Hello, I’m Mana.
Today, I’d like to explain three essential technologies at the heart of generative AI: foundation models, transformers, and attention mechanisms. These might sound technical, but I’ll break them down simply so we can learn together!
🧱 What is a Foundation Model?
A foundation model is a general-purpose AI system trained on large-scale data that can be adapted for many tasks like conversation, translation, summarization, and even image generation.
- 📚 Trained on massive datasets from the internet
- 🛠️ Can be customized for specific tasks using fine-tuning or prompt engineering
- 🧠 Examples: GPT-3/4, Claude, Gemini, PaLM, etc.
This represents a major shift from building separate models for each task to using one versatile model for many applications.
🔁 What is a Transformer?
The transformer is a neural network architecture that powers most modern generative AI systems. It was introduced in a 2017 paper by Google called “Attention is All You Need.”
Key features:
- 📖 Processes entire sentences at once (unlike older sequential models like RNNs)
- ⚡ Supports parallel processing, allowing faster training
- 🧩 Uses encoder-decoder architecture for flexibility across tasks
This structure allows models to handle long and complex contexts more effectively than older methods like RNNs and LSTMs.
🎯 What is Attention?
The core mechanism inside a transformer is called attention.
In simple terms…
It’s a system that calculates which words in a sentence are most relevant to each other, helping the model focus on important parts of the input.
Example: “He stopped in front of the bank.”
Depending on the context, “bank” could mean a financial institution or a riverbank. The attention mechanism considers surrounding words like “money” or “stood” to guess the correct meaning.
How it works:
- 🔁 Calculates the relationship (weight) between all word pairs in the input
- 👀 Focuses more on the important words (Self-Attention)
This significantly improves the model’s ability to understand context and meaning in complex sentences.
📘 Conclusion
Understanding the key components behind generative AI—foundation models, transformers, and attention—gives us a clearer picture of how these powerful tools work.
Rather than memorizing terms, it’s helpful to think about why these systems are needed and how they function in real-world AI applications.
Let’s continue exploring and learning about AI together! 📘
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