Topics for artificial intelligence

Branches

• Machine Learning: for simple task. (FCL, Bayesian inference,AutoML)

• Deep Learning: for complex data structures. (CNNs, Autoencoders, Capsule Networks)

• Natural Language Processing (NLP): To understand, interpret, and generate human language. This has a wide application on: Sentiment Analysis, Language Translation, Text Summarization and Question Answering. (transformer, BERT, Named Entity Recognition (NER), GPT, GNN, Attention)

• Computer Vision: To interpret and analyze visual data, such as images and video. (Object detection, 3D computer vision, YOLO, Semantic segmentation, Image captioning, Image retrieval, Image repair)

• Robotics: To control robots. (Reinforcement Learning, imitation learning, Vision-based control, Sim-to-real transfer, Multi-agent systems, MPC)

• Explainable AI: AI systems that are transparent and can explain their reasoning to humans. (Model interpretability, Counterfactual explanations, Human-AI collaboration, Explainable deep learning)

• AI Ethics: The study of ethical issues arising from the development and use of AI, such as privacy, bias, and accountability.

• Generative Models: To generate new content, such as images, music, or text. (GANs, VAEs, Auto-regressive models, Flow-based models)

• Reinforcement Learning: To learn based on the interaction of environment. (DQN, A2C, Policy-based, Meta-learning)

• Edge Computing: Model inference on low-powered devices such as smartphones or IoT devices, instead of in the cloud. (Model distillation)

• Transfer Learning: The ability of an AI model to transfer knowledge learned from one task to another, enabling more efficient learning and improved performance.

• Quantum Computing: To use quantum-mechanical phenomena to perform operations, which may enable new breakthroughs in AI.