Papers
Foundational and modern papers analyzed across all 4 rounds + results verification. Each feeds into the Networks, AI, and AI×Networks domains.
Every paper opens with a plain-language What · How · Why explainer — what the paper is, how its mechanism actually works, and why it matters then and now — before the dense technical rounds. So you can grasp each idea without the jargon barrier, then go as deep as you like.
Analyzed — 10 papers, all Round 4 ✓
On Computable Numbers (Entscheidungsproblem) Round 4 ✓
Alan M. Turing · 1936 — defines computation, the Universal Machine, undecidability. Root node of the KB.
A Mathematical Theory of Communication Round 4 ✓
Claude E. Shannon · 1948 — entropy, channel capacity, source/channel coding. The Networks bridge.
The Perceptron Round 4 ✓
Frank Rosenblatt · 1958 — first learning machine with a convergence proof; the single neuron.
Perceptrons Round 4 ✓
Minsky & Papert · 1969 — XOR/expressivity limits; the case for depth. AI-winter trigger.
Time, Clocks, and the Ordering of Events Round 4 ✓
Leslie Lamport · 1978 — happens-before, logical clocks, state-machine replication. Distributed-systems bridge.
Learning Representations by Back-Propagating Errors Round 4 ✓
Rumelhart, Hinton & Williams · 1986 — the chain-rule credit assignment that trains every deep net.
The Anatomy of a Large-Scale Search Engine (PageRank) Round 4 ✓
Brin & Page · 1998 — link-graph eigenvector ranking; ancestor of graph ML.
ImageNet Classification with Deep CNNs (AlexNet) Round 4 ✓
Krizhevsky, Sutskever & Hinton · 2012 — ReLU + dropout + GPUs; the deep-learning ignition (15.3% top-5).
Attention Is All You Need Round 4 ✓
Vaswani et al. (Google) · 2017 — the Transformer. The AI core; feeds traffic prediction & config generation.
Language Models are Few-Shot Learners (GPT-3) Round 4 ✓
Brown et al. (OpenAI) · 2020 — 175B params, in-context learning, scaling laws. Capstone of the lineage.