sanbuphy
209 lines
6.0 KiB
JavaScript
209 lines
6.0 KiB
JavaScript
// 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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{
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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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},
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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:
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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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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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{
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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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// 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, 0.1, 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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{
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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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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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{
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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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