The Embedding Model That Thought Like a Human, Not a GPU
Long before GPUs, transformers, or billion-parameter models, researchers were already building word embeddings.
In 1996, they called one of them HAL — Hyperspace Analogue to Language. Its goal wasn’t scale or performance. It was a much stranger question:
Can a machine learn meaning the way humans do—just by being exposed to language?
