Epiplexity vs Entropy: The New Metric That Explains What AI Actually Learns
Автор: Binary Verse AI
Загружено: 2026-01-10
Просмотров: 40
Read the full article: https://binaryverseai.com/epiplexity-...
Most AI training talks obsess over prediction. This one is about the hidden asset, the program your model writes into its weights. We unpack Epiplexity, why Shannon entropy can be a trap, how loss curves can mislead, and how to use Epiplexity-style thinking for LLM optimization, data selection, synthetic data quality, and lower AI training cost. If you build models, curate datasets, or tune pipelines, this is the mental model that stops you from paying for “hard but hollow” tokens.
Chapters
00:00 The Program, Not the Prediction
00:42 The Loss Curve is a Liar
01:30 Why Noise Masquerades as Information
02:05 Not All Bits Are Created Equal
03:00 Enter Epiplexity: Defining Structure
03:35 The Constraint Creates the Structure
04:05 The Two Ledgers of Learning (MDL)
04:35 Epiplexity vs. Time-Bounded Entropy
05:25 Paradox 1: Creation From Determinism (AlphaZero)
06:30 Paradox 2: The Cost of Accessibility
07:25 Paradox 3: Emergence Over Rules
08:35 Measurement: Prequential Coding
09:05 Case Study: Rule 54 vs. Rule 30
09:45 Measurement: Requential Coding
10:20 Flipping the Optimization Script
11:00 Your New Data Pipeline (Workflow)
11:45 Stop Paying for Hollow Hardness
12:25 The Role of Synthetic Data
13:20 Real World Verification (OpenWebText vs CIFAR)
14:40 Conclusion: The Future of Data-Centric AI
If this helped, subscribe for weekly deep dives, and drop a comment with the strangest loss curve you’ve seen. I’ll pick a few and do a follow-up breakdown.
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