Quanta Magazine’s primer outlines nineteen essential AI concepts—from neural networks and foundation models to generative AI, embeddings, and mechanistic interpretability—providing formal definitions, context, and examples for intermediate readers interested in current AI technologies.
Key points
- Introduces the term 'foundation model' to describe pretrained AI systems adaptable across tasks such as GPT-3 and DALL-E
- Explains embeddings as numerical vector representations capturing relationships between inputs
- Highlights benchmarks like ImageNet and GLUE that drive AI progress and reveal model limitations
- Describes generative AI architectures including transformers and diffusion models powering text and image synthesis
- Outlines mechanistic interpretability efforts to reverse-engineer neural networks’ internal mechanisms and features
Q&A
- What distinguishes a foundation model from other AI models?
- How do AI embeddings work?
- Why do generative AI models hallucinate?
- What is mechanistic interpretability?
