Updated July 5, 2026
A scroll-led map from Turing's early machine-intelligence work to integrated agents, reasoning models, multimodal systems, and the infrastructure now shaping AI as an operating layer.
1947
1947
Foundations
Alan Turing begins treating machine intelligence as something that can be built, tested, and reasoned about.
The pre-AI era starts with a shift from philosophy to machinery: could a programmable computer exhibit intelligent behavior if it had enough memory, search, and learning?
Source1950
1950
Foundations
Turing publishes "Computing Machinery and Intelligence" and reframes the question of whether machines can think.
The paper gives AI a public thought experiment: judge machine intelligence by behavior in a controlled interaction, not by introspection into consciousness.
Source1956
1956
Foundations
McCarthy, Minsky, Rochester, and Shannon propose a summer research project on artificial intelligence.
The field gets a name, an agenda, and a founding myth: intelligence might be described precisely enough that machines can simulate it.
Source1958
1958
Symbolic and Early Learning
Frank Rosenblatt introduces a trainable pattern-recognition machine inspired by neural computation.
The perceptron turns learning from examples into hardware and math, giving neural networks their first major public wave.
Source1966
1966
Symbolic and Early Learning
Joseph Weizenbaum builds a pattern-matching chatbot that imitates a psychotherapist.
ELIZA is technically simple but culturally important: people respond to conversational interfaces as social systems before the systems truly understand them.
Source1969
1969
Symbolic and Early Learning
SRI builds an early mobile robot that reasons over a simplified world and executes plans.
Shakey becomes a canonical example of embodied AI: sensing, symbolic planning, and physical action stitched together in one system.
Source1972
1972
Expert Systems
Stanford researchers build a rule-based medical expert system for infectious disease consultation.
MYCIN proves that narrow expert knowledge can be encoded into rules, explanations, and recommendations, even when deployment remains difficult.
Source1980
1980
Expert Systems
DEC uses a rule-based system to configure computer orders, showing commercial value in expert systems.
The lesson is pragmatic: AI reaches production first where the world is constrained, expensive mistakes are common, and rules can capture expert work.
Source1986
1986
Neural Revival
Rumelhart, Hinton, and Williams popularize learning internal representations with backpropagation.
This becomes a technical hinge: neural networks can now learn useful hidden features instead of relying only on hand-designed inputs.
Source1989
1989
Neural Revival
LeCun and collaborators demonstrate backpropagation for handwritten ZIP code recognition.
Modern computer vision starts to take shape: local filters, shared weights, and gradient learning applied to real image data.
Source1997
1997
Benchmarks
IBM Deep Blue defeats Garry Kasparov in a six-game match.
The victory makes AI visible to the public as a specialized system that can exceed elite human performance in a formal domain.
Source2006
2006
Deep Learning
Researchers show practical ways to train deeper neural networks before today's data and compute scale.
The field starts moving away from brittle feature engineering and toward representations learned by stacked models.
Source2009
2009
Deep Learning
A massive labeled image dataset creates a shared arena for measuring visual recognition progress.
ImageNet matters because it pairs scale with competition, making model progress legible and comparable year after year.
Source2011
2011
Deep Learning
IBM Watson defeats champion players by combining search, language processing, and evidence scoring.
Watson shows that AI systems can integrate many imperfect subsystems and still win in a fast, language-heavy environment.
Source2012
2012
Deep Learning
A GPU-trained convolutional network wins ImageNet by a large margin.
The result changes the allocation of research attention: data, GPUs, depth, and learned representations become the default path for perception.
Source2014
2014
Generative Models
Generative adversarial networks train a generator against a discriminator.
GANs make image synthesis feel dynamic and competitive, opening a new era of generative modeling before diffusion becomes dominant.
Source2016
2016
Reinforcement Learning
DeepMind combines deep networks, search, and reinforcement learning to beat a Go world champion.
Go had long represented intuition and astronomical search space. AlphaGo makes self-play and policy/value networks central to frontier AI imagination.
Source2017
2017
Transformers
"Attention Is All You Need" introduces the architecture that scales into modern language models.
The transformer is the pivotal infrastructure idea: parallel training, long-range token interactions, and a model family that keeps improving with scale.
Source2018
2018
Transformers
BERT and GPT-style models show that large-scale pretraining can transfer broadly across language tasks.
Instead of building task-specific systems from scratch, teams pretrain general models and adapt them, setting up the foundation-model era.
Source2019
2019
Transformers
OpenAI releases a larger transformer language model with surprisingly coherent long-form generation.
GPT-2 turns language modeling from a benchmark exercise into a visible product and safety conversation.
Source2020
2020
Foundation Models
A 175B parameter language model demonstrates strong few-shot behavior through natural-language prompts.
The interface changes: users can specify tasks in ordinary text, and the model adapts without a bespoke training run for each workflow.
Source2020
2020
Foundation Models
DeepMind's system reaches breakthrough accuracy on protein folding.
AI progress becomes visibly scientific, not just linguistic or perceptual: learned systems can compress years of biological structure work.
Source2021
2021
Foundation Models
Text-conditioned image generation becomes a mainstream AI capability.
Generation starts moving from demos to creative tools: prompts become a bridge between language, visual concepts, and controllable outputs.
Source2021
2021
Foundation Models
OpenAI Codex connects language models to code generation and developer workflows.
The coding assistant category appears: models can draft, translate, complete, and explain code well enough to reshape developer tooling.
Source2022
2022
Scaling and Chat
DeepMind argues many large language models are undertrained relative to their parameter count.
The frontier shifts from bigger-only thinking to compute-optimal tradeoffs across parameters, tokens, and training budget.
Source2022
2022
Scaling and Chat
A latent diffusion model becomes widely available for local and community experimentation.
Open image generation changes the culture of AI: model weights, extensions, fine-tunes, and creative workflows spread beyond closed labs.
Source2022
November 2022
Scaling and Chat
A chat interface turns language-model capability into a daily tool for millions of people.
The important move is product shape: instruction following, memory of a conversation, and low-friction access make LLMs legible to non-specialists.
Source2023
March 2023
Multimodal and Agents
OpenAI releases a stronger multimodal-capable model with broad performance gains.
GPT-4 becomes a reference point for model capability, safety evaluation, enterprise adoption, and the first wave of serious agent experiments.
Source2023
February 2023
Multimodal and Agents
Meta releases LLaMA, helping open-weight language-model experimentation spread quickly.
A parallel ecosystem forms around smaller, adaptable models that can be inspected, fine-tuned, and deployed outside a single hosted API.
Source2023
March 2023
Multimodal and Agents
Anthropic introduces Claude as a constitutional-AI assistant focused on helpfulness, honesty, and harmlessness.
The market becomes multi-lab and safety-positioned: assistant behavior, context length, and reliability become product differentiators.
Source2023
December 2023
Multimodal and Agents
Google introduces Gemini as a family of models designed around multimodal reasoning.
The field moves from text-only chat toward systems that operate across images, audio, video, code, and tool use.
Source2024
February 2024
Multimodal and Agents
OpenAI previews a video generation model capable of long, coherent scenes from text prompts.
Video makes the generative leap visceral: temporal consistency, physical plausibility, and creative control become frontier concerns.
Source2024
May 2024
Multimodal and Agents
OpenAI releases an omni model designed for real-time multimodal interaction.
The assistant interface starts to feel less like a text box and more like a real-time collaborator with speech, image, and tool surfaces.
Source2024
September 2024
Reasoning and Agents
OpenAI introduces o1, emphasizing deliberate reasoning for math, code, and hard multi-step tasks.
The product language changes from instant completion to thinking time, evaluation, and reliability on tasks that require sustained reasoning.
Source2025
January 2025
Reasoning and Agents
DeepSeek releases open reasoning models that make frontier-style reasoning feel less exclusive.
R1 intensifies the global model race around efficiency, open weights, reinforcement learning, and how fast strong capabilities can diffuse.
Source2025
February 2025
Reasoning and Agents
OpenAI launches a research agent that searches, reads, and synthesizes sources into longer reports.
The assistant moves from answering from model memory toward using tools, citations, and multi-step source gathering as part of the work product.
Source2025
April 2025
Reasoning and Agents
GPT-4.1 focuses on API coding performance while o3 and o4-mini combine reasoning with tool use.
The model menu fragments into useful roles: faster coding models, deeper reasoning models, cheaper small models, and multimodal tool users.
Source2025
May 2025
Reasoning and Agents
Anthropic introduces Claude Opus 4 and Sonnet 4 with emphasis on coding and extended task performance.
The agentic frontier becomes practical: models are judged by whether they can hold a software task, inspect context, edit, test, and recover.
Source2025
2025
Reasoning and Agents
OpenAI presents GPT-5 as a broadly available model with built-in reasoning and strong coding performance.
By this point, users expect a single assistant to route between fast answers, deeper reasoning, multimodal inputs, and tool-driven work.
Source2026
February 2026
Integrated AI
Deep research expands toward authenticated apps, trusted-source constraints, and real-time progress control.
The direction is clear: serious AI work is not only model capability, but permissioned context, connectors, interruption, provenance, and workflow fit.
Source2026
2026
Integrated AI
Stanford HAI reports rapid capability growth, broad adoption, infrastructure strain, and policy gaps.
AI is no longer one technical story. It is an industrial, educational, scientific, political, and infrastructure system moving at different speeds.
Source