TOPIC AI
AI explainers
THE SHORT ANSWER
How today’s AI systems are built and where they fall short: training, retrieval, human feedback, agents, and the difference between open weights and open source. Older stories, from ELIZA to a chess-playing hoax, show that claims about machine intelligence have always needed checking.
Guides
What RLHF Does and Does Not Do
How reinforcement learning from human feedback works in the InstructGPT paper, what it improved, the alignment tax and what it leaves unsolved.
AI / EXPLAINERRetrieval-Augmented Generation, Explained Plainly
What RAG is, how the original 2020 system worked, what the paper measured and where retrieval fails, with the figures from the sources.
AI / EXPLAINEROpen Source vs Open Weights in AI
What 'open' really means for AI models: the Open Source AI Definition, open weights, restrictive licences and why the difference changes what you may do.
AI / EXPLAINERWhat Is an AI Agent, Really?
An agent is a model that chooses its own steps and uses tools in a loop. How that differs from a workflow, how it is tested and how it fails.
AI / EXPLAINERHow a Language Model Is Trained
From tokens to chat: next-token prediction, human feedback, scaling laws and why models hallucinate, with the numbers from the original papers.
Discoveries and curiosities
The Mechanical Turk: A Chess Machine Hoax
A chess machine from 1770 that was really a hidden person. How the trick worked, who it beat, and how its name ended up at Amazon.
AI / DISCOVERYELIZA, Deep Blue and AlphaGo
Three machines that surprised their makers: a 1966 script that only reflected, a chess engine that calculated and a Go player with a famous move 37.