Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of living beings, primarily of humans. It is a field of research in computer science that develops and studies methods and software which enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.
Such machines may be called AIs. Some high-profile applications of AI include advanced web search engines (e.g., Google Search); recommendation systems (used by YouTube, Amazon, and Netflix); interacting via human speech (e.g., Google Assistant, Siri, and Alexa); autonomous vehicles (e.g., Waymo); generative and creative tools (e.g., ChatGPT, and AI art); and superhuman play and analysis in strategy games (e.g., chess and Go).
Large language models (LLMs) are a type of AI trained on enormous amounts of text data. GPT-4, developed by OpenAI, is one of the most capable LLMs, able to generate coherent text, write code, and answer complex questions. PaLM, developed by Google, demonstrates strong reasoning and multilingual capabilities. LLaMA, released by Meta, is an open-weights model that has enabled widespread research and experimentation. Claude, developed by Anthropic, emphasizes safety and helpfulness.
These models use transformer architectures with self-attention mechanisms to learn relationships between tokens in text. During training, the models predict the next token in a sequence, allowing them to learn grammar, facts, and reasoning patterns from vast datasets. Fine-tuning and reinforcement learning from human feedback (RLHF) are used to align model behavior with human preferences.
The capabilities of modern AI systems have grown dramatically. In natural language processing, models can now perform question answering, summarization, translation, and creative writing at near-human levels. In computer vision, deep learning models achieve superhuman accuracy on image classification benchmarks. Multimodal models that process both text and images are becoming increasingly powerful.
AI safety and alignment research focuses on ensuring that AI systems behave as intended and do not cause unintended harm. Researchers at organizations like Anthropic, DeepMind, and OpenAI are working on interpretability, robustness, and value alignment. The field grapples with challenges such as avoiding harmful outputs, ensuring factual accuracy, and maintaining consistent behavior across diverse inputs.
The economic and social impacts of AI are profound. Automation may displace certain jobs while creating new categories of work. AI-powered tools are accelerating scientific discovery in domains such as drug development, materials science, and climate modeling. Policymakers around the world are developing regulations to govern AI deployment, balancing innovation with risk mitigation.