The AI Architecture Landscape
Why are there so many different AI models? Understanding the big picture before diving into specifics.
Why So Many AI Architectures?
If you've tried to learn about AI, you've probably encountered a confusing alphabet soup: RNNs, LSTMs, CNNs, GPT, GANs, GNNs. Each sounds technical and intimidating. But here's the truth—each architecture exists because it solves a specific problem better than the others.
Think of AI architectures like tools in a toolbox. You wouldn't use a hammer to cut wood or a saw to drive nails. Similarly, you wouldn't use a CNN to generate text or a transformer to analyze molecular structures. The key is understanding which tool fits which job.
The Six Core Architectures
In practice, six architectures power roughly 90% of all deployed AI systems today:
| Architecture | Full Name | Primary Strength |
|---|---|---|
| RNN | Recurrent Neural Network | Sequential data processing |
| LSTM | Long Short-Term Memory | Long sequences with memory |
| CNN | Convolutional Neural Network | Image and visual analysis |
| Transformer | Transformer (Attention) | Language understanding |
| GAN | Generative Adversarial Network | Image and video generation |
| GNN | Graph Neural Network | Relationship and network data |
The Evolution Mindset
Here's what makes learning AI architectures easier: they evolved in response to limitations. LSTMs fixed what RNNs couldn't do. Transformers outperformed LSTMs for language. Each generation solved the previous generation's weaknesses.
1980s: Basic Neural Networks emerge
1990s: RNNs tackle sequences, LSTMs solve memory
1998: CNNs revolutionize image processing
2014: GANs introduce adversarial generation
2017: Transformers change everything with attention
2020s: GNNs gain traction for graph data
In the following chapters, we'll walk through this evolution story. By understanding why each architecture was created, you'll naturally understand when to use it.
Don't try to memorize architectures. Instead, understand the problems they solve. The right architecture becomes obvious when you clearly define your problem.