Method and proof wey dem use

Check di structure wey dem publish, claim status, uncertainty disclosures, corrections, den licensed expert contributions wey dey behind dis documentary.

• voiceover The Prediction — The unfinished sentence

  Start with the smallest honest description of a language model and mark what it cannot prove.

• chronology The Prediction — When context became parallel

  Place the Transformer milestone precisely without turning architecture into mythology.

• source_card Training Is Not Inference — What training rewards

  Show how an objective makes learning measurable—and what it leaves out.

• graphic Training Is Not Inference — A prompt is not a training run

  Explain in-context adaptation without implying every conversation rewrites the model.

• graphic Training Is Not Inference — Why the biggest number is not the whole model

  Replace parameter-count spectacle with a joint account of model, data, and training.

• graphic What Scale Measures — The smooth curve

  State the regularity, then keep its metric boundary inside the documentary's main flow.

• source_card What Scale Measures — What the curve cannot prove

  Show why extrapolating one measured relationship into every desired property is a category error.

• archival What Scale Measures — A breakthrough, properly bounded

  Show how task, data, metric, and comparator make a breakthrough intelligible.

• archival Capability Without Myth — A prediction that changed a field

  Examine AlphaFold through blind assessment, metric, confidence, and limits.

• voiceover Capability Without Myth — There is no single capability meter

  Compare forms of prediction without pretending their metrics can be added into one universal rank.

• source_card Fluency and Uncertainty — When imitation reproduces a misconception

  Use a benchmark designed around false beliefs to expose a failure hidden by fluency.

• source_card Fluency and Uncertainty — What the benchmark can say

  Keep vintage, questions, scoring, and model set attached to the memorable percentages.

• graphic Fluency and Uncertainty — Three uncertainties in one answer

  Separate output probabilities, knowledge limits, and the stakes of acting.

• chronology From Model to Agent — When prediction gets hands

  Trace the move from language output to a system that can observe and act.

• graphic From Model to Agent — The control plane

  Use NIST's functions to organize responsibility without reducing governance to a badge.

• source_card From Model to Agent — No certification theatre

  End with continuous testing, monitoring, correction, and recourse.

• source_card The Material System — A dataset is an argument

  Make curation visible as choices rather than a neutral mirror of the world.

• graphic The Material System — Average accuracy, unequal errors

  Show why one score can conceal the people carrying failures.

• map The Material System — There is no cloud without a grid

  Keep totals, local concentration, AI attribution, and scenario uncertainty separate.

• voiceover The Accountable Room — Prediction becomes power through a system

  Synthesize without pretending one layer explains the whole outcome.

• voiceover The Accountable Room — Seven questions before belief

  Close with a reusable evidence discipline rather than a prediction about the future.

Wetin dem claim and proof wey dey

• verified · high

  The AlexNet paper reports 15.3% top-five test error on ILSVRC-2012, compared with 26.2% for the second-best entry.

  Person wey write am: Krizhevsky, Sutskever, and Hinton (2012)

  Wetin still no resolve: The comparison is specific to ILSVRC-2012 and establishes neither general intelligence nor deployment reliability.

  Check di evidence
• verified · high

  The AlexNet paper describes training a deep convolutional neural network to classify 1.2 million high-resolution images into 1,000 classes.

  Person wey write am: Krizhevsky, Sutskever, and Hinton (2012)

  Wetin still no resolve: This is the paper's named task and dataset, not a definition of every later system called artificial intelligence.

  Check di evidence
• verified · high

  The Transformer paper proposed an encoder-decoder architecture based on attention mechanisms, dispensing with recurrence and convolutions in that architecture.

  Person wey write am: Vaswani et al. (2017)

  Wetin still no resolve: The paper concerns the proposed architecture and translation experiments; it did not originate attention or establish every later model behavior.

  Check di evidence
• inference · high

  An architecture diagram specifies information flow and computation; by itself it does not establish beliefs, intentions, consciousness, or a theory of mind.

  Person wey write am: Documentary inference from Vaswani et al. (2017) and NIST (2023)

  Wetin still no resolve: This separates computational description from contested mental-state attribution.

  Check di evidence
• verified · high

  The GPT-3 paper describes GPT-3 as an autoregressive language model with 175 billion parameters.

  Person wey write am: Brown et al. (2020)

  Wetin still no resolve: This identifies one 2020 model and objective; parameter count alone does not characterize current systems.

  Check di evidence
• inference · high

  For an autoregressive language model, fluent continuation is produced through conditional token prediction; token probability is not, without additional evidence, a calibrated belief that its proposition is true.

  Person wey write am: Documentary inference from Brown et al. (2020) and Lin et al. (2022)

  Wetin still no resolve: Other model families and calibration methods can expose additional uncertainty signals; this concerns what token likelihood alone licenses.

  Check di evidence
• verified · high

  The GPT-3 authors state that their pretraining objective weights every token equally and lacks a notion of what is most important to predict.

  Person wey write am: Brown et al. (2020)

  Wetin still no resolve: This is the authors' description of GPT-3's 2020 objective, not every modern training pipeline.

  Check di evidence
• inference · high

  Training changes learned parameters through optimization over examples, while inference applies learned parameters to new inputs; retrieval, tools, memory, or later fine-tuning are additional mechanisms that should be named.

  Person wey write am: Documentary synthesis of Vaswani et al. (2017) and Brown et al. (2020)

  Wetin still no resolve: Some deployed systems update memory or parameters online; those mechanisms must be identified rather than folded into ordinary inference.

  Check di evidence
• verified · high

  In the GPT-3 paper's zero-shot, one-shot, and few-shot evaluations, tasks were specified through text and the model was applied without gradient updates or fine-tuning.

  Person wey write am: Brown et al. (2020)

  Wetin still no resolve: Other products may retrieve data, call tools, update memory, or fine-tune models.

  Check di evidence
• inference · high

  Changing examples or instructions in context can change an autoregressive model's output without changing its learned weights; this is conditional computation, not proof that a training run occurred inside the conversation.

  Person wey write am: Documentary inference from Brown et al. (2020)

  Wetin still no resolve: The internal interpretation of in-context learning remains an active research question, and products may add state outside the base model.

  Check di evidence
• verified · high

  The Chinchilla study trained more than 400 language models and found, within its tested regime, that compute-optimal scaling required increasing model size and training tokens together; its 70-billion-parameter model outperformed larger comparison models at the same training-compute budget.

  Person wey write am: Hoffmann et al. (2022)

  Wetin still no resolve: The empirical result does not fix one timeless token-to-parameter rule for every architecture, dataset, or inference workload.

  Check di evidence
• inference · high

  Parameter count is one model property, not a sufficient account of data, optimization, architecture, evaluation, inference cost, or downstream behavior.

  Person wey write am: Documentary inference from Hoffmann et al. (2022) and Kaplan et al. (2020)

  Wetin still no resolve: Parameter count remains useful when surrounding training and evaluation conditions are controlled.

  Check di evidence
• verified · high

  The scaling-laws paper reports empirical power-law relationships between language-model cross-entropy loss and model size, dataset size, and training compute across the regimes it studied.

  Person wey write am: Kaplan et al. (2020)

  Wetin still no resolve: The relationships concern cross-entropy and the studied architectures, data, and compute ranges; extrapolation can fail.

  Check di evidence
• inference · high

  Smooth improvement in predictive loss does not by itself establish truthfulness, causal understanding, agency, safety, or social value; each needs a separate operational definition and evaluation.

  Person wey write am: Documentary inference from Kaplan et al. (2020), Lin et al. (2022), and NIST (2023)

  Wetin still no resolve: Predictive loss can correlate with downstream results, but the relationship must be measured rather than assumed.

  Check di evidence
• inference · high

  A benchmark result is evidence about a defined task, dataset, metric, comparator, and evaluation date; deployment claims require added evidence about context and consequences.

  Person wey write am: Documentary inference from Krizhevsky et al. (2012) and NIST (2023)

  Wetin still no resolve: Benchmarks vary in external validity; required deployment evidence depends on system and stakes.

  Check di evidence
• verified · high

  In CASP14, the AlphaFold paper reports median backbone accuracy of 0.96 angstrom r.m.s.d.95, compared with 2.8 angstrom for the next-best method, on the assessed CASP domains.

  Person wey write am: Jumper et al. (2021)

  Wetin still no resolve: The metric, domain set, confidence interval, and blind assessment define this result; not every predicted structure is correct.

  Check di evidence
• verified · high

  The AlphaFold paper reports lower accuracy when sequence alignments are shallow and weakness for proteins whose shape depends heavily on interactions with other chains.

  Person wey write am: Jumper et al. (2021)

  Wetin still no resolve: These are observed AlphaFold2 limitations; later systems and specific targets require their own evidence.

  Check di evidence
• inference · high

  AI capability is plural and task-bound: exceptional protein-structure prediction, image classification, or language continuation does not collapse into one context-free quantity called intelligence.

  Person wey write am: Documentary inference across three primary research programs

  Wetin still no resolve: General-purpose transfer can be real, but must be evaluated across explicit task distributions.

  Check di evidence
• verified · high

  TruthfulQA contains 817 questions across 38 categories designed to elicit imitative falsehoods; in the paper's evaluated model set, the best model was truthful on 58% of questions and the reported human comparison was 94%.

  Person wey write am: Lin, Hilton, and Evans (2022)

  Wetin still no resolve: The figures belong to the paper's benchmark, scoring, prompts, and model vintages; they are not current performance claims.

  Check di evidence
• verified · high

  Within the TruthfulQA paper's evaluated model set, larger models were generally less truthful, a pattern the authors connect to learning false answers from the training distribution.

  Person wey write am: Lin, Hilton, and Evans (2022)

  Wetin still no resolve: This is not a universal law that added scale always reduces truthfulness.

  Check di evidence
• inference · medium

  A responsible AI answer separates predictive uncertainty about outputs, epistemic uncertainty about adequate knowledge, and decision uncertainty about the cost of acting.

  Person wey write am: Documentary analytical framework grounded in Brown et al. (2020), Lin et al. (2022), and NIST (2023)

  Wetin still no resolve: The three-layer vocabulary is an editorial synthesis, not a standardized taxonomy in the cited sources.

  Check di evidence
• verified · high

  The ReAct paper studies prompts that interleave language-model reasoning traces with task-specific actions, allowing a system to gather information from external sources or interact with an environment.

  Person wey write am: Yao et al. (2023)

  Wetin still no resolve: ReAct is one studied design and does not establish human-like reasoning or autonomous intent.

  Check di evidence
• inference · high

  Operational agency depends on the surrounding loop: goals, tools, available data, action permissions, persistent state, and human authority to stop execution.

  Person wey write am: Documentary inference from Yao et al. (2023) and NIST (2023)

  Wetin still no resolve: Philosophical accounts differ; this concerns observable operational control in deployed systems.

  Check di evidence
• verified · high

  NIST AI RMF 1.0 organizes its Core around GOVERN, MAP, MEASURE, and MANAGE, with governance cross-cutting the other functions.

  Person wey write am: NIST (2023)

  Wetin still no resolve: AI RMF 1.0 is voluntary and under revision; the functions are not proof a particular system is safe.

  Check di evidence
• verified · high

  NIST AI RMF 1.0 says risk management should be continuous across the AI lifecycle and systems should be tested before deployment and regularly while in operation.

  Person wey write am: NIST (2023)

  Wetin still no resolve: Implementation and evidence remain the responsibility of the organization using the system.

  Check di evidence
• inference · high

  Applying a framework name is not a safety finding; responsible governance needs traceable evidence, named owners, monitoring, response, and recourse for the actual system and context.

  Person wey write am: Documentary inference from NIST (2023)

  Wetin still no resolve: Certification regimes may add audited requirements; AI RMF 1.0 itself is voluntary.

  Check di evidence
• verified · high

  Datasheets for Datasets proposes documenting a dataset's motivation, composition, collection process, recommended uses, and operating context.

  Person wey write am: Gebru et al. (2021)

  Wetin still no resolve: A datasheet improves inspectability but cannot prove collection, consent, representation, or downstream use is acceptable.

  Check di evidence
• inference · high

  Training data is not raw reality: inclusion, exclusion, labeling, filtering, deduplication, and documentation are design choices shaping what can be learned and whose experience becomes legible.

  Person wey write am: Documentary inference from Gebru et al. (2021)

  Wetin still no resolve: The influence of one data decision on behavior usually requires targeted measurement.

  Check di evidence
• verified · high

  In the Gender Shades audit of three commercial gender-classification systems, error rates reached 34.7% in its darker-skinned female subgroup, while the maximum error rate in its lighter-skinned male subgroup was 0.8%.

  Person wey write am: Buolamwini and Gebru (2018)

  Wetin still no resolve: The study's female/male labels and Fitzpatrick skin-type groupings are operational benchmark categories, not independent findings about each person's gender identity; the categories, products, benchmark, and 2018 evaluation date bound the result.

  Check di evidence
• inference · high

  Aggregate accuracy can hide sharply different error distributions; evaluation must inspect representation, subgroup errors, resulting decisions, and recourse.

  Person wey write am: Documentary inference from Buolamwini and Gebru (2018) and NIST (2023)

  Wetin still no resolve: Meaningful subgroup analyses depend on application, population, measurement quality, and legal context.

  Check di evidence
• verified · high

  The IEA estimates data centres used about 415 terawatt-hours of electricity in 2024, around 1.5% of global electricity consumption.

  Person wey write am: IEA (2025)

  Wetin still no resolve: This is for all data centres, not AI alone; global totals do not describe a facility or grid.

  Check di evidence
• verified · high

  In the IEA Base Case, global data-centre electricity consumption rises to about 945 terawatt-hours by 2030; AI is the most important growth driver alongside other digital services.

  Person wey write am: IEA (2025)

  Wetin still no resolve: This is a scenario projection, not a meter reading; adoption, efficiency, hardware, and energy bottlenecks can change it.

  Check di evidence
• verified · high

  The IEA sensitivity cases span roughly 700 to 1,700 terawatt-hours of global data-centre electricity demand in 2035.

  Person wey write am: IEA (2025)

  Wetin still no resolve: The range belongs to named cases and is not a formal confidence interval.

  Check di evidence
• inference · high

  AI-system consequences are produced jointly by model behavior, data, interfaces, permissions, institutional incentives, infrastructure, and people exposed to decisions.

  Person wey write am: Documentary synthesis across system, data, impact, infrastructure, and governance evidence

  Wetin still no resolve: The relative contribution of each layer is context-specific and must be investigated.

  Check di evidence
• inference · high

  A defensible AI claim names task, data, metric, uncertainty, action boundary, affected people, and accountable owner, then preserves monitoring and correction after deployment.

  Person wey write am: Documentary synthesis from Gebru et al. (2021), NIST (2023), and Lin et al. (2022)

  Wetin still no resolve: Specific sectors may require additional legal, scientific, or community-defined duties.

  Check di evidence

• 2026-08-29T00:00:00Z: Retained the released benchmark statement while moving its evidence reference to the newly reviewed second version of the same AlexNet source lineage and sharpening the evaluation-boundary note.
• 2026-08-29T00:00:00Z: Retained the released training-scope statement while moving its evidence reference to the newly reviewed second version of the same AlexNet source lineage and tightening the excerpt and scope boundary.