- Deep Learning: Hierarchical neural networks learning complex representations automatically.

- Neural Networks: Layered architectures efficiently model nonlinear relationships accurately.

- NLP: Techniques to process and understand natural language text.

- Computer Vision: Algorithms interpreting and analyzing visual data effectively

- Reinforcement Learning: Distributed traffic across multiple servers for reliability.

- Generative Models: Creating new data samples using learned data.

- LLM: Generates human-like text using massive pre-trained data.

- Transformers: Self-attention-based architecture powering modern AI models.

- Feature Engineering: Designing informative features to improve model performance significantly.

- Supervised Learning: Learns useful representations without labeled data.

- Bayesian Learning: Incorporate uncertainty using probabilistic model approaches.

- Prompt Engineering: Crafting effective inputs to guide generative model outputs.

- AI Agents: Autonomous systems that perceive, decide, and act.

- Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks.

- Multimodal Models: Processes and generates across multiple data types like images, videos, and text.

- Embeddings: Transforms input into machine-readable vector formats.

- Vector Search: Finds similar items using dense vector embeddings.

- Model Evaluation: Assessing predictive performance using validation techniques.

- AI Infrastructure: Deploying scalable systems to support AI operations.

Are there any other AI concepts you would add to the list?