Architectures and the buzzwords that matter
Governance pros must hold a credible conversation about architectures: transformer models (process inputs in parallel), multimodal models/LMMs (WHO 2024 ethics guidance), generative and specialised networks (CNN/RNN/GNN), and RAG for pulling in external information.
You will not build these, but you must hold a credible conversation about them to assess risk.
Transformer models (foundation models):
- Deep learning that learns context and meaning by tracking relationships in sequential data (words in a sentence)
- Find patterns mathematically → no need for large labelled datasets
- Process inputs in parallel → efficient training and inference
- Enable modern NLP and multimodal models
- Bonus uses → protein sequencing for medications, DNA sequencing
Multimodal models (LMMs):
- Inputs and outputs across image, video, audio and text (unimodal = one modality)
- NLP is a key component
- Use cases → weather forecasting, medical diagnoses, code generation
- WHO released AI ethics guidance for LMMs in 2024 → concerns about inaccurate or biased output affecting health decisions, poor training data, patient privacy
- Tools → Gemini, ChatGPT, ImageBind (Meta), Inworld AI
Other architectures: generative architectures create new text, images, audio or code from learned patterns (GPT, LLaMA, DALL-E 2). Specialised networks → know the acronyms: CNN (convolutional, images), RNN (recurrent, sequences), GNN (graph). RAG → retrieval-augmented generation lets a GenAI system pull in external information when answering, boosting LLM accuracy and relevance.
Key terms - quick answers
What is “Transformer models”?
What is “Multimodal models (LMMs)”?
What is “CNN”?
What is “RNN”?
Sources and study method
This independent lesson uses active recall, spaced retrieval and scenario practice. Read the full study method.