Computer (the engine).
A computer supplies the power that makes AI possible: it captures data (sensors, forms, cameras), stores it (files/databases), and provides the processors—CPU/GPU/TPU—that crunch the math behind training and inference. In practice, computers move data through a pipeline: ingest → clean → transform → feed to models → return results to users. Whether on your phone (edge) or in the cloud, the computer’s speed, memory, and storage determine how much data an AI can learn from and how quickly it can respond.
Data (the fuel).
Data is the raw material AI learns from—numbers in tables, text, images, audio, and logs. Before it helps, data must be collected responsibly, cleaned for errors, and organized so models can see consistent patterns. High-quality, representative data lets AI generalize; poor or biased data produces weak or unfair results. During inference, fresh data (your new input) flows through the same computer pipeline so the model can produce a prediction or answer.
Artificial Intelligence (the method).
Artificial Intelligence (AI) transforms data into decisions by training models—algorithms whose parameters are fine-tuned on powerful computers until they can reliably detect patterns (for example, distinguishing spam from non-spam, recognizing faces, or translating languages). Once trained, the model can be deployed on the same or smaller computers to perform inference in real time—such as labeling an email, summarizing text, or recommending a route. The effectiveness of any AI system ultimately depends on two factors: the quality of the data it learns from and the computing power available. Higher-quality data combined with sufficient processing capacity leads to more accurate and faster results.
How they work together (the system).
Think of a loop: computers collect and store data → engineers prepare and label it → computers train AI models on that data → the trained model runs on computers to answer new inputs → user feedback becomes new data that improves the next version. Example: a language assistant ingests your prompt (data), a GPU executes the model (computer), the model generates a response (AI), and your rating feeds back to refine future outputs. Computers move and compute, data informs, AI learns—together they deliver useful, evolving solutions.