Welcome to AIWiki Malaysia
The free AI knowledge base — with Malaysian context
AIWiki is a free, curated reference for artificial intelligence — written for practitioners, students, and decision-makers in Malaysia. Every article includes a Malaysian Context section covering local regulations, MDEC initiatives, homegrown vendors, and real-world case examples.
Featured Articles
AI Ethics
AI ethics is the branch of applied ethics addressing the moral dimensions of designing, deploying, and governing artificial intelligence systems — covering fairness, accountability, transparency, privacy, and safety.
AI Regulation in Malaysia
An overview of the Malaysian regulatory landscape governing artificial intelligence — covering PDPA, sectoral guidelines, national AI policy, and Malaysia's approach to the global AI governance debate.
Artificial Intelligence
Artificial intelligence (AI) is the simulation of human intelligence processes by computer systems, encompassing learning, reasoning, problem-solving, perception, and language understanding.
Articles
3D Gaussian Splatting
A scene representation and rendering technique that models a 3D scene as millions of coloured 3D Gaussians, enabling high-quality, real-time novel view synthesis.
A/B Testing (ML)
A/B testing in machine learning is a controlled experiment method that compares two or more model variants in production to determine which delivers superior performance on real-world business metrics.
Activation Function
A mathematical function applied to a neuron's output in a neural network that introduces non-linearity, enabling models to learn complex patterns beyond simple linear relationships.
Active Learning
Active learning is a machine learning paradigm in which the algorithm selectively queries a human annotator for labels on the most informative data points, minimising labelling effort while maximising model performance.
Adam Optimizer
Adam is an adaptive gradient-based optimization algorithm for training neural networks that combines momentum with per-parameter adaptive learning rates derived from estimates of the first and second moments of the gradients.
Adversarial Machine Learning
Adversarial machine learning is the study of attacks that exploit weaknesses in machine learning models, such as crafted inputs that cause misclassification, and of the defences designed to make models more robust.
Agent2Agent Protocol (A2A)
An open protocol that lets independent AI agents built on different frameworks discover one another, exchange messages, and delegate tasks over a standard interface.
Agentic AI
Agentic AI refers to artificial intelligence systems designed to pursue goals autonomously over extended time horizons by perceiving their environment, reasoning about actions, executing multi-step plans, and learning from outcomes without requiring continuous human instruction.
Agentic RAG
Agentic RAG is an approach to retrieval-augmented generation in which autonomous AI agents dynamically decide when, what, and how to retrieve information, applying planning, reflection, and tool use rather than following a fixed retrieve-then-generate pipeline.
AI Agents
Autonomous AI systems that perceive their environment, reason over goals, select and execute actions using external tools, and operate across multi-step tasks with minimal human intervention.
AI Alignment
AI alignment is the field of research dedicated to ensuring that artificial intelligence systems pursue goals, values, and behaviours that are consistent with human intentions.
AI Automation
AI automation combines artificial intelligence with robotic process automation and workflow orchestration to eliminate repetitive tasks, reduce errors, and free human workers for higher-value work.