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

docs/.vitepress/theme/locales/ai-history/en.js

@@ -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']
+      }
     ]
   }
 }