TL;DR

Different AI tasks need different architectures. Transformers dominate language and sequence tasks, CNNs excel at images, diffusion models generate images, and specialized architectures handle audio, video, and more.

Major AI architectures

Transformers (2017-present):

  • Best for: Language, sequences
  • Examples: GPT, BERT, Claude
  • Key feature: Attention mechanism
  • Dominates NLP

Convolutional Neural Networks (CNNs):

  • Best for: Images, spatial data
  • Examples: ResNet, VGG, EfficientNet
  • Key feature: Convolution layers detect patterns
  • Used in: Image classification, object detection

Diffusion models:

  • Best for: Image generation
  • Examples: Stable Diffusion, DALL-E
  • Key feature: Iteratively denoise random pixels
  • Creates high-quality images

Recurrent Neural Networks (RNNs/LSTMs):

  • Best for: Sequential data (legacy)
  • Mostly replaced by transformers
  • Still used in some time-series tasks

Graph Neural Networks:

  • Best for: Networked data
  • Examples: Social networks, molecules
  • Learns from graph structures

Transformer architecture deep-dive

Components:

  • Input embeddings
  • Positional encoding
  • Multi-head attention
  • Feed-forward layers
  • Output layer

Why transformers won:

  • Process text in parallel (fast)
  • Handle long-range dependencies
  • Scale well with data and compute
  • Transfer learning works great

CNN architecture overview

Layers:

  • Convolutional layers (detect features)
  • Pooling layers (reduce size)
  • Fully connected layers (classification)

Use cases:

  • Image classification
  • Object detection
  • Face recognition
  • Medical imaging

Diffusion model process

  1. Start with random noise
  2. Gradually remove noise (denoise)
  3. Guided by text prompt
  4. Result: High-quality image

Advantages:

  • Extremely high quality
  • Diverse outputs
  • Can be controlled precisely

Encoder vs decoder models

Encoders (BERT-style):

  • Understand and classify text
  • Good for: Sentiment analysis, Q&A

Decoders (GPT-style):

  • Generate text
  • Good for: Writing, chatbots

Encoder-decoder (T5, BART):

  • Both understand and generate
  • Good for: Translation, summarization

Multi-modal models

  • Process multiple types of data (text + images)
  • Examples: GPT-4V, Gemini, CLIP
  • Can: Describe images, answer visual questions
  • Future: Video, audio, sensors

Model size trade-offs

Small models (< 1B parameters):

  • Fast
  • Cheap
  • Less capable

Medium (7B-70B):

  • Good balance
  • Most common for deployment

Large (100B+ parameters):

  • Most capable
  • Expensive
  • Slow

What&#39;s next