sanbuphy
621 lines
15 KiB
Vue
621 lines
15 KiB
Vue
<template>
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<div class="ai-evolution-demo">
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<!-- Timeline -->
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<div class="timeline-container">
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<div class="timeline-track"></div>
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<button
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v-for="(stage, index) in stages"
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:key="index"
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class="timeline-node"
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:class="{
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active: currentStage === index,
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passed: currentStage > index
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}"
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@click="currentStage = index"
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>
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<div class="node-dot">
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<div class="inner-dot"></div>
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</div>
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<div class="node-content">
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<span class="year-badge">{{ stage.year }}</span>
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<span class="node-label">{{ stage.label }}</span>
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</div>
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</button>
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</div>
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<!-- Content -->
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<div class="content-wrapper">
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<transition name="fade-slide" mode="out-in">
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<div :key="currentStage" class="stage-content">
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<div class="header-section">
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<h3>
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<span class="stage-index"
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>{{ indexToRoman(currentStage + 1) }}.</span
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>
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{{ stages[currentStage].title }}
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</h3>
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<p>{{ stages[currentStage].desc }}</p>
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</div>
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<div class="visualization-grid">
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<!-- Concept/Logic View -->
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<div class="mac-window concept-window">
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<div class="window-bar">
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<div class="traffic-lights">
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<span class="light red"></span>
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<span class="light yellow"></span>
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<span class="light green"></span>
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</div>
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<div class="window-title">Core Logic</div>
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</div>
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<div class="concept-canvas">
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<!-- Stage 0: Symbolism -->
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<div v-if="currentStage === 0" class="vis-symbolism">
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<div class="logic-gate">
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<div class="input-group">
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<span class="input-val">A: True</span>
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<span class="input-val">B: False</span>
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</div>
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<div class="gate-box">AND Rule</div>
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<div class="output-val">Output: False</div>
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</div>
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<div class="math-note">If A and B then C</div>
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</div>
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<!-- Stage 1: Expert Systems -->
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<div v-if="currentStage === 1" class="vis-expert">
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<div class="decision-tree">
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<div class="tree-node root">Is it raining?</div>
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<div class="branches">
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<div class="branch">
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<span class="condition">Yes</span>
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<div class="tree-node leaf">Take Umbrella</div>
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</div>
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<div class="branch">
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<span class="condition">No</span>
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<div class="tree-node leaf">Go Out</div>
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</div>
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</div>
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</div>
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<div class="kb-note">Knowledge Base + Inference Engine</div>
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</div>
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<!-- Stage 2: Deep Learning -->
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<div v-if="currentStage === 2" class="vis-dl">
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<div class="neural-net">
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<div class="layer input">
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<div class="neuron" v-for="n in 3" :key="`i-${n}`"></div>
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</div>
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<div class="layer hidden">
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<div class="neuron" v-for="n in 4" :key="`h-${n}`"></div>
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</div>
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<div class="layer output">
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<div class="neuron" v-for="n in 2" :key="`o-${n}`"></div>
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</div>
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<!-- Connections drawn via CSS/SVG ideally, simplified here -->
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<svg class="connections">
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<line x1="10" y1="20" x2="60" y2="10" />
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<line x1="10" y1="20" x2="60" y2="30" />
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<!-- Abstract lines -->
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</svg>
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</div>
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<div class="dl-note">Feature Extraction (Black Box)</div>
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</div>
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<!-- Stage 3: GenAI -->
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<div v-if="currentStage === 3" class="vis-genai">
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<div class="transformer-block">
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<div class="block-layer attn">Self-Attention</div>
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<div class="block-layer ff">Feed Forward</div>
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<div class="block-layer norm">Norm & Add</div>
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</div>
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<div class="chat-sim">
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<div class="msg user">"Draw a cat"</div>
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<div class="msg ai">Generates 🐱...</div>
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</div>
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</div>
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</div>
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</div>
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<!-- Application/Impact View -->
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<div class="mac-window app-window">
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<div class="window-bar">
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<div class="window-title">Real-world Impact</div>
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</div>
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<div class="app-canvas">
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<div class="impact-card">
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<div class="impact-icon">{{ stages[currentStage].icon }}</div>
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<div class="impact-title">
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{{ stages[currentStage].appTitle }}
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</div>
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<div class="impact-desc">
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{{ stages[currentStage].appDesc }}
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</div>
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</div>
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</div>
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</div>
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</div>
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</div>
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</transition>
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</div>
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</div>
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</template>
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<script setup>
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import { ref } from 'vue'
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const currentStage = ref(0)
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const indexToRoman = (num) => {
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const map = { 1: 'I', 2: 'II', 3: 'III', 4: 'IV' }
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return map[num] || num
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}
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const stages = [
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{
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year: '1950s-1970s',
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label: 'Symbolism',
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title: 'The Dawn: Logic & Rules',
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desc: 'AI started as "Symbolic AI". Scientists believed intelligence could be described by formal logic and rules. If we can write down all the rules of the world, a computer can be intelligent.',
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icon: '♟️',
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appTitle: 'Chess & Logic',
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appDesc:
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'Programs could solve logic puzzles and play simple chess, but failed at "common sense" or recognizing a cat in a photo.'
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},
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{
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year: '1980s-1990s',
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label: 'Expert Systems',
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title: 'Knowledge Engineering',
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desc: 'The era of "Expert Systems". We tried to hard-code human expertise (e.g., medical diagnosis rules) into databases. Useful for specific domains, but brittle and hard to maintain.',
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icon: '🏥',
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appTitle: 'MYCIN / Deep Blue',
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appDesc:
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'Systems that could diagnose blood infections or beat Garry Kasparov at chess (Deep Blue, 1997), but still lacked true learning capability.'
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},
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{
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year: '2010s',
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label: 'Deep Learning',
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title: 'Connectionism & Big Data',
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desc: 'The breakthrough of Neural Networks. Inspired by the human brain, computers learned patterns from massive data instead of being told rules. AlexNet (2012) changed everything.',
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icon: '🧠',
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appTitle: 'AlphaGo & FaceID',
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appDesc:
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'AI learned to see (ImageNet), hear (Siri), and play Go (AlphaGo). It surpassed humans in specific perceptual tasks.'
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},
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{
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year: '2020s+',
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label: 'Generative AI',
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title: 'Generative Intelligence (LLMs)',
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desc: 'The Transformer architecture allowed AI to understand context and generate new content. AI moved from "classifying" (is this a cat?) to "creating" (draw a cat).',
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icon: '✨',
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appTitle: 'ChatGPT & Midjourney',
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appDesc:
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'AI that can write code, poetry, paint images, and reason across multiple domains. A step towards AGI (General Intelligence).'
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}
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]
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</script>
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<style scoped>
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.ai-evolution-demo {
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border-radius: 8px;
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background: var(--vp-c-bg-soft);
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border: 1px solid var(--vp-c-divider);
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overflow: hidden;
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margin: 1rem 0;
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}
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/* Reusing Timeline Styles from FrontendEvolutionDemo for consistency */
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.timeline-container {
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padding: 2rem 1rem 1rem;
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background: var(--vp-c-bg-soft);
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display: flex;
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justify-content: space-between;
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position: relative;
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border-bottom: 1px solid var(--vp-c-divider);
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}
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.timeline-track {
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position: absolute;
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top: 2.5rem;
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left: 3rem;
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right: 3rem;
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height: 2px;
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background: var(--vp-c-divider);
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z-index: 0;
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}
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.timeline-node {
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position: relative;
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z-index: 1;
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background: transparent;
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border: none;
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display: flex;
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flex-direction: column;
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align-items: center;
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cursor: pointer;
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padding: 0;
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width: 25%;
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transition: all 0.3s ease;
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opacity: 0.6;
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}
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.timeline-node:hover {
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opacity: 0.9;
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}
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.timeline-node.active,
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.timeline-node.passed {
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opacity: 1;
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}
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.node-dot {
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width: 16px;
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height: 16px;
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border-radius: 50%;
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background: var(--vp-c-bg);
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border: 2px solid var(--vp-c-text-3);
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margin-bottom: 0.8rem;
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display: flex;
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align-items: center;
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justify-content: center;
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transition: all 0.3s;
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}
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.inner-dot {
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width: 0;
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height: 0;
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border-radius: 50%;
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background: var(--vp-c-brand);
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transition: all 0.3s;
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}
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.timeline-node.active .node-dot {
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border-color: var(--vp-c-brand);
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transform: scale(1.3);
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box-shadow: 0 0 0 4px var(--vp-c-bg-soft);
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}
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.timeline-node.active .inner-dot {
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width: 8px;
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height: 8px;
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}
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.timeline-node.passed .node-dot {
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border-color: var(--vp-c-brand);
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background: var(--vp-c-brand);
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}
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.node-content {
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text-align: center;
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display: flex;
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flex-direction: column;
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align-items: center;
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gap: 0.2rem;
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}
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.year-badge {
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font-size: 0.75rem;
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font-family: var(--vp-font-family-mono);
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background: var(--vp-c-bg-alt);
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padding: 2px 6px;
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border-radius: 4px;
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color: var(--vp-c-text-2);
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}
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.node-label {
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font-size: 0.85rem;
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font-weight: 600;
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color: var(--vp-c-text-1);
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}
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/* Content Area */
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.content-wrapper {
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padding: 2rem;
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min-height: 400px;
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}
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.header-section {
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text-align: center;
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margin-bottom: 2rem;
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max-width: 600px;
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margin: 0 auto 2rem;
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}
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.header-section h3 {
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font-size: 1.5rem;
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margin-bottom: 0.5rem;
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color: var(--vp-c-text-1);
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}
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.stage-index {
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color: var(--vp-c-text-3);
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-webkit-text-fill-color: var(--vp-c-text-3);
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margin-right: 0.5rem;
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font-weight: normal;
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}
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.header-section p {
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font-size: 1rem;
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color: var(--vp-c-text-2);
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line-height: 1.6;
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}
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/* Visualization */
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.visualization-grid {
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display: grid;
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grid-template-columns: 1fr 1fr;
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gap: 2rem;
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align-items: stretch;
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}
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@media (max-width: 768px) {
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.visualization-grid {
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grid-template-columns: 1fr;
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}
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}
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.mac-window {
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border-radius: 12px;
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border: 1px solid var(--vp-c-divider);
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overflow: hidden;
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display: flex;
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flex-direction: column;
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background: var(--vp-c-bg);
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}
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.concept-window {
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background: var(--vp-c-bg);
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}
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.app-window {
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background: var(--vp-c-bg);
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}
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.window-bar {
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padding: 0.8rem 1rem;
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background: var(--vp-c-bg-soft);
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border-bottom: 1px solid var(--vp-c-divider);
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display: flex;
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align-items: center;
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position: relative;
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}
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.traffic-lights {
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display: flex;
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gap: 6px;
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}
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.light {
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width: 10px;
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height: 10px;
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border-radius: 50%;
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}
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.light.red {
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background: var(--vp-c-red-1, #ef4444);
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}
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.light.yellow {
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background: var(--vp-c-yellow-1, #f59e0b);
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}
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.light.green {
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background: var(--vp-c-green-1, #22c55e);
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}
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.window-title {
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position: absolute;
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left: 0;
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right: 0;
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text-align: center;
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font-size: 0.8rem;
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color: var(--vp-c-text-2);
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font-weight: 600;
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}
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.concept-canvas,
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.app-canvas {
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padding: 2rem;
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flex: 1;
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display: flex;
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align-items: center;
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justify-content: center;
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min-height: 250px;
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}
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/* Visualizations */
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/* Symbolism */
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.logic-gate {
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border: 2px solid var(--vp-c-divider);
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padding: 1rem;
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border-radius: 8px;
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text-align: center;
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background: var(--vp-c-bg);
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}
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.input-group {
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display: flex;
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gap: 1rem;
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justify-content: center;
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margin-bottom: 0.5rem;
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font-family: var(--vp-font-family-mono);
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}
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.gate-box {
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background: var(--vp-c-brand);
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color: var(--vp-c-bg);
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padding: 4px 10px;
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margin: 0.5rem 0;
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border-radius: 4px;
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}
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.math-note {
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margin-top: 1rem;
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font-family: var(--vp-font-family-mono);
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color: var(--vp-c-text-2);
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font-size: 0.8rem;
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}
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/* Expert Systems */
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.decision-tree {
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display: flex;
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flex-direction: column;
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align-items: center;
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gap: 1rem;
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}
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.tree-node {
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border: 1px solid var(--vp-c-divider);
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padding: 6px 12px;
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border-radius: 20px;
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background: var(--vp-c-bg);
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font-size: 0.8rem;
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color: var(--vp-c-text-1);
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}
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.tree-node.root {
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border-color: var(--vp-c-brand);
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color: var(--vp-c-brand);
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font-weight: bold;
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}
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.branches {
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display: flex;
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gap: 2rem;
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}
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.branch {
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display: flex;
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flex-direction: column;
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align-items: center;
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gap: 0.5rem;
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}
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.condition {
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font-size: 0.7rem;
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color: var(--vp-c-text-2);
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background: var(--vp-c-bg-alt);
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padding: 2px 6px;
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border-radius: 4px;
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}
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.kb-note {
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margin-top: 1rem;
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font-size: 0.8rem;
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color: var(--vp-c-text-2);
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font-style: italic;
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}
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/* Deep Learning */
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.neural-net {
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display: flex;
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gap: 2rem;
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align-items: center;
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position: relative;
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}
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.layer {
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display: flex;
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flex-direction: column;
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gap: 0.5rem;
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}
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.neuron {
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width: 12px;
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height: 12px;
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border-radius: 50%;
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background: var(--vp-c-bg-alt);
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border: 1px solid var(--vp-c-divider);
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}
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.layer.input .neuron {
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background: rgba(var(--vp-c-brand-rgb), 0.25);
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}
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.layer.hidden .neuron {
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background: rgba(var(--vp-c-brand-rgb), 0.18);
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}
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.layer.output .neuron {
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background: rgba(var(--vp-c-brand-rgb), 0.12);
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}
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.connections {
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position: absolute;
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top: 0;
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left: 0;
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width: 100%;
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height: 100%;
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pointer-events: none;
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opacity: 0.2;
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}
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.connections line {
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stroke: var(--vp-c-text-2);
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stroke-width: 1;
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}
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.dl-note {
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margin-top: 2rem;
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font-size: 0.8rem;
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color: var(--vp-c-text-2);
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}
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/* GenAI */
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.vis-genai {
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display: flex;
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flex-direction: column;
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gap: 1rem;
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align-items: center;
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width: 100%;
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}
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.transformer-block {
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border: 2px solid var(--vp-c-brand);
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border-radius: 8px;
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padding: 0.5rem;
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width: 120px;
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text-align: center;
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background: rgba(var(--vp-c-brand-rgb), 0.08);
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|
}
|
|
.block-layer {
|
|
border: 1px solid var(--vp-c-divider);
|
|
background: var(--vp-c-bg);
|
|
margin: 4px 0;
|
|
padding: 4px;
|
|
font-size: 0.7rem;
|
|
border-radius: 4px;
|
|
}
|
|
.chat-sim {
|
|
width: 100%;
|
|
border: 1px solid var(--vp-c-divider);
|
|
border-radius: 8px;
|
|
padding: 1rem;
|
|
background: var(--vp-c-bg);
|
|
font-size: 0.8rem;
|
|
}
|
|
.msg {
|
|
padding: 6px 10px;
|
|
border-radius: 12px;
|
|
margin-bottom: 0.5rem;
|
|
max-width: 80%;
|
|
}
|
|
.msg.user {
|
|
background: rgba(var(--vp-c-brand-rgb), 0.1);
|
|
margin-left: auto;
|
|
color: var(--vp-c-text-1);
|
|
}
|
|
.msg.ai {
|
|
background: var(--vp-c-bg-soft);
|
|
margin-right: auto;
|
|
color: var(--vp-c-text-1);
|
|
}
|
|
|
|
/* Impact Card */
|
|
.impact-card {
|
|
text-align: center;
|
|
}
|
|
.impact-icon {
|
|
font-size: 4rem;
|
|
margin-bottom: 1rem;
|
|
}
|
|
.impact-title {
|
|
font-size: 1.2rem;
|
|
font-weight: bold;
|
|
margin-bottom: 0.5rem;
|
|
color: var(--vp-c-text-1);
|
|
}
|
|
.impact-desc {
|
|
font-size: 0.9rem;
|
|
color: var(--vp-c-text-2);
|
|
line-height: 1.5;
|
|
}
|
|
|
|
/* Transitions */
|
|
.fade-slide-enter-active,
|
|
.fade-slide-leave-active {
|
|
transition: all 0.4s ease;
|
|
}
|
|
.fade-slide-enter-from {
|
|
opacity: 0;
|
|
transform: translateY(20px);
|
|
}
|
|
.fade-slide-leave-to {
|
|
opacity: 0;
|
|
transform: translateY(-20px);
|
|
}
|
|
</style>
|