Artificial Intelligence (AI) has evolved through several key phases since its inception. Here's a concise overview:
1. Early Foundations (1950s-1970s)
💡 1950: Alan Turing publishes the Turing Test, defining machine intelligence.
📚 1956: The term "Artificial Intelligence" is coined at the Dartmouth Conference.
🤖 1960s-1970s: Early AI systems like ELIZA (chatbot) and Dendral (chemistry analysis) emerge.
⚠️ Challenges: Limited computational power and data scarcity hinder progress.
2. Knowledge-Based Systems (1980s)
🧠 Expert Systems: Rule-based programs like MYCIN (medical diagnosis) dominate.
🔍 Symbolic AI: Focus on logic, reasoning, and knowledge representation.
📉 Decline: The "AI Winter" begins due to overhyped expectations and technical limits.
3. Machine Learning Revolution (1990s-2000s)
📈 Statistical Learning: Algorithms like SVM and Naive Bayes gain traction.
🌐 Big Data: Increased data availability fuels AI growth.
🧠 Neural Networks: Backpropagation enables deeper learning models.
🤖 2006: Geoffrey Hinton's work on deep learning marks a turning point.
4. Deep Learning Era (2010s-Present)
🧠 2012: AlexNet wins ImageNet competition, sparking interest in CNNs.
📊 2015: Google's DeepMind develops AlphaGo, defeating a human champion.
🌐 2020s: Generative AI (e.g., GPT-3, BERT) transforms NLP and beyond.
🚀 Current Trends: AI integration in healthcare, autonomous systems, and quantum computing.
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