how they created IA

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 Creating IA (Artificial Intelligence) involves a combination of mathematics, computer science, neuroscience, and data. Here's a simplified overview of how AI is created:

 1. Defining the Goal

Creators first decide what they want the AI to do. Examples:

  • Recognize images or speech

  • Translate languages

  • Play a game

  • Chat with humans (like me!)

 2. Choosing or Designing a Model

The "model" is a mathematical structure that learns patterns. The most common today is:

  • Neural Networks – inspired by the human brain

    • Simple ones: perceptrons

    • Advanced ones: deep learning models (like GPT, used in ChatGPT)

 3. Feeding It Data

AI learns from huge amounts of data:

  • Text, images, videos, audio, etc.

  • For example, ChatGPT was trained on massive amounts of written text from the internet.

 4. Training the Model

This is like teaching:

  • The model is shown input (e.g., a sentence)

  • It makes a guess (e.g., the next word)

  • If it’s wrong, it gets corrected

  • This process repeats millions or billions of times

Training uses:

  • Backpropagation (to correct mistakes)

  • Gradient descent (to adjust how the model learns)

  • Massive computational power (using GPUs or TPUs)

 5. Testing and Improving

Once trained, the model is tested on new data to see how well it performs. If it makes too many mistakes, it's:

  • Adjusted

  • Retrained

  • Or made more complex

 6. Deployment

When it’s working well, it’s deployed into:

  • Websites

  • Apps

  • Robots

  • Or embedded into devices

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