Trentism's definitions
Did you hear that he is now one of the newest Teslanaires ? Yes, he is worth $1.4 Million dollars from his investment in Tesla stock.
by Trentism January 13, 2021
Get the Teslanaires mug.Self Rehearsal is a post-reinforcement training phase for Large Vector Models (LVMs) that strengthens conceptual coherence through autonomous vector replay and refinement.
AI context:
During self rehearsal, the large vector model replays its recently reinforced concept transitions to consolidate semantic stability in latent space.
Biology context:
In the human brain, self rehearsal occurs during sleep when neural circuits spontaneously reactivate recent experiences to strengthen long-term memory traces.
During self rehearsal, the large vector model replays its recently reinforced concept transitions to consolidate semantic stability in latent space.
Biology context:
In the human brain, self rehearsal occurs during sleep when neural circuits spontaneously reactivate recent experiences to strengthen long-term memory traces.
by Trentism October 24, 2025
Get the Self Rehearsal mug.A bidirectional component that converts human language inputs directly into high-dimensional semantic vectors and reconstructs human-interpretable outputs from those vectors, bypassing traditional tokenization. Unlike a tokenizer—which segments text into discrete linguistic units—the Neuralator enables concept-native processing by preserving semantic relationships in compressed vector form.
Sometimes spelled: Neurolator
Sometimes spelled: Neurolator
In contrast to BERT’s tokenizer, the LN system uses a Neuralator to encode and decode conceptual information without relying on syntactic fragmentation.
by Trentism July 9, 2025
Get the Neuralator mug.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 Neurolese mug.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 9, 2025
Get the Nuclear Diversity mug.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 7, 2021
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