Description
- Artificial Intelligence Features Summary
- Rule-based automation handles deterministic tasks with simple if/then logic for workflows and validations.
- Data preprocessing cleans and transforms raw inputs into usable features for all models.
- Basic supervised learning (classification and regression) maps inputs to labels or continuous outputs using classical algorithms.
- Feature extraction converts text, images, and signals into numeric representations like embeddings and descriptors.
- Clustering and unsupervised learning discover patterns and segments without labeled data.
- Transfer learning fine-tunes pretrained models to new tasks, reducing data and compute needs.
- NLP pipelines enable tokenization, embeddings, and transformer-based tasks such as summarization and NER.
- Deep learning at scale uses CNNs and transformers for high‑capacity tasks, requiring GPUs/TPUs and large datasets.
- Generative models and LLMs create text, images, and code, enabling content synthesis and assistance.
- Reinforcement learning trains agents via rewards for sequential decision-making in simulated or real environments.
- Multimodal AI fuses text, vision, and audio for richer understanding and interaction.
- Explainability and fairness provide model interpretation and bias mitigation for trustworthy deployment.
- MLOps and monitoring automate training, deployment, drift detection, and lifecycle management.
- Safety, privacy, and compliance require governance, access controls, and privacy-preserving techniques.
- Common limitations include data bias, opacity of deep models, high compute costs, and potential misuse.
- Practical roadmap: define KPIs, start with baselines, build data pipelines, iterate to advanced models, and invest in monitoring and ethics.
