The History of AI

Turing frames the question

Alan Turing proposes the imitation game, giving computer intelligence a durable testable shape.

AI gets its name

The Dartmouth workshop coins artificial intelligence and launches it as a formal research field.

Conversation becomes visible

ELIZA shows how pattern matching can mimic dialogue, raising early questions about machine understanding.

Expert systems rise

Rule-based programs capture specialist knowledge, proving useful in narrow domains while exposing limits.

AI winters reset expectations

Funding cools as systems disappoint, but research continues in machine learning, robotics, and neural nets.

Deep Blue beats Kasparov

IBM's chess victory becomes a public symbol of specialized machine reasoning at scale.

Deep learning breaks through

AlexNet dramatically improves image recognition, accelerating GPU-driven neural network research.

Transformers change the stack

The transformer architecture makes large-scale language and multimodal models far more capable.

ChatGPT changes the interface

Instruction-tuned LLMs make natural language the primary control surface for writing, coding, search, analysis, and support.

• Conversational prompting lowers the skill barrier for nontechnical users.
• RLHF and safety tuning make models more helpful, steerable, and product-ready.

Frontier LLMs improve reasoning

GPT-4-class systems raise expectations for complex writing, code generation, exam performance, and multimodal understanding.

• Developers begin pairing LLMs with retrieval, private data, and workflow tools.
• Function calling turns free-form chat into structured software integration.

Context windows and multimodality expand

Models handle larger documents, codebases, images, audio, and video, moving from chat assistants toward richer work systems.

• Long-context models make full-file and multi-document analysis practical.
• Omni-style models bring faster voice, vision, and text interaction into one loop.

Reasoning models split from chat models

New model families spend more compute on hard problems, improving math, code, planning, and scientific reasoning.

• Instead of only predicting the next response quickly, they deliberate before answering.
• Evaluation shifts toward multi-step tasks where accuracy matters more than speed.

LLMs become agentic infrastructure

Models increasingly use tools, inspect files, operate browsers, write and test code, and coordinate longer digital workflows.

• Smaller models get cheaper and faster while retaining strong task performance.
• Enterprise use shifts from novelty chatbots to governed assistants embedded in daily operations.