feat: complete English translation of AI IDE introduction including Appendix 2

2026-02-26 12:17:40 +08:00
parent 7cd26c59d8
commit b2e3ffa1fd
23 changed files with 3762 additions and 2087 deletions
@@ -48,13 +48,45 @@ export default {
  foundation: {
    label: 'Core idea of Symbolism — encoding knowledge as rules',
    lines: [
-      { parts: [{ kw: 'IF' }, { text: '  temperature > 38.5°C  ' }, { kw: 'AND' }, { text: '  WBC count > 11000' }] },
-      { indent: true, parts: [{ kw: 'THEN' }, { text: '  diagnosis = ' }, { str: '"bacterial infection"' }] },
-      { parts: [{ kw: 'IF' }, { text: '  diagnosis = ' }, { str: '"bacterial infection"' }, { text: '  ' }, { kw: 'AND' }, { text: '  no penicillin allergy' }] },
-      { indent: true, parts: [{ kw: 'THEN' }, { text: '  treatment = ' }, { str: '"penicillin 400mg / twice daily"' }] }
+      {
+        parts: [
+          { kw: 'IF' },
+          { text: '  temperature > 38.5°C  ' },
+          { kw: 'AND' },
+          { text: '  WBC count > 11000' }
+        ]
+      },
+      {
+        indent: true,
+        parts: [
+          { kw: 'THEN' },
+          { text: '  diagnosis = ' },
+          { str: '"bacterial infection"' }
+        ]
+      },
+      {
+        parts: [
+          { kw: 'IF' },
+          { text: '  diagnosis = ' },
+          { str: '"bacterial infection"' },
+          { text: '  ' },
+          { kw: 'AND' },
+          { text: '  no penicillin allergy' }
+        ]
+      },
+      {
+        indent: true,
+        parts: [
+          { kw: 'THEN' },
+          { text: '  treatment = ' },
+          { str: '"penicillin 400mg / twice daily"' }
+        ]
+      }
    ],
-    comment: '// The early medical expert system MYCIN (1977) consisted of 450+ rules like these',
-    caption: 'Human experts translate experience into IF-THEN rules; the machine matches and executes them one by one'
+    comment:
+      '// The early medical expert system MYCIN (1977) consisted of 450+ rules like these',
+    caption:
+      'Human experts translate experience into IF-THEN rules; the machine matches and executes them one by one'
  },

  // PerceptronDemo
@@ -63,16 +95,33 @@ export default {
    biasLabel: 'Bias',
    activated: 'Fire',
    silent: 'Silent',
-    caption: '① Input features\u2003② Multiply by weights (importance)\u2003③ Sum + bias\u2003④ Fires output 1 if above threshold, otherwise 0'
+    caption:
+      '① Input features\u2003② Multiply by weights (importance)\u2003③ Sum + bias\u2003④ Fires output 1 if above threshold, otherwise 0'
  },

  // BackpropagationDemo
  backprop: {
    steps: [
-      { icon: '➡️', name: 'Forward Pass', desc: 'Data flows through the network to produce a prediction' },
-      { icon: '📐', name: 'Compute Loss', desc: 'Prediction vs. ground truth → calculate loss' },
-      { icon: '⬅️', name: 'Backpropagation', desc: 'Trace back each weight\'s "responsibility" layer by layer' },
-      { icon: '⚙️', name: 'Update Weights', desc: 'Adjust proportionally to reduce future error' }
+      {
+        icon: '➡️',
+        name: 'Forward Pass',
+        desc: 'Data flows through the network to produce a prediction'
+      },
+      {
+        icon: '📐',
+        name: 'Compute Loss',
+        desc: 'Prediction vs. ground truth → calculate loss'
+      },
+      {
+        icon: '⬅️',
+        name: 'Backpropagation',
+        desc: 'Trace back each weight\'s "responsibility" layer by layer'
+      },
+      {
+        icon: '⚙️',
+        name: 'Update Weights',
+        desc: 'Adjust proportionally to reduce future error'
+      }
    ],
    lossLabel: 'Loss decreases over training epochs:',
    axisHigh: 'High',
@@ -84,7 +133,10 @@ export default {
  neuralNet: {
    layers: [
      { name: 'Input Layer', desc: 'Raw pixels / numerical signals' },
-      { name: 'Hidden Layers (stackable)', desc: 'Low → edges; Mid → shapes; High → semantic concepts' },
+      {
+        name: 'Hidden Layers (stackable)',
+        desc: 'Low → edges; Mid → shapes; High → semantic concepts'
+      },
      { name: 'Output Layer', desc: 'Final classification or prediction' }
    ]
  },
@@ -94,16 +146,41 @@ export default {
    colLabel: 'Attention distribution when processing "{word}":',
    sentence: ['John', 'gave', 'the', 'apple', 'to', 'his', 'mother'],
    focusIdx: 5,
-    weights: [0.62, 0.08, 0.03, 0.10, 0.05, 0.07, 0.05],
-    caption: '"his" sits mid-sentence, yet the model directs 62% attention to "John" at the start — resolving the pronoun across distance'
+    weights: [0.62, 0.08, 0.03, 0.1, 0.05, 0.07, 0.05],
+    caption:
+      '"his" sits mid-sentence, yet the model directs 62% attention to "John" at the start — resolving the pronoun across distance'
  },

  // GPTEvolutionDemo
  gptEvolution: [
-    { name: 'GPT-1', year: '2018', params: '117 M', barWidth: '2%', key: 'Pre-train + fine-tune paradigm' },
-    { name: 'GPT-2', year: '2019', params: '1.5 B', barWidth: '6%', key: 'Zero-shot generalization' },
-    { name: 'GPT-3', year: '2020', params: '175 B', barWidth: '45%', key: '⚡ Emergence! In-context learning' },
-    { name: 'GPT-4', year: '2023', params: '~1.8 T', barWidth: '100%', key: 'Multimodal + complex reasoning' }
+    {
+      name: 'GPT-1',
+      year: '2018',
+      params: '117 M',
+      barWidth: '2%',
+      key: 'Pre-train + fine-tune paradigm'
+    },
+    {
+      name: 'GPT-2',
+      year: '2019',
+      params: '1.5 B',
+      barWidth: '6%',
+      key: 'Zero-shot generalization'
+    },
+    {
+      name: 'GPT-3',
+      year: '2020',
+      params: '175 B',
+      barWidth: '45%',
+      key: '⚡ Emergence! In-context learning'
+    },
+    {
+      name: 'GPT-4',
+      year: '2023',
+      params: '~1.8 T',
+      barWidth: '100%',
+      key: 'Multimodal + complex reasoning'
+    }
  ],

  // AIErasComparisonDemo
@@ -113,11 +190,41 @@ export default {
    mechanismLabel: 'Core Mechanism',
    examplesLabel: 'Key Examples',
    eras: [
-      { name: 'Rule-Based Era', time: '1960s - 1980s', driver: 'Human-coded knowledge', mechanism: 'If-Then logical deduction', examples: ['Dendral', 'Deep Blue'] },
-      { name: 'Classical ML', time: '1990s - 2000s', driver: 'Manual feature engineering + statistics', mechanism: 'Finding mathematical decision boundaries', examples: ['SVM', 'Random Forest'] },
-      { name: 'Deep Learning Revolution', time: '2010s', driver: 'Big data + GPU compute', mechanism: 'Neural nets auto-extract features', examples: ['AlexNet (CNN)', 'AlphaGo (RL)'] },
-      { 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'] },
-      { name: 'Agentic AI', time: 'Now - future', driver: 'LLM brain + environment perception', mechanism: 'Autonomous planning + tool use', examples: ['AI Programmer', 'Embodied AI'] }
+      {
+        name: 'Rule-Based Era',
+        time: '1960s - 1980s',
+        driver: 'Human-coded knowledge',
+        mechanism: 'If-Then logical deduction',
+        examples: ['Dendral', 'Deep Blue']
+      },
+      {
+        name: 'Classical ML',
+        time: '1990s - 2000s',
+        driver: 'Manual feature engineering + statistics',
+        mechanism: 'Finding mathematical decision boundaries',
+        examples: ['SVM', 'Random Forest']
+      },
+      {
+        name: 'Deep Learning Revolution',
+        time: '2010s',
+        driver: 'Big data + GPU compute',
+        mechanism: 'Neural nets auto-extract features',
+        examples: ['AlexNet (CNN)', 'AlphaGo (RL)']
+      },
+      {
+        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']
+      },
+      {
+        name: 'Agentic AI',
+        time: 'Now - future',
+        driver: 'LLM brain + environment perception',
+        mechanism: 'Autonomous planning + tool use',
+        examples: ['AI Programmer', 'Embodied AI']
+      }
    ]
  }
 }
@@ -48,10 +48,36 @@ export default {
  foundation: {
    label: '符号主义的核心思路 ── 把知识写成规则',
    lines: [
-      { parts: [{ kw: 'IF' }, { text: '  体温 > 38.5°C  ' }, { kw: 'AND' }, { text: '  白细胞计数 > 11000' }] },
-      { indent: true, parts: [{ kw: 'THEN' }, { text: '  诊断 = ' }, { str: '"细菌感染"' }] },
-      { parts: [{ kw: 'IF' }, { text: '  诊断 = ' }, { str: '"细菌感染"' }, { text: '  ' }, { kw: 'AND' }, { text: '  对青霉素不过敏' }] },
-      { indent: true, parts: [{ kw: 'THEN' }, { text: '  治疗方案 = ' }, { str: '"青霉素 400mg / 每日两次"' }] }
+      {
+        parts: [
+          { kw: 'IF' },
+          { text: '  体温 > 38.5°C  ' },
+          { kw: 'AND' },
+          { text: '  白细胞计数 > 11000' }
+        ]
+      },
+      {
+        indent: true,
+        parts: [{ kw: 'THEN' }, { text: '  诊断 = ' }, { str: '"细菌感染"' }]
+      },
+      {
+        parts: [
+          { kw: 'IF' },
+          { text: '  诊断 = ' },
+          { str: '"细菌感染"' },
+          { text: '  ' },
+          { kw: 'AND' },
+          { text: '  对青霉素不过敏' }
+        ]
+      },
+      {
+        indent: true,
+        parts: [
+          { kw: 'THEN' },
+          { text: '  治疗方案 = ' },
+          { str: '"青霉素 400mg / 每日两次"' }
+        ]
+      }
    ],
    comment: '// 早期医疗专家系统（MYCIN，1977）就是由 450+ 条这样的规则组成的',
    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'
@@ -63,7 +89,8 @@ export default {
    biasLabel: '偏置',
    activated: '激活',
    silent: '静默',
-    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'
+    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'
  },

  // BackpropagationDemo
@@ -84,7 +111,10 @@ export default {
  neuralNet: {
    layers: [
      { name: '输入层', desc: '原始像素 / 数值信号' },
-      { 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' },
+      {
+        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'
+      },
      { name: '输出层', desc: '最终分类或预测结果' }
    ]
  },
@@ -94,16 +124,41 @@ export default {
    colLabel: '处理「{word}」时的注意力分配：',
    sentence: ['小明', '把', '苹果', '给了', '他', '的', '母亲'],
    focusIdx: 4,
-    weights: [0.65, 0.05, 0.10, 0.10, 0.05, 0.03, 0.02],
-    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'
+    weights: [0.65, 0.05, 0.1, 0.1, 0.05, 0.03, 0.02],
+    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'
  },

  // GPTEvolutionDemo
  gptEvolution: [
-    { name: 'GPT-1', year: '2018', params: '1.17 亿', barWidth: '2%', key: '预训练+微调范式确立' },
-    { name: 'GPT-2', year: '2019', params: '15 亿', barWidth: '6%', key: 'Zero-shot 零样本泛化' },
-    { name: 'GPT-3', year: '2020', params: '1750 亿', barWidth: '45%', key: '⚡ 涌现！上下文学习' },
-    { name: 'GPT-4', year: '2023', params: '~1.8 万亿', barWidth: '100%', key: '多模态 + 复杂推理' }
+    {
+      name: 'GPT-1',
+      year: '2018',
+      params: '1.17 亿',
+      barWidth: '2%',
+      key: '预训练+微调范式确立'
+    },
+    {
+      name: 'GPT-2',
+      year: '2019',
+      params: '15 亿',
+      barWidth: '6%',
+      key: 'Zero-shot 零样本泛化'
+    },
+    {
+      name: 'GPT-3',
+      year: '2020',
+      params: '1750 亿',
+      barWidth: '45%',
+      key: '⚡ 涌现！上下文学习'
+    },
+    {
+      name: 'GPT-4',
+      year: '2023',
+      params: '~1.8 万亿',
+      barWidth: '100%',
+      key: '多模态 + 复杂推理'
+    }
  ],

  // AIErasComparisonDemo
@@ -113,11 +168,41 @@ export default {
    mechanismLabel: '核心机制',
    examplesLabel: '典型代表',
    eras: [
-      { 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)'] },
-      { 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'] },
-      { 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)'] },
-      { 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'] },
-      { 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'] }
+      {
+        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)']
+      },
+      {
+        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']
+      },
+      {
+        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)']
+      },
+      {
+        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']
+      },
+      {
+        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']
+      }
    ]
  }
 }