feat: complete English translation of AI IDE introduction including Appendix 2
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sanbuphy
@@ -48,10 +48,36 @@ export default {
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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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parts: [
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{ kw: 'IF' },
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{ text: ' 体温 > 38.5°C ' },
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{ kw: 'AND' },
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{ text: ' 白细胞计数 > 11000' }
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]
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},
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{
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indent: true,
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parts: [{ kw: 'THEN' }, { text: ' 诊断 = ' }, { str: '"细菌感染"' }]
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},
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{
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parts: [
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{ kw: 'IF' },
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{ text: ' 诊断 = ' },
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{ str: '"细菌感染"' },
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{ text: ' ' },
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{ kw: 'AND' },
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{ text: ' 对青霉素不过敏' }
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]
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},
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{
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indent: true,
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parts: [
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{ kw: 'THEN' },
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{ text: ' 治疗方案 = ' },
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{ str: '"青霉素 400mg / 每日两次"' }
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]
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}
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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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@@ -63,7 +89,8 @@ export default {
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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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caption:
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'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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@@ -84,7 +111,10 @@ export default {
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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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{
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name: '隐藏层（可叠加多层）',
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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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},
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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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@@ -94,16 +124,41 @@ export default {
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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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weights: [0.65, 0.05, 0.1, 0.1, 0.05, 0.03, 0.02],
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caption:
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'「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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name: 'GPT-1',
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year: '2018',
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params: '1.17 亿',
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barWidth: '2%',
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key: '预训练+微调范式确立'
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},
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{
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name: 'GPT-2',
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year: '2019',
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params: '15 亿',
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barWidth: '6%',
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key: 'Zero-shot 零样本泛化'
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},
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{
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name: 'GPT-3',
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year: '2020',
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params: '1750 亿',
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barWidth: '45%',
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key: '⚡ 涌现！上下文学习'
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},
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{
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name: 'GPT-4',
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year: '2023',
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params: '~1.8 万亿',
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barWidth: '100%',
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key: '多模态 + 复杂推理'
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}
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],
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// AIErasComparisonDemo
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@@ -113,11 +168,41 @@ export default {
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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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name: '规则系统时代',
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time: '1960s - 1980s',
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driver: '人类硬编码知识',
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mechanism: 'If-Then 逻辑推演',
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examples: ['Dendral', '深蓝 (Deep Blue)']
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},
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{
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name: '传统机器学习',
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time: '1990s - 2000s',
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driver: '人工特征工程 + 统计学',
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mechanism: '寻找数学决策边界',
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examples: ['支持向量机 (SVM)', '随机森林']
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},
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{
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name: '深度学习革命',
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time: '2010s',
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driver: '大数据 + 算力爬升',
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mechanism: '神经网络自动提取特征',
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examples: ['AlexNet (CNN)', 'AlphaGo (RL)']
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},
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{
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name: '大语言模型 (LLM)',
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time: '2018 - 至今',
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driver: '海量无标注数据 + 暴力计算',
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mechanism: '预测下一个词 + 涌现常识',
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examples: ['GPT-4', 'Claude 3']
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},
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{
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name: '智能体 (Agentic AI)',
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time: '现在 - 未来',
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driver: '大模型大脑 + 环境感知',
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mechanism: '自主规划 + 工具调用',
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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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}
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