The true tech-history of AI - a graph traversal across the sciences

(AI-chronology Tab, #249. Central claim: "AI" is not "LLM." AI is a broad field of many branches - logic/search, statistical ML, reinforcement learning, computer vision, robotics, protein folding. LLMs (transformers, 2017+) are the currently-dominant branch, not the field. Below is a followable walk of the lineage, breadth-first across roots, then depth-first down each branch.)

Roots (the shared substrate)

• Mathematical logic (Boole, Frege, Turing, Church, 1850s-1936) formalized computation itself.
• Probability + statistics (Bayes, Fisher) gave learning from data its footing.
• Information theory (Shannon, 1948) quantified information (entropy, likelihood).
• Cybernetics + control (Wiener, 1948) framed feedback + goal-seeking systems.
• Neuroscience abstraction (McCulloch-Pitts neuron, 1943) modeled the brain as computation.

Branch A - Symbolic AI (the first paradigm)

Logic -> the Dartmouth 1956 program coins "AI" -> search, theorem-proving, LISP -> expert systems (MYCIN, DENDRAL, 1970s-80s), commercial AI's first wave -> AI winter when hand-coded rules proved brittle. Lesson that echoes forward: rules don't scale; learn from data instead.

Branch B - Connectionism (the branch that won, eventually)

Cybernetics + McCulloch-Pitts -> Perceptron (Rosenblatt, 1958), the first trainable net -> frozen by Minsky-Papert (1969) -> revived by backpropagation (Rumelhart/Hinton/Williams, 1986) for multi-layer nets -> stalled again on compute until...

The hardware unlock (the cross-cutting edge)

GPUs + CUDA (2007) made deep-net training practical - hardware, not just algorithms, opened the modern era. It fed multiple branches at once:

• -> ImageNet + AlexNet (2012): deep learning crushes vision (not language). The deep-learning era begins.
• -> Deep reinforcement learning: DQN (2013) -> AlphaGo (2016) (deep RL + tree search) -> AlphaFold (2020-24) solves protein structure (a Nobel). These are superhuman AI systems that are NOT LLMs - the clearest proof the field is bigger than language models.

Branch C - Sequences -> attention -> LLMs

Vision deep learning spread to sequences (RNN/LSTM) -> attention -> the Transformer (Vaswani et al., 2017) -> scaling laws -> GPT/BERT -> ChatGPT (2022) -> the frontier race. This is the branch now soaking up attention + capital - but trace the tree: LLMs are one branch (C) of deep learning, itself one branch of ML, itself one branch of AI. Branches A (symbolic) and B/RL (AlphaGo/AlphaFold) are alive and, in domains like science, ahead.

Why the walk matters

Treating "AI" as synonymous with "LLM" mis-reads both the risk surface and the history: the capital + safety-regulation fight (spec-ai-lab-chronology-ea-pathways) centers on LLMs, but the field's biggest scientific wins came from other branches, and its future may too. Keeping the taxonomy honest is the point of this Tab.

Sources: standard histories of computing, AI, ML, and the contributing fields. All edges are established lineage (fact); the "LLMs are one branch" framing is a taxonomy claim, not a value judgment. Cross-refs: Mathematical_Logic, Information_Theory, Cybernetics, Perceptron, Symbolic_AI, Expert_Systems, Backpropagation, GPU_Compute, ImageNet_AlexNet, Transformer, Reinforcement_Learning, AlphaGo_AlphaFold, Deep_Learning.

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