An Ideal Laboratory for Self‑Improving Agents Settlers of Catan looks disarmingly simple—collect wood, sheep, brick, wheat, and ore, then build roads and settlements. Yet the game’s soul is negotiation and long‑term planning under uncertainty, exactly the kind of challenge that has tripped up many reinforcement‑learning systems. Dice inject randomness, opponents conceal their intentions, and every trade reshapes […]| NATURAL 20
From Static Training to Continuous Learning For most of the deep‑learning era, language models have behaved like gifted but forgetful students. They memorize vast libraries of text, shine on day‑one exams, and then freeze in time, unable to integrate new material without a costly retraining cycle. MIT researchers have now upended that workflow with Self‑Adapting […]| NATURAL 20
Automation’s Long Shadow For seventy years, machines have chipped away at human work, one incremental improvement at a time. Mainframe spreadsheets dethroned clerks, industrial robots lightened assembly lines, and, most recently, large language models began writing copy and debugging code. Labor‑force participation in the United States peaked in the 1950s and has slipped ever since, […]| NATURAL 20