Artificial intelligence (AI) refers to the set of techniques that let a machine perform tasks associated with human intelligence: recognizing an image, understanding a text, making a decision, generating content. The term was proposed by John McCarthy for the 1956 Dartmouth conference.
For decades, AI relied mainly on hand-written rules (expert systems). Since the 2010s, it has relied mostly on machine learning: the model learns from examples rather than rules. Deep learning, then Transformer architectures, paved the way for large language models (LLMs) and generative AI.
Today a distinction is drawn between "predictive" AI (scoring, anomaly detection, vision) and "generative" AI (text, code, images). Both coexist in industry, healthcare, defense and finance, as well as in everyday software.
Why it matters when hiring
"AI" covers very different jobs: a researcher who designs models, an ML Engineer who trains them and puts them into production, an AI Engineer who assembles existing LLMs into a product. Before hiring, be clear about whether you build models or use them: it is neither the same talent pool nor the same level of scarcity. On a resume, look for systems shipped to production and measured, not just notebooks or demos.