Knowledge Evolution Explorer

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Knowledge Evolution Explorer | 知識演進探索器

Knowledge Evolution Explorer

Journey through millennia of human understanding - from ancient wisdom to modern insights

The Great Timeline of Knowledge Evolution

Ancient Greece (6th-4th Century BCE)

Socratic method revolutionizes learning through systematic questioning and dialogue. Philosophy emerges as rigorous inquiry into fundamental questions.

Scientific Revolution (16th-17th Century)

Galileo, Newton, and others establish experimental method. Mathematics becomes the language of nature. Paradigm shift from authority to evidence.

Digital Revolution (20th-21st Century)

Computers amplify human cognition. Internet creates global knowledge network. AI begins to augment and challenge human reasoning.

Interactive Timeline Visualization

Ancient Wisdom: The Foundations of Knowledge

Socratic Method Simulator

Socrates:
What do you think knowledge truly is?

Aristotelian Classification

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Interactive Classification Tree

Ancient Astronomical Observations

Modern Science: The New Paradigms

Data Science Visualization

Data insights will appear here...

Quantum Thinking Simulator

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Quantum Superposition Visualization

|ψ⟩ = α|0⟩ + β|1⟩

AI-Assisted Knowledge Discovery

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Machine Learning

Pattern recognition at unprecedented scale

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Neural Networks

Biomimetic information processing

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Global Knowledge Graph

Interconnected human understanding

Ancient vs Modern: A Knowledge Comparison

🏛️ Ancient Approach

  • Intuitive Reasoning

    Reliance on first principles and logical deduction

  • Authority-Based

    Knowledge validated by respected sources

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🔬 Modern Approach

  • Empirical Evidence

    Data-driven conclusions and statistical validation

  • Peer Review

    Collaborative validation through scientific community

Interactive Comparison Tool

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Select a domain to see detailed comparison

Discovery Laboratory: Experience Scientific Breakthroughs

Galileo's Telescope

Use controls to observe celestial bodies

Newton's Laws Simulator

Interactive Discovery Quiz

What was the key insight that led to Darwin's theory of evolution?

Choose an answer to see explanation

Wisdom Dialogue: Conversations Across Time

Imaginary Debate: Ancient vs Modern Thinkers

Socrates (470-399 BCE):
The unexamined life is not worth living. How can your modern data truly lead to wisdom without deep self-reflection?
Marie Curie (1867-1934):
Nothing in life is to be feared, it is only to be understood. Data and observation allow us to understand the fundamental nature of reality itself.
Confucius (551-479 BCE):
Real knowledge is to know the extent of one's ignorance. Your machines may calculate, but do they cultivate virtue and human relationships?
Alan Turing (1912-1954):
We can only see a short distance ahead, but we can see plenty there that needs to be done. Machines extend human capability, including moral reasoning.

Knowledge Synthesis Exercise

Combine ancient wisdom with modern insights to solve contemporary problems:

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Select a problem to begin synthesis

Personal Knowledge Journey

Track your learning progress through different eras of knowledge:

Ancient Wisdom 75%
Modern Science 60%
Future Thinking 45%

此網誌的熱門文章

自訂網路結構的神經網路訓練與預測 (動畫＋公式＋損失／激活函數

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自訂網路結構的神經網路訓練與預測 (動畫＋公式＋損失／激活函數) 自訂網路結構的神經網路訓練與預測 此範例示範一個多層神經網路，其網路結構、激活函數及損失函數可依需求自訂。 訓練與預測均以動畫展示前向傳播（並於訓練時進行反向傳播更新權重），搭配公式與統計資訊。 深入了解神經網路的基本元素與計算方法 在本範例中，我們介紹神經網路中的各個核心基本元素，如參數與超參數，並說明它們如何在模型計算與實際應用中發揮作用。 參數 (Parameters) 參數主要指神經網路中的權重 (Weights) 與偏置 (Biases)。在前向傳播時， 輸入資料依據公式： a = activation(Σ(x * weight) + bias) 其中，x 為輸入值、weight 為權重、bias 為偏置，activation 則代表激活函數，用以引入非線性。權重與偏置在訓練過程中透過反向傳播 (Backpropagation) 依據損失函數的梯度進行更新，以達到模型調整誤差的效果。 超參數 (Hyperparameters) 超參數是在訓練前由使用者設定的參數，不會在訓練過程中自動調整。常見超參數包括： 學習率 (Learning Rate)： 控制每次參數更新的步幅，影響收斂速度與穩定性。 Epoch 次數： 表示整個資料集被完整遍歷的次數，決定訓練強度與時間。 網路結構設定： 包含輸入層、隱藏層與輸出層神經元的數量，直接決定模型表達能力。 激活函數與損失函數： 分別決定每層的非線性處理與模型誤差的計算方式。 合理配置超參數能夠有效避免過擬合或欠擬合，並確保網路運算的穩定與高效。舉例來說，若學習率過高，可能會使參數更新過度，導致模型難以收斂；相反，學習率過低時則會使訓練變慢。 基本原理與實際應用 神經網路的運算流程主要分為前向傳播與反向傳播兩大步驟： ...
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Customizable Neural Network Training and Prediction (Animations + Formulas + Loss/Activation Functions)

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Customizable Neural Network Training and Prediction (Animations + Formulas + Loss/Activation Functions) Customizable Neural Network Training and Prediction This example demonstrates a multi-layer neural network where you can customize the network structure, activation functions, and loss functions as needed. Both training and prediction are shown with animations illustrating forward propagation (with back propagation update during training), accompanied by formulas and statistical information. In-depth Understanding of Neural Network Basic Elements and Calculation Methods In this example, we introduce the core components of a neural network—parameters and hyperparameters—and explain how they function in model computation and practical applications. Parameters Parameters refer mainly to the weights and biases within the network. During forward propagation, input data is computed as: a = activa...
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