“AI,” “Machine Learning,” “Deep Learning,” “LLMs,” “Generative AI”… These words are on everyone’s lips, but do we really know what they mean? 🤔
An anecdote: Whether it’s internally (for my day job as a lawyer) or during client meetings (for my night job as an entrepreneur), I often hear the term “AI” in reference to ChatGPT. But when I ask my counterpart what the difference is between AI, Machine Learning, and Deep Learning, the answer is often “I don’t know” or “ChatGPT.”
So let’s set the record straight. 🚀
Artificial intelligence is not new. In fact, for nearly a century, it has evolved in stages, from simple algorithms to models capable of generating text and images. To understand where we are today, let’s take a trip through time. ⏳
🏛️ The Origins of AI: Algorithms (1930-1950)
Even before we talked about AI, it all started with algorithms: sets of instructions to solve a problem. Like a recipe, where each step follows a precise logic.
The first computers of the 1940s-1950s already used these algorithms to perform complex calculations. This mathematical foundation remains the cornerstone of all artificial intelligence today.
🤖 The Birth of AI: The Era of Pioneers (1950-1960)
Alan Turing posed a key question in 1950: Can a machine think? He invented the Turing Test, designed to measure if a machine could imitate human intelligence.
A few years later, in 1956, the term “Artificial Intelligence” was officially introduced at the Dartmouth Conference. This marked the beginning of an ambitious technological quest.
⚙️ Automation: The Foundation of the Future (1960-1980)
The first so-called “intelligent” programs appeared, but they were actually rule-based systems (IF this, THEN that).
👉 Example: The first industrial robots, capable of following pre-programmed instructions, marked the entry of AI into the workplace.
🧠 Machine Learning: AI That Learns (1980-2000)
A major revolution: instead of programming a machine with fixed rules, it was given the ability to learn from data.
Much like a child learning to recognize a cat not by memorizing a definition, but by seeing hundreds of images and deducing the characteristics themselves.
💡 Concrete Applications: spam filters, purchase recommendations, weather forecasts.
🔍 Deep Learning: AI That Reasons in Layers (2000-2015)
Inspired by the human brain, Deep Learning relies on artificial neural networks.
Imagine a multi-layered cake: each layer processes information differently, allowing for a finer and more detailed understanding of data.
💡 Concrete Applications: facial recognition, voice assistants, automatic translation, autonomous vehicles.
📚 LLMs: AI That Understands and Generates Text (2015-Present)
Large Language Models (LLMs) like ChatGPT, DeepSeek, or Mistral are trained on billions of texts and can write articles, answer questions, or even hold a fluent conversation.
But beware: these models do not “think.” They simply predict the most probable words based on context. No AI knows if it is telling the truth or not.
🎨 Generative AI: AI That Creates (2020-Present)
DALL·E, Midjourney, Runway… Generative AI is the latest leap forward.
It no longer just understands; it produces original content (in the scientific sense of the term): text, images, videos, music. Then, it is we, the users, who see meaning, interest, or not.
👉 Example: DALL·E creates images from textual descriptions, while other models generate music or animate videos from simple prompts.
❗ So… What is ChatGPT?
ChatGPT is an LLM that relies on Machine Learning, using Deep Learning algorithms to generate text.
👉 It does not think. It does not understand like a human. It simply anticipates the logical continuation of a sentence.
🚘 An Example: The Autonomous Car
It combines several branches of AI:
✅ Machine Learning: to analyze sensor data.
✅ Deep Learning: to identify pedestrians and traffic signs.
✅ LLMs: to understand the driver’s voice commands.
✅ Generative AI: to simulate learning scenarios.
🤯 Conclusion: AI is Everywhere, but AI is Not ChatGPT
Behind the buzz, artificial intelligence is a set of interconnected technologies.
Do you now understand why saying “ChatGPT = AI” is an oversimplification?
✍️ If this post helped you better understand AI and its algorithmic foundations, share it with your colleagues who still confuse ChatGPT with AI! 😉
