Artificial Intelligence (AI)
The simulation of human intelligence by computer systems, including learning, reasoning, and self-correction. Modern AI is primarily powered by machine learning and neural networks.
Why it matters
AI is no longer a lab curiosity. It quietly powers the spam filter on your email, the shows Netflix recommends, and the chatbot you type questions into. Understanding what AI actually is (and isn't) helps you judge when to trust its output, when to double-check it, and where it can genuinely save you time versus where it's just marketing on an ordinary piece of software.
In practice
Say you upload a photo to your phone and it instantly groups pictures of the same friend together, or you dictate a text and it types the words for you. Those are everyday AI systems learning patterns from huge amounts of examples. Nobody hand-wrote a rule for your friend's face; the system learned it. That same underlying idea scales up to chatbots that write emails and draft plans.
Related terms
Context Window
The maximum amount of text (measured in tokens) an AI model can consider at once. Larger context windows allow the model to reference more information in a single conversation.
Large Language Model (LLM)
An AI model trained on vast amounts of text data that can generate, summarize, translate, and analyze human language. Examples include GPT-4, Claude, Gemini, and Llama.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. Powers chatbots, translation, sentiment analysis, and text summarization.
Prompt
The text instruction you give to an AI model to generate a response. Prompt quality directly impacts output quality — better prompts yield dramatically better results.
Token
The basic unit of text that AI models process — roughly 3/4 of a word in English. 'Unbelievable' is 3 tokens. Token limits determine how much text a model can process at once.
Tokenizer
The component that splits text into tokens (sub-word units) before feeding it to an AI model. Different models use different tokenizers — affecting how they count input length, handle multilingual text, and process code.