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
Get the Neurolesemug. The quirky, often-flawed but curiously coherent dialect you get when an AI translates its inner language—known as Latent Neurolese—into human English. Think uncanny metaphors, oddly specific analogies, and sentence structures that feel like they just passed through an alien’s poetry workshop.
It’s not a bug—it’s an accent. The linguistic vapor trail of how the machine thinks behind the curtain.
It’s not a bug—it’s an accent. The linguistic vapor trail of how the machine thinks behind the curtain.
by Trentism June 19, 2025
Get the Ainglishmug. Spiffy-pop; Regarding equity’s such as Stocks, a spiffy pop is when your original purchase price is gained in a single day.
I.e. you bought TSLA for $50/share and it goes up $50 in a single day usually in the distant future. Previous Close; $755. Current Price $806. So your gain is a spiffy pop.
by Trentism January 07, 2021
Get the spiffy popmug. A knowledge distillation approach that uses extreme loss function weighting to force neural networks to preserve semantic differences between distinct concepts while preventing mode collapse. The technique employs "nuclear" (extreme) lambda parameters that heavily weight diversity preservation over teacher alignment, ensuring that different input concepts produce genuinely different vector representations.
Key characteristics:
Uses extreme weighting ratios (e.g., λ_diversity = 2.0-6.0 vs λ_alignment = 0.02-0.1)
Prevents mode collapse where different inputs produce nearly identical outputs
Maintains semantic separation in compressed vector spaces
Applied in the LN (Learning Networks) Semantic Encoder architecture
Measures success by reducing cosine similarity between different concepts from ~0.99 to ~0.3-0.7
The term "nuclear" emphasizes the aggressive, sometimes extreme measures needed to solve fundamental problems in neural network training where subtle parameter adjustments fail to achieve the desired diversity preservation.
Key characteristics:
Uses extreme weighting ratios (e.g., λ_diversity = 2.0-6.0 vs λ_alignment = 0.02-0.1)
Prevents mode collapse where different inputs produce nearly identical outputs
Maintains semantic separation in compressed vector spaces
Applied in the LN (Learning Networks) Semantic Encoder architecture
Measures success by reducing cosine similarity between different concepts from ~0.99 to ~0.3-0.7
The term "nuclear" emphasizes the aggressive, sometimes extreme measures needed to solve fundamental problems in neural network training where subtle parameter adjustments fail to achieve the desired diversity preservation.
The researchers implemented nuclear diversity in their knowledge distillation pipeline, using extreme lambda weighting of 6.0 for diversity preservation versus 0.02 for teacher alignment, successfully reducing semantic collapse from 0.998 to 0.324 cosine similarity between distinct concepts.
by Trentism July 09, 2025
Get the Nuclear Diversitymug. Acceleration of the death of vehicle OEMs due to the Inverse wrights law. For instance if their sales drop by 50% their cost to go up 15% and if their gross margin was originally 20% it would drop to 5% assuming they could not increase their pricing.
by Trentism January 16, 2022
Get the inverse wrights lawmug. Teslaforever describes an investor in Tesla with a stock holding period that is indefinite. They will never sell the equity, extracting funds needed via Margin (borrowing against the equity) or future dividends when Tesla decides to pay dividends in the future.
by Trentism January 16, 2022
Get the Teslaforevermug. Software 2.0 is software based artificial intelligence and defined by weights in a large language model.
Andrej Karpathy, a prominent Al researcher and former senior director of Al at Tesla, coined the term
"Software 2.0"
Software 2.0 refers to a new paradigm in software development where traditional programming is replaced by machine learning models. Instead of writing explicit code, developers create and train neural networks that learn from data to perform tasks. This approach allows software to solve complex problems like image recognition, natural language processing, and more, by learning from examples rather than following predefined rules.
Andrej Karpathy, a prominent Al researcher and former senior director of Al at Tesla, coined the term
"Software 2.0"
Software 2.0 refers to a new paradigm in software development where traditional programming is replaced by machine learning models. Instead of writing explicit code, developers create and train neural networks that learn from data to perform tasks. This approach allows software to solve complex problems like image recognition, natural language processing, and more, by learning from examples rather than following predefined rules.
Most of the large language models we use aren’t compiled code. They are all software 2.0. The software that full self drives my Tesla is software 2.0.
by Trentism January 11, 2025
Get the Software 2.0mug.