AGI (Artificial General Intelligence)
Artificial General Intelligence (AGI) refers to AI systems with human-level cognitive capabilities—the ability to understand, learn, and apply knowledge across multiple domains without task-specific programming.
AI Democratization
AI democratization is the movement to make AI tools and resources accessible to non-technical users across organizations.
AI Ethics-by-Design
Ethics-by-design is an approach that embeds fairness, accountability, and transparency into AI development from the earliest stages of design.
AI-Orchestrated Workflows
AI-Orchestrated Workflows use intelligent automation to coordinate multiple systems, processes, or AI agents toward achieving business outcomes efficiently.
AIaaS (AI as a Service)
AI as a Service delivers prebuilt AI tools and APIs in the cloud, enabling organizations to integrate advanced AI capabilities without developing models internally.
AIOps
AIOps (Artificial Intelligence for IT Operations) uses machine learning and big data to automate and enhance IT management tasks like event correlation, anomaly detection, and root-cause analysis.
API (Application Programming Interface)
An API is a set of defined rules and protocols that allow different software systems to communicate and share data or functionality securely and efficiently.
ASI (Artificial Superintelligence)
Artificial Superintelligence (ASI) refers to AI systems that surpass human intelligence in creativity, reasoning, and problem-solving.
AutoML (Automated Machine Learning)
AutoML automates the process of selecting algorithms, tuning hyperparameters, and optimizing models, reducing the need for manual intervention.
Cognitive Computing
Cognitive computing mimics human reasoning and learning by combining AI, natural language processing, and data analytics to support decision-making.
Digital Twin
A Digital Twin is a real-time virtual model of a physical object, system, or process used for monitoring, simulation, and optimization.
Edge Intelligence
Edge Intelligence combines AI and edge computing to process and analyze data locally on devices rather than on centralized servers.
LLMOps
LLMOps (Large Language Model Operations) extends MLOps principles to manage the lifecycle of LLMs, including prompt management, version control, security, and performance optimization.
MLOps
MLOps (Machine Learning Operations) combines machine learning, DevOps, and data engineering practices to automate the end-to-end lifecycle of AI models—from training and deployment to monitoring and retraining.
RAG (Retrieval-Augmented Generation)
RAG combines a large language model (LLM) with an external knowledge retrieval component to ground AI responses in factual, up-to-date data.
RLHF (Reinforcement Learning from Human Feedback)
RLHF is a training technique where AI models learn preferred behaviors by receiving feedback from human evaluators on generated responses.
SHAP / LIME (Explainability Methods)
SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are techniques that explain how AI models make predictions by identifying which features influence outcomes.








