Artificial intelligence (AI) is a branch of computer science focused on designing and developing systems capable of performing tasks that traditionally require human intelligence. These tasks encompass learning from data, reasoning, problem-solving, natural language understanding and generation, perception (vision and hearing), planning, and decision-making.

AI systems operate through several technical approaches: symbolic AI uses explicit rules and logic for formal reasoning; machine learning enables systems to learn patterns from data through supervised, unsupervised, and reinforcement learning paradigms; deep learning leverages multi-layered neural networks and has proven particularly effective for perception and language tasks. Modern AI, especially generative models, can create text, images, and code.

Current AI is predominantly narrow (or weak) AI—systems designed for specific tasks such as image classification, speech recognition, content recommendation, fraud detection, medical diagnosis support, and logistics optimization. General AI (AGI), which would possess broad human-level cognitive abilities across diverse domains, remains theoretical and does not yet exist. Superintelligence, surpassing human capabilities across most domains, is even more speculative.

Despite significant achievements, AI faces substantial challenges: lack of common-sense reasoning and genuine understanding, brittleness when encountering data outside training distributions, inherited bias from training data, requirements for large computational resources and extensive datasets, and persistent interpretability and safety concerns. These technical limitations highlight why current AI, while capable, remains far from human-like general intelligence.

Beyond technical dimensions, AI development raises critical ethical and societal questions including privacy protection, fairness and non-discrimination, accountability for AI decisions, workforce impacts through job displacement or transformation, and safety in high-stakes applications. Responsible AI development requires fairness auditing, transparency mechanisms, human oversight, and regulatory frameworks appropriate to risk levels.