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Principles of Artificial Intelligence
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Introduction8 Topics|1 Quiz
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Core concepts of Artificial Intelligence15 Topics
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Learning (Machine Learning, Deep Learning, Reinforcement Learning)
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Reasoning (Logical Inference, Decision-Making, Planning Algorithms)
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Perception (Computer Vision, Speech Recognition, NLP)
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Knowledge Representation (Ontologies, Knowledge Graphs, Structured Data)
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Problem-Solving (Search Algorithms, Optimization Techniques)
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Natural Interaction (Human-Computer Interaction, Conversational AI, Virtual Assistants)
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Autonomy (Self-Learning Systems, Autonomous Agents, Robotics)
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Generalization (Transfer Learning, Few-Shot Learning, Zero-Shot Learning)
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Transparency & Explainability (Interpretable Models, Explainable AI, Trustworthiness)
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Fairness & Ethics (Bias Mitigation, Responsible AI, AI for Social Good)
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Robustness & Safety (Adversarial Robustness, Reliability, Fault Tolerance)
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Scalability (Distributed Computing, Cloud AI, Edge AI)
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Computational Efficiency (Hardware Acceleration, Model Compression, Energy-Efficient AI)
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Adaptability (Self-Improving Systems, Meta-Learning, Domain Adaptation)
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Emerging Principles and Considerations
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Learning (Machine Learning, Deep Learning, Reinforcement Learning)
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Important Questions on AI Principles14 Topics
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How do machines learn through different approaches in AI?
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What techniques enable AI to reason and make decisions?
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How do AI systems perceive and understand the world around them?
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How is knowledge represented in AI systems?
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What algorithms do AI systems use for problem-solving?
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How do AI systems interact with humans in a natural way?
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What makes an AI system autonomous?
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How do AI models generalize to new tasks or data?
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Why are transparency and explainability important in AI?
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How do we ensure AI is fair and used ethically?
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How can AI systems be made robust and safe from failure or attack?
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How do AI systems scale to handle very large data and many users?
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What techniques improve the computational efficiency of AI?
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How do AI systems adapt and self-improve when facing new conditions?
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How do machines learn through different approaches in AI?
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Glossary of Key AI Terms
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Recommended literature on Principles of AI
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References on Principles of AI
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Tools for Demonstrating AI Concepts11 Topics
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Machine Learning Basics – Google Teachable Machine
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Machine Learning Basics - ML Playground
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Deep Learning Fundamentals - Runway ML
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Deep Learning Fundamentals – Google Colab
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Natural Language Processing - Hugging Face Transformers Demo
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Reinforcement Learning - OpenAI Gymnasium
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Reinforcement Learning – SimpleGrid
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Generative AI - Image Processing and Computer Vision – DeepAI
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Image Processing and Computer Vision – YOLO
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General AI Demonstration Platforms - AI Experiments by Google
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General AI Demonstration Platforms - Kaggle Kernels
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Machine Learning Basics – Google Teachable Machine
Participants 15
Lesson Progress
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The development of Artificial Intelligence (AI) has gone through several key historical milestones:
Early Foundations (Pre-1950s)
- Ancient Mythologies & Automata: The idea of artificial beings with intelligence appears in mythology (e.g., Talos, a bronze automaton in Greek mythology) and in early mechanical automata (e.g., Al-Jazari’s programmable humanoid robots in the 12th century).
- Mathematical Foundations:
- Gottfried Wilhelm Leibniz (17th century): Early ideas of symbolic logic.
- George Boole (1854): Developed Boolean algebra, which later became fundamental for computing.
- Alan Turing (1936): Proposed the Turing machine, laying the theoretical groundwork for AI.
Birth of AI as a Field (1950s – 1960s)
- 1950: Alan Turing introduces the Turing Test in his paper Computing Machinery and Intelligence.
- 1956: The Dartmouth Conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon, officially introduces the term “Artificial Intelligence” and launches AI as an academic discipline.
- 1958: John McCarthy develops LISP, the primary AI programming language for decades.
- 1965: Joseph Weizenbaum creates ELIZA, one of the first chatbots.
AI Boom and Expert Systems (1970s – 1980s)
- 1970s: Development of expert systems, such as DENDRAL (for chemical analysis) and MYCIN (for medical diagnosis).
- 1980: AI funding surges due to the success of expert systems.
- 1987: The AI Winter begins due to high expectations and slow progress.
Machine Learning and Neural Networks Resurgence (1990s – 2000s)
- 1997: IBM’s Deep Blue defeats world chess champion Garry Kasparov.
- 1998: LeNet (Yann LeCun) advances convolutional neural networks (CNNs) for handwriting recognition.
- 2006: Geoffrey Hinton and others rekindle interest in deep learning.
Deep Learning and Modern AI Revolution (2010s – Present)
- 2012: AlexNet wins the ImageNet challenge, leading to a deep learning revolution.
- 2016: AlphaGo defeats Go champion Lee Sedol.
- 2020s: AI applications expand with transformer models (GPT, BERT, etc.), autonomous vehicles, robotics, and more.