feat(i18n): add AI history components internationalization support
- Add useI18n composable and ai-history locale files - Refactor 10 AI history Vue components to support i18n (GPTEvolutionDemo, AIErasComparisonDemo, AiEvolutionDemo, etc.) - Add English version of AI history appendix article - Add English translations for stage-1 appendix-articles: - vibe-coding-tools-snake-game-tutorial.md - vibe-coding-tools-build-website-with-ai-coding-and-design-agents.md - Use relative paths to reference Chinese version images - Update appendix sidebar config to use English AI history link
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// AI History – English locale
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export default {
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// AiEvolutionDemo
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aiEvolution: {
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eras: [
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{ label: 'Foundations', years: '1940s-50s' },
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{ label: '1st Wave', years: '1960s-70s' },
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{ label: '❄️ Winter I', years: '1974-80' },
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{ label: '2nd Wave', years: '1980s' },
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{ label: '❄️ Winter II', years: '1987-93' },
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{ label: 'ML Rise', years: '1990s-2000s' },
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{ label: 'Deep Learning', years: '2010s' },
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{ label: 'LLM Era', years: '2018+' }
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],
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legend: {
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wave: 'Tech Wave',
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winter: '❄️ AI Winter',
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llm: 'LLM Era'
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}
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},
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// DiscriminativeVsGenerativeDemo
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schools: {
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repLabel: 'Examples',
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items: [
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{
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name: 'Symbolism',
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idea: 'Intelligence = symbolic reasoning / If-Then rules',
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rep: 'Expert Systems, Deep Blue',
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status: '→ Merging with connectionism (neuro-symbolic AI)'
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},
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{
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name: 'Connectionism',
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idea: 'Intelligence = neural networks + massive data',
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rep: 'AlphaGo, GPT series',
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status: '→ Dominates the LLM era, current mainstream'
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},
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{
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name: 'Behaviorism',
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idea: 'Intelligence = interaction with environment / RL',
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rep: 'AlphaGo (RL component)',
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status: '→ Merging with connectionism (deep RL)'
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}
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]
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},
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// FoundationDemo
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foundation: {
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label: 'Core idea of Symbolism — encoding knowledge as rules',
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lines: [
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{ parts: [{ kw: 'IF' }, { text: ' temperature > 38.5°C ' }, { kw: 'AND' }, { text: ' WBC count > 11000' }] },
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{ indent: true, parts: [{ kw: 'THEN' }, { text: ' diagnosis = ' }, { str: '"bacterial infection"' }] },
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{ parts: [{ kw: 'IF' }, { text: ' diagnosis = ' }, { str: '"bacterial infection"' }, { text: ' ' }, { kw: 'AND' }, { text: ' no penicillin allergy' }] },
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{ indent: true, parts: [{ kw: 'THEN' }, { text: ' treatment = ' }, { str: '"penicillin 400mg / twice daily"' }] }
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],
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comment: '// The early medical expert system MYCIN (1977) consisted of 450+ rules like these',
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caption: 'Human experts translate experience into IF-THEN rules; the machine matches and executes them one by one'
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},
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// PerceptronDemo
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perceptron: {
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features: ['Feature x₁', 'Feature x₂'],
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biasLabel: 'Bias',
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activated: 'Fire',
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silent: 'Silent',
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caption: '① Input features\u2003② Multiply by weights (importance)\u2003③ Sum + bias\u2003④ Fires output 1 if above threshold, otherwise 0'
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},
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// BackpropagationDemo
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backprop: {
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steps: [
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{ icon: '➡️', name: 'Forward Pass', desc: 'Data flows through the network to produce a prediction' },
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{ icon: '📐', name: 'Compute Loss', desc: 'Prediction vs. ground truth → calculate loss' },
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{ icon: '⬅️', name: 'Backpropagation', desc: 'Trace back each weight\'s "responsibility" layer by layer' },
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{ icon: '⚙️', name: 'Update Weights', desc: 'Adjust proportionally to reduce future error' }
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],
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lossLabel: 'Loss decreases over training epochs:',
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axisHigh: 'High',
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axisLow: 'Low',
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axisEpochs: 'Training Epochs'
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},
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// NeuralNetworkVisualizationDemo
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neuralNet: {
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layers: [
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{ name: 'Input Layer', desc: 'Raw pixels / numerical signals' },
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{ name: 'Hidden Layers (stackable)', desc: 'Low → edges; Mid → shapes; High → semantic concepts' },
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{ name: 'Output Layer', desc: 'Final classification or prediction' }
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]
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},
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// AttentionMechanismDemo
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attention: {
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colLabel: 'Attention distribution when processing "{word}":',
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sentence: ['John', 'gave', 'the', 'apple', 'to', 'his', 'mother'],
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focusIdx: 5,
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weights: [0.62, 0.08, 0.03, 0.10, 0.05, 0.07, 0.05],
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caption: '"his" sits mid-sentence, yet the model directs 62% attention to "John" at the start — resolving the pronoun across distance'
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},
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// GPTEvolutionDemo
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gptEvolution: [
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{ name: 'GPT-1', year: '2018', params: '117 M', barWidth: '2%', key: 'Pre-train + fine-tune paradigm' },
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{ name: 'GPT-2', year: '2019', params: '1.5 B', barWidth: '6%', key: 'Zero-shot generalization' },
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{ name: 'GPT-3', year: '2020', params: '175 B', barWidth: '45%', key: '⚡ Emergence! In-context learning' },
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{ name: 'GPT-4', year: '2023', params: '~1.8 T', barWidth: '100%', key: 'Multimodal + complex reasoning' }
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],
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// AIErasComparisonDemo
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erasComparison: {
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header: '🌟 AI Development Stages & Core Paradigms at a Glance',
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driverLabel: 'Driver',
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mechanismLabel: 'Core Mechanism',
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examplesLabel: 'Key Examples',
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eras: [
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{ name: 'Rule-Based Era', time: '1960s - 1980s', driver: 'Human-coded knowledge', mechanism: 'If-Then logical deduction', examples: ['Dendral', 'Deep Blue'] },
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{ name: 'Classical ML', time: '1990s - 2000s', driver: 'Manual feature engineering + statistics', mechanism: 'Finding mathematical decision boundaries', examples: ['SVM', 'Random Forest'] },
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{ name: 'Deep Learning Revolution', time: '2010s', driver: 'Big data + GPU compute', mechanism: 'Neural nets auto-extract features', examples: ['AlexNet (CNN)', 'AlphaGo (RL)'] },
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{ name: 'Large Language Models', time: '2018 - present', driver: 'Massive unlabeled data + brute-force compute', mechanism: 'Next-token prediction + emergent knowledge', examples: ['GPT-4', 'Claude 3'] },
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{ name: 'Agentic AI', time: 'Now - future', driver: 'LLM brain + environment perception', mechanism: 'Autonomous planning + tool use', examples: ['AI Programmer', 'Embodied AI'] }
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]
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}
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}
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import zhCn from './zh-cn.js'
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import en from './en.js'
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export const aiHistoryLocale = {
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'zh-cn': zhCn,
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en
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}
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// AI 简史 – 中文语言包
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export default {
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// AiEvolutionDemo
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aiEvolution: {
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eras: [
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{ label: '理论奠基', years: '1940s-50s' },
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{ label: '第一次浪潮', years: '1960s-70s' },
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{ label: '❄️ 寒冬 I', years: '1974-80' },
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{ label: '第二次浪潮', years: '1980s' },
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{ label: '❄️ 寒冬 II', years: '1987-93' },
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{ label: 'ML 崛起', years: '1990s-2000s' },
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{ label: '深度学习', years: '2010s' },
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{ label: '大模型时代', years: '2018+' }
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],
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legend: {
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wave: '技术浪潮',
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winter: '❄️ AI 寒冬',
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llm: '大模型时代'
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}
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},
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// DiscriminativeVsGenerativeDemo
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schools: {
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repLabel: '代表',
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items: [
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{
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name: '符号主义',
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idea: '智能 = 符号推理 / If-Then 规则',
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rep: '专家系统、深蓝',
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status: '→ 与连接主义融合（神经符号 AI）'
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},
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{
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name: '连接主义',
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idea: '智能 = 神经元网络 + 海量数据',
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rep: 'AlphaGo、GPT 系列',
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status: '→ 主导大模型时代，当前主流'
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},
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{
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name: '行为主义',
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idea: '智能 = 与环境互动 / 强化学习',
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rep: 'AlphaGo（RL 部分）',
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status: '→ 与连接主义融合（深度强化学习）'
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}
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]
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},
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// FoundationDemo
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foundation: {
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label: '符号主义的核心思路 ── 把知识写成规则',
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lines: [
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{ parts: [{ kw: 'IF' }, { text: ' 体温 > 38.5°C ' }, { kw: 'AND' }, { text: ' 白细胞计数 > 11000' }] },
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{ indent: true, parts: [{ kw: 'THEN' }, { text: ' 诊断 = ' }, { str: '"细菌感染"' }] },
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{ parts: [{ kw: 'IF' }, { text: ' 诊断 = ' }, { str: '"细菌感染"' }, { text: ' ' }, { kw: 'AND' }, { text: ' 对青霉素不过敏' }] },
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{ indent: true, parts: [{ kw: 'THEN' }, { text: ' 治疗方案 = ' }, { str: '"青霉素 400mg / 每日两次"' }] }
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],
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comment: '// 早期医疗专家系统（MYCIN，1977）就是由 450+ 条这样的规则组成的',
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caption: '人类专家把经验翻译成一条条 IF-THEN 规则，机器逐条匹配执行'
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},
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// PerceptronDemo
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perceptron: {
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features: ['特征 x₁', '特征 x₂'],
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biasLabel: '偏置',
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activated: '激活',
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silent: '静默',
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caption: '① 输入特征\u2003② 乘以权重（重要性）\u2003③ 求和 + 偏置\u2003④ 超过阈值就激活输出 1，否则输出 0'
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},
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// BackpropagationDemo
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backprop: {
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steps: [
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{ icon: '➡️', name: '前向传播', desc: '数据流过网络，得出预测' },
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{ icon: '📐', name: '计算误差', desc: '预测值 vs 正确答案，算 Loss' },
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{ icon: '⬅️', name: '反向传播', desc: '逐层追溯每个权重的"责任"' },
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{ icon: '⚙️', name: '更新权重', desc: '按责任微调，减少下次误差' }
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],
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lossLabel: 'Loss（误差）随训练轮次下降：',
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axisHigh: '高',
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axisLow: '低',
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axisEpochs: '训练轮次 (Epochs)'
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},
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// NeuralNetworkVisualizationDemo
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neuralNet: {
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layers: [
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{ name: '输入层', desc: '原始像素 / 数值信号' },
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{ name: '隐藏层（可叠加多层）', desc: '底层识别边缘 → 中层识别形状 → 高层识别语义概念' },
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{ name: '输出层', desc: '最终分类或预测结果' }
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]
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},
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// AttentionMechanismDemo
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attention: {
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colLabel: '处理「{word}」时的注意力分配：',
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sentence: ['小明', '把', '苹果', '给了', '他', '的', '母亲'],
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focusIdx: 4,
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weights: [0.65, 0.05, 0.10, 0.10, 0.05, 0.03, 0.02],
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caption: '「他」虽在句中间，模型却把 65% 注意力精准投向句首的「小明」，跨越距离识别代词指代'
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},
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// GPTEvolutionDemo
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gptEvolution: [
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{ name: 'GPT-1', year: '2018', params: '1.17 亿', barWidth: '2%', key: '预训练+微调范式确立' },
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{ name: 'GPT-2', year: '2019', params: '15 亿', barWidth: '6%', key: 'Zero-shot 零样本泛化' },
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{ name: 'GPT-3', year: '2020', params: '1750 亿', barWidth: '45%', key: '⚡ 涌现！上下文学习' },
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{ name: 'GPT-4', year: '2023', params: '~1.8 万亿', barWidth: '100%', key: '多模态 + 复杂推理' }
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],
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// AIErasComparisonDemo
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erasComparison: {
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header: '🌟 AI 发展阶段与核心范式全景对比',
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driverLabel: '驱动方式',
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mechanismLabel: '核心机制',
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examplesLabel: '典型代表',
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eras: [
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{ name: '规则系统时代', time: '1960s - 1980s', driver: '人类硬编码知识', mechanism: 'If-Then 逻辑推演', examples: ['Dendral', '深蓝 (Deep Blue)'] },
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{ name: '传统机器学习', time: '1990s - 2000s', driver: '人工特征工程 + 统计学', mechanism: '寻找数学决策边界', examples: ['支持向量机 (SVM)', '随机森林'] },
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{ name: '深度学习革命', time: '2010s', driver: '大数据 + 算力爬升', mechanism: '神经网络自动提取特征', examples: ['AlexNet (CNN)', 'AlphaGo (RL)'] },
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{ name: '大语言模型 (LLM)', time: '2018 - 至今', driver: '海量无标注数据 + 暴力计算', mechanism: '预测下一个词 + 涌现常识', examples: ['GPT-4', 'Claude 3'] },
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{ name: '智能体 (Agentic AI)', time: '现在 - 未来', driver: '大模型大脑 + 环境感知', mechanism: '自主规划 + 工具调用', examples: ['AI 程序员', '具身智能'] }
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]
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}
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}
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