AI Safety Fundamentals A Short Introduction to Machine Learning
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Jan 4, 2025 Dive into an engaging overview of machine learning, exploring its key concepts and their connections. Discover the distinction between symbolic AI and learning, while unraveling the mysteries of neural networks and optimization techniques. Learn about supervised and reinforcement learning, including challenges like credit assignment. The discussion highlights the journey from training models to their deployment in the real world, emphasizing the implications for AI safety. This framework provides a refreshing lens for both newcomers and seasoned AI enthusiasts.
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Layered Taxonomy Connects Goals To Methods
- AI splits into tasks (what to do) and techniques (how to do it), forming a layered taxonomy.
- This framing helps relate high-level goals to specific ML methods and architectures.
Deep Blue Showed Symbolic AI Strengths
- Symbolic search excelled in chess, exemplified by Deep Blue beating Kasparov in 1997.
- That success relied on explicit rules and search rather than learned parameters.
Learning Replaces Hand-Coding At Scale
- Machine learning learns model parameters from data instead of hard-coding rules.
- Deep learning scaled success by training very large neural networks with lots of compute and data.