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
Definition of Artificial Intelligence (AI)
There is no precise, commonly accepted definition of AI, but…
AI is the field of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include problem-solving, learning, reasoning, perception, natural language understanding, and decision-making.
Types of AI
AI can be classified based on capability and functionality:
1. Based on Capabilities
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Narrow AI (Weak AI):
- Specializes in a specific task (e.g., voice assistants, recommendation systems).
- Examples: Siri, Google Translate, AlphaGo.
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General AI (Strong AI):
- Can perform any intellectual task that a human can.
- Not yet achieved but remains the goal of AI research.
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Super AI:
- Hypothetical AI surpassing human intelligence.
- Could potentially exhibit self-awareness and independent decision-making.
2. Based on Functionality
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Reactive AI:
- Responds to stimuli but lacks memory.
- Example: IBM’s Deep Blue (chess-playing AI).
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Limited Memory AI:
- Learns from past data and makes decisions.
- Example: Self-driving cars.
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Theory of Mind AI (Not yet developed):
- Would understand emotions, beliefs, and intentions.
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Self-Aware AI (Hypothetical):
- Would have consciousness and self-awareness.
Key AI Techniques
- Machine Learning (ML): AI learns from data.
- Deep Learning (DL): Uses artificial neural networks for complex tasks.
- Natural Language Processing (NLP): AI understands and generates human language.
- Computer Vision: AI interprets and processes images or videos.
- Robotics: AI powers autonomous physical machines.
Definitions of Artificial Intelligence
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IBM: Defines AI as “technology that enables computers and machines to simulate human learning, comprehension, problem-solving, decision-making, creativity, and autonomy.” ibm.com
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NASA: Describes AI as “computer systems that can perform complex tasks normally done by human reasoning, decision-making, creating, etc.” nasa.gov
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McKinsey & Company: States that AI is “a machine’s ability to perform the cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, and even exercising creativity.” mckinsey.com