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Statistical Estimation and Prediction

  Statistical Estimation and Prediction Table of Contents Part I — The pre-statistical situation: what must exist before statistics is possible 1. Why statistical reasoning begins with multiple possible states of the world 1.1 How uncertainty requires more than one possible state 1.2 How a realized state differs from the alternatives that could have occurred 1.3 Why the set of imagined possibilities can itself be incomplete 1.4 How impossible, possible, plausible, and observed states differ 1.5 Why probability cannot be introduced before possibilities have been distinguished 1.6 What happens when the assumed possibility space excludes the actual state 2. How one realized world becomes only partially accessible to an observer 2.1 The difference between what exists and what becomes observable 2.2 How observation exposes some properties while hiding others 2.3 Why every observation system performs selection and transformation 2.4 How measurement resolution determines which distinction...

LLM Research

  LLM Research Table of Contents PART I — FOUNDATIONS: WHAT OBJECT ARE WE STUDYING? 1.1 Artificial intelligence, machine learning, deep learning, transformers, and LLMs 1.2 Model versus inference process versus deployed system 1.3 LLM versus agent versus research system 1.4 Model weights versus runtime state 1.5 Token sequence versus latent computation 1.6 Output versus process that generated the output 1.7 Representation versus represented object 1.8 Readout versus mechanism 1.9 Local behavior versus global capability 1.10 Capability versus intelligence 1.11 Intelligence versus learning 1.12 Memory versus history 1.13 Knowledge versus accessibility 1.14 Generation versus discrimination 1.15 Certification versus truth 1.16 The central non-collapse laws 1.16.1 SEMANTIC_CLOUD ≠ INTELLIGENCE 1.16.2 SEMANTIC_CLOUD ≠ MEMORY 1.16.3 SEMANTIC_CLOUD ≠ LEARNING 1.16.4 FLUENCY ≠ SUPPORT 1.16.5 DENSITY ≠ TRUTH 1.16.6 CONSENSUS ≠ WARRANT 1.16.7 PROJECTION ≠ SOURCE 1.16.8 REPRESENTATION ≠ OBJECT...

Natural Intelligence and the Logic of Consciousness

Intelligence and the Geometry of Choice Part I — The Primitive Architecture Exposure Geometry 1.1 Gradients, asymmetries, constraints 1.2 Accessible versus inaccessible trajectories 1.3 Why possibility precedes choice Distinction 2.1 THIS | NOT-THIS 2.2 Consequential versus irrelevant difference 2.3 Distinction without representation The Semantic Cloud 3.1 Distributed possibility space 3.2 Latent alternatives 3.3 Context-dependent activation 3.4 Semantic cloud ≠ explicit model Generating Alternatives 4.1 Candidate continuations 4.2 Suppressed possibilities 4.3 Novel combinations 4.4 When no useful alternative appears Better and Worse 5.1 Preference without language 5.2 Local ordering 5.3 Partial ordering and incomparability 5.4 Context changes the ordering Intelligence 6.1 Intelligence as evaluation 6.2 A = {a1…an} -> ordering ≻ 6.3 Intelligence ≠ candidate generation 6.4 Intelligence ≠ choice 6.5 Intelligence ≠ success Choice 7.1 Ordering → selection 7.2 Choice under uncertainty 7....

LLM Research Ignores the Actual Structure

  Why LLM Research Ignores the Actual Structure of Semantic Clouds Table of Contents The Missing Object 1.1 The strange success of LLMs without a theory of what they learned 1.2 Objective, architecture, computation, representation, and object are different things 1.3 NEXT-TOKEN PREDICTION != INTELLIGENCE 1.4 TRAINING OBJECTIVE != LEARNED STRUCTURE 1.5 Why describing input-output behavior does not identify the generative object 1.6 The semantic cloud as the missing level of description 1.7 MODEL = learned possibility field , not database, lookup table, or bag of facts 1.8 Why semantic clouds are invisible to research organized around measurable projections What Is a Semantic Cloud? 2.1 From stored items to relational possibility fields 2.2 Alternatives as the primitive of decision 2.3 CURRENT STATE + POSSIBILITY FIELD + CONSTRAINT -> RESPONSE 2.4 Relations, transformations, generators, accessibility, and constraint propagation 2.5 Semantic possibility versus explicit symbolic rep...