use or regulation (for example, the AI Act) have their own guides and courses. Here we just want you to have the map of what AI is and what types there are; ethics and regulation build on that map but aren't developed here.
It doesn't compare specific models (GPT-4 vs Claude vs Llama, etc.). That's Module 5 (LLMs) and Module 6 (API ecosystem). In this module we only say that large language models exist and that they're narrow AI and often GenAI; the names and comparisons come later.
It doesn't explain how they work inside — neither neural networks nor LLMs (architecture, attention, tokens, etc.). That's Modules 3 (neural networks without math), 4 (Transformers) and 5 (LLMs). Here we only give the definitions and the classification so that when you reach those modules you don't get lost.

This module only organizes ideas and gives you the map. The "how it's built" and the "how it's used" (code, APIs, design) come in later modules and guides.

Where each topic IS covered (quick reference):

What this module does NOT cover	Where it is covered
Programming or using AI APIs	AI Engineering Bootcamp (Week 2: Python + LLM Access); guides "Python + REST APIs for AI", "LLM Access Strategies"
Math (algebra, calculus, statistics)	ML/DL courses and books; Modules 3, 4, 5 of this guide give architecture without formulas
Ethics or regulation in depth	Dedicated guides and courses (AI Ethics, AI Act, etc.)
Comparing specific models (GPT-4 vs Claude, etc.)	Module 5 (LLMs) and Module 6 (API ecosystem) of this guide
How networks or LLMs work inside	Modules 3 (neural networks), 4 (Transformers), 5 (LLMs) of this guide

Evidence of success: how you know you're ready

You know you've made good use of the module when:

You can explain in 2–3 sentences the difference between AI, ML, Deep Learning and Generative AI without mixing them up.
You can place, broadly, the history of AI in time: Turing, winters, expert systems, the rise of ML and Deep Learning, Transformers and LLMs.
You can say what "narrow AI" is and what "AGI" (or "general AI") is and why everything that exists today in products is narrow.
You can give 2–3 examples of what's possible today with AI in products (for example, text generation, recommendations, voice recognition) and 2–3 examples of what's not possible or not reliable (for example, AGI, zero hallucinations, infinite memory).
You've completed the lesson 06 exercise: at least 5 products listed, classified (AI, ML, DL/GenAI when applicable) and with a brief reflection on what the AI does in each one and what limits you've noticed.

If any of these things still isn't clear, go back to the corresponding lesson (02 definitions, 03 history, 04 types, 05 current state, 06 exercise) and review. You don't need to memorize dates or exact numbers; what matters is having the mental map and being able to use it in conversations or reading.

Checklist before moving to Module 2: Before continuing with Machine Learning (Module 2), check that you can do this without looking at the lessons: (1) Explain in one sentence the difference between AI, ML, Deep Learning and Generative AI. (2) Say what "narrow AI" is and what "AGI" is and why everything current is narrow. (3) Give 2 examples of what's possible today with AI in products and 2 examples of what's not possible or not reliable. (4) Have completed exercise 06 with at least 5 products classified. If you fail on (1) or (2), review lessons 02 and 04. If you fail on (3), review 05. If you haven't done (4), do it before moving on; it's what closes the loop of the module.

✓ Quick visual checklist before Module 2:

I can explain in 1 sentence the difference between AI, ML, DL and GenAI
I can say what "narrow AI" is and what "AGI" is (and why everything current is narrow)
I can give 2 examples of what's possible today with AI in products
I can give 2 examples of what is NOT possible or NOT reliable today
I completed exercise 06 with at least 5 products classified

If you checked all 5, you're ready for Module 2. If any is missing, review the corresponding lesson.

How to use this module

If it's your first time with AI: Read lessons 02 to 05 in order; then do 06 calmly. Don't skip 06: the exercise forces you to apply the concepts and connect them to real products, and that fixes the ideas much better.
If you already have some context (for example, you've used ChatGPT or heard about machine learning): You can skim 02 (definitions) to make sure you're not mixing up terms, and go deeper into 03 (history) and 05 (current state). Even so, don't skip 06: the exercise of classifying products is still useful to organize what you "already knew" with precise vocabulary.
If you're going to do the AI Engineering Bootcamp: This module is the recommended basis for Week 1 (Intro to AI Engineering). Having it done beforehand saves time and avoids misunderstandings when the bootcamp talks about "models", "LLMs" or "AI APIs". If you don't have time to do the whole module, at least read lessons 02 (definitions) and 05 (current state) and do a short version of exercise 06 (2–3 products instead of 5).
If you get stuck on a lesson: Lessons 02 and 04 are the densest in definitions. If in 02 you struggle to remember the AI → ML → DL → GenAI pyramid, use the "Quick comparison" table and the "How to explain it in one sentence" section as a cheat sheet. If in 04 you struggle with narrow vs general, think "everything that exists today in products = narrow" and "AGI = doesn't exist, it's hypothetical". For 06, if you can't think of 5 products, start with the ones you use every day: email, social media, maps, music, search, voice assistants, work tools (Word, Google Docs, etc.); almost all of them have some AI component.

If you get stuck: where to look for help: (1) In lesson 02, if you mix up AI and ML, reread the "The pyramid" section and the "Quick comparison" table; the most common mistake is treating AI and ML as synonyms. (2) In lesson 03, if you get lost in dates, don't worry; what matters is the thread: rules → expert systems → ML → DL → Transformers → LLMs. (3) In lesson 04, if narrow vs general confuses you, remember: narrow = one or a few tasks done very well (everything current); general = any human intellectual task (doesn't exist today). (4) In lesson 05, if you don't know what's "possible today", reread the "What's possible today" and "What's not possible" sections; they're concrete lists. (5) In 06, if you can't think of products, look at the full sample answer (Spotify, Google Search, ChatGPT, Waze, Instagram) and adapt it with products you use.

Each lesson ends with a brief summary and additional resources to go deeper. The exercises in lessons 02 to 05 have solutions or answer guides in <details>; those in 06 have a full sample answer and a self-assessment guide. Use all of that to check your reflection and confirm that you're applying the concepts consistently.

Frequently asked questions

Do I have to memorize dates or exact names?
No. What matters is the thread: Turing and the origin of the term, AI winters, the shift from rules to data (ML), then deep networks (DL), then Transformers and LLMs. If you remember "1950s origin, 1970s and 1980s winters, 2010s Deep Learning, 2017 Transformers, 2022+ LLMs and ChatGPT" that's enough; the exact dates aren't the goal.

Is this module useful for someone who doesn't want to program?
Yes. It's designed for anyone who wants to understand what AI is, how it's classified and what's possible today. If your role is product, business, design or communication, this module gives you the vocabulary and the map without requiring code.

Can I skip lesson 03 (history)?
You can, but it's not recommended the first time. The history explains why today we talk so much about LLMs and why before we talked about "expert systems" or "classic machine learning"; without that, modules 4 and 5 can seem like a list of acronyms with no context.

Is exercise 06 mandatory?
To get good use out of the module, yes. Identifying and classifying products with the module's concepts is what closes the loop: you go from "reading definitions" to "applying them". If you have very little time, do a short version (2–3 products instead of 5) rather than skipping it entirely.

What do I do if an additional resource (link) is broken or in another language?
The resources are mostly in English (documentation, Stanford, the global course platform). If a link is broken, search for the resource's title or the course's name on Google; they're usually updated at another URL. If you prefer only material in Spanish, lesson 02 and the following ones have enough content in the text; the additional resources are optional for going deeper.

Can I do the module over several days?
Yes. You can do 01 and 02 one day, 03 and 04 another day, and 05 and 06 another. Ideally, don't leave too much time between 02 (definitions) and 06 (exercise), because the exercise applies those definitions; if many weeks pass, review 02 before 06.

Does this module have a certificate or assessment?
This guide doesn't issue a certificate; the assessment is yours (Evidence of success, Checklist before moving to Module 2, exercise 06). If you're doing the AI Engineering Bootcamp, this module is supporting material; the bootcamp's assessment is independent.

What to expect from each lesson (in detail)

So you know exactly what you take away from each one:

Lesson 02 (Definitions): You'll leave with a clear map: AI is the umbrella, ML is a subset (learning from data), DL is a subset of ML (deep networks), GenAI is a use (generating content) that usually relies on DL. You'll learn not to say "AI" and "ML" as if they were the same and not to treat "DL" and "ML" as opposites. It includes classification exercises (is this system AI, ML, DL or GenAI?) with solutions in <details>.
Lesson 03 (History): You'll see the thread from Turing and the Dartmouth Workshop to the AI "winters" (less funding, more skepticism), expert systems, the turn toward "learning from data" (ML), the Deep Learning boom and the era of Transformers and LLMs. You don't need to memorize dates; what matters is understanding why today we talk so much about language models and why before we talked about other things (rules, expert systems, classic ML).
Lesson 04 (Types): You'll learn to classify systems into narrow AI (what exists today: one or a few tasks done very well) vs general AI or AGI (hypothetical: any human intellectual task). Also weak vs strong AI (simulation vs real consciousness/understanding). It will help you not confuse what there is (narrow, weak) with what's debated for the future (general, strong) and to read news or job postings precisely.
Lesson 05 (AI in 2024-2026): You'll have a realistic view of the present: what's possible today (generating text and images, recommending, translating, recognizing voice, assisting with code, etc.) and what's not possible or not reliable (AGI, zero hallucinations, infinite memory, total replacement of experts without supervision). It includes trends (cheaper models, RAG, agents, regulation) and how to read news and announcements with a critical eye.
Lesson 06 (Exercise): You'll apply everything above: you'll list at least 5 products you use that incorporate AI, classify them (AI, ML, DL/GenAI when applicable), describe in one sentence what the AI does in each and, for 3 of them, answer what would happen without AI, whether it's narrow or not and what limits you've noticed. Optionally you'll reflect on what AI can do today that it couldn't a few years ago. It includes a full sample answer and a self-assessment guide.

With this you have the complete map of the module before starting lesson 02.

Common mistakes when starting this module

Believing that "AI" and "ML" are the same: They're not. ML is a type of AI (learning from data). There can be AI without ML (fixed rules). In lesson 02 you'll see it in detail; don't skip that lesson.
Looking for code or programming exercises: This module is conceptual; there's no code or installations. The exercises are reflection and classification (definitions, types, products). If you're looking to program with AI APIs, that comes in the bootcamp and other modules.
Memorizing exact dates and names: No need. What matters is the thread (Turing → winters → expert systems → ML → DL → Transformers → LLMs). The exact dates (1950, 1974, 2017, 2022, etc.) are reference, not goal.
Skipping exercise 06: The exercise forces you to apply the concepts to real products; without that, the definitions are forgotten fast. Even if you're in a hurry, do at least a short version (2–3 products instead of 5).
Thinking that "narrow AI" means "not very capable": Narrow means "limited in scope" (one or a few tasks), not "dumb". ChatGPT is narrow AI and is very capable at language. In lesson 04 you'll see it in detail.

Summary

This is Module 1 of the AI Fundamentals guide (Phase 1: Conceptual foundations).
The goal is to define and classify AI, place it in history and in the present, without code or math.
There are 6 lessons: introduction (this one), definitions, history, types, current state and a practical exercise identifying AI in products.
What you learn here is the foundation for Machine Learning, Transformers, LLMs, APIs and the AI Engineer role in the following modules.
It does not cover programming, math, deep ethics or model comparisons; that's covered in other modules or guides.
You know you're ready when you can explain AI vs ML vs DL vs GenAI, narrow vs general, and what's possible today; and when you've completed exercise 06 with at least 5 products classified.
The 3-phase structure (Foundations → Modern architecture → Application and role) builds progressively; this module is the entry point.
Before moving to Module 2, check that you meet the "Evidence of success" and the "Checklist before moving to Module 2" from this lesson.

Summary of the full guide (8 modules)

So you have the full picture before starting:

Modules 1-3 (Phase 1): What AI is, what ML is, what neural networks are (conceptual). No code.
Modules 4-6 (Phase 2): Transformers, LLMs (GPT, Claude, etc.), API ecosystem. How they work and how to access them.
Modules 7-8 (Phase 3): What AI Engineering is, how it differs from ML/Data Science, and your first AI system design.

This module (Module 1) is just the first piece; modules 2 and 3 complete Phase 1. You don't need to memorize the list of 8 modules; knowing there are 3 phases (foundations → modern architecture → application and role) is enough to not get lost.

How to use this introduction if you're short on time

If you're very short on time and want to go straight to the content: read at least the "Module goal" section (what you'll be able to do by the end), the "Module roadmap" table (what you'll see in each lesson) and the "Limits" section (what this module doesn't do). With that you have the minimum to not expect something we're not going to give and to know what you'll gain in each lesson. The rest of this introduction (context, connection with the guide, FAQ, etc.) you can read when you have more time or when a question comes up.

If you have normal time (2 hours for the full module): read this whole introduction once; it'll help you not lose the thread when you reach lesson 02 and to know why each lesson is where it is. Then continue in order: 02 → 03 → 04 → 05 → 06.

Next step

When you finish reading this introduction, move on to Lesson 02: Definitions of AI. There you'll untangle the most common confusion: what exactly "artificial intelligence", "machine learning", "deep learning" and "generative AI" are, and why they aren't the same. With that you'll have the map of terms you'll use throughout the rest of the guide.

How to know if you're ready for lesson 02:

You're clear on what the goal of Module 1 is (define, classify, place in history and present, without code).
You know what each lesson covers (02 definitions, 03 history, 04 types, 05 current state, 06 exercise).
You understand that modules 1-3 are conceptual foundations and that modules 4-8 build on them (Transformers, LLMs, APIs, AI Engineering, design).
You know what this module does NOT cover (programming, math, deep ethics, comparisons of specific models) and where each thing is covered.

If any of these things still isn't clear, review the corresponding section of this introduction before continuing.

Additional resources

Artificial Intelligence: A Modern Approach (Russell & Norvig) — Reference book; chapter 1 is a good expansion of "what is AI" and of the definitions you'll see in lesson 02. In English.
Stanford Encyclopedia of Philosophy: Artificial Intelligence — Definitions and philosophical debate (weak vs strong AI, consciousness, etc.). Useful if you want to go deeper into lesson 04 (types of AI). In English.
The State of AI Report (2024) — Annual view of the state of the field: investment, models, trends. Complements lesson 05 (AI in 2024-2026). Updated every year; search for "State of AI Report" if the link changes. In English.
AI For Everyone (Coursera) — Andrew Ng's non-technical introductory course; covers what AI is, what it can and can't do, and strategy in companies. Complements this module if you want more business context and fewer formal definitions. In English with subtitles in several languages.
Elements of AI (elementsofai.com) — Free course in several languages (including Spanish) on the fundamentals of AI; the first chapter has exercises similar to our lesson 06 (identifying AI in your environment). Useful for practicing more classifications.

If something doesn't add up

If you think "AI" and "ML" are the same: Lesson 02 clarifies it with the pyramid (AI → ML → DL) and the comparison table. ML is a subset of AI; not all AI is ML.
If you don't know what "narrow" or "AGI" is: Lesson 04 explains it. Narrow = one or a few tasks done very well (everything current). AGI = hypothetical general intelligence (doesn't exist today).
If you get lost in the history (lesson 03): No need to memorize dates. Keep the thread: rules → expert systems → ML → DL → Transformers → LLMs. That's enough for the rest of the guide.
If exercise 06 seems vague: Use the full sample answer (Spotify, Google Search, ChatGPT, Waze, Instagram) as a template. List 5 products you use, classify them with the definitions from 02 and answer the three questions for 3 of them. There's no single correct answer; what matters is using the concepts consistently.

With this you have everything you need to start lesson 02. When you finish the whole module, come back to this introduction if you want to review the map (goals, roadmap, connection with the guide, limits).

In one sentence: what each thing is (cheat sheet to review): AI = systems with intelligent behavior (perceiving, reasoning, learning, acting). ML = AI that improves with data (subset of AI). DL = ML with deep neural networks (subset of ML). GenAI = AI that generates new content (text, images, etc.), often via DL. Narrow AI = one or a few tasks done very well (everything current). AGI = hypothetical general intelligence (doesn't exist today). When you finish lesson 02 and 04, this cheat sheet will help you review before exercise 06.

Next →2. Definitions of AI: AI vs ML vs DL vs GenAI