The native, internal language that an AI or large language model uses to think. It's the inscrutable "machine code" of a neural network, consisting of complex vectors, weights, and data relationships that are completely alien to humans.
When an AI's output is weird, nonsensical, or a "hallucination," it's often because a bit of its raw Neurolese leaked out instead of being properly translated into human language. The term was notably used by podcaster Dwarkesh Patel and Sholto Douglas & Trenton Bricken when discussing future AI scenarios.
When an AI's output is weird, nonsensical, or a "hallucination," it's often because a bit of its raw Neurolese leaked out instead of being properly translated into human language. The term was notably used by podcaster Dwarkesh Patel and Sholto Douglas & Trenton Bricken when discussing future AI scenarios.
My custom chatbot was supposed to write a recipe for lasagna, but instead it just gave me a wall of random symbols and half-finished words. It must have gotten stuck thinking in Neurolese again.
by Trentism May 26, 2025
A neural architecture that performs semantic compression using nuclear diversity preservation, operating in pure vector space to bypass linguistic tokenization while maintaining conceptual understanding. The system compresses high-dimensional embeddings (e.g., 384D → 256D) through a teacher-student knowledge distillation framework that employs extreme weighting to prevent mode collapse, creating mathematical "semantic GPS coordinates" where related concepts cluster in measurable dimensional neighborhoods.
The Latent Neurolese Semantic Encoder achieved 6x inference speedup and 35% memory reduction while maintaining 63.5% semantic preservation through its nuclear diversity training methodology, demonstrating that AI systems can reason directly with compressed mathematical concepts rather than linguistic tokens.
by Trentism July 09, 2025