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link: piped.video/watch?v=y1fGlAEC…
my notes:
THINGS YOU WILL LEARN
1. How foundation models learn meaning through relational context rather than explicit rules
2. Why self-supervised learning is the only scalable approach for real-world AI applications
3. The philosophical shift from top-down design to bottom-up learning in AI systems
4. How to apply next-token prediction and contrastive learning methods to extract knowledge from data
5. Why supervised learning and reinforcement learning fail at scale in chaotic environments
6. How to build general-purpose AI systems that work across scientific and business domains
FOUNDATION MODELS & GENERATIVE AI FUNDAMENTALS
- Foundation models work by learning meaning through relational context rather than explicit labeling
- Self-supervised learning enables models to extract knowledge by observing patterns in unlabeled data
- The breakthrough comes from training massive models on enormous datasets to capture as many relationships as possible
- These models become general-purpose tools applicable across domains once they understand relational structures
PHILOSOPHICAL SHIFT IN AI APPROACH
- Traditional AI relied on top-down design and explicit rule-based systems that don't scale to real-world chaos
- The world is fundamentally chaotic at human-scale interactions, requiring bottom-up learning approaches
- Neural networks are our best tool for navigating chaotic environments, mimicking how brains process information
- Learning from observation is the only scalable approach since supervised learning is expensive and reinforcement learning is dangerous
LEARNING PARADIGM COMPARISON
- Supervised learning fails because labeling everything is impossible and humans can't articulate all knowledge
- Reinforcement learning is too risky and slow for complex real-world environments
- Self-supervised learning succeeds by extracting supervision signals directly from data structure
- Meaning emerges from correlations and contrasts between concepts appearing in similar contexts
SELF-SUPERVISED LEARNING METHODS
- Next-token prediction forces models to understand grammar, semantics, knowledge, and context
- Contrastive learning pushes related items closer and unrelated items apart in representation space
- Both approaches scale infinitely with available data without requiring human annotation
- Models implicitly learn complex patterns that would take humans years to identify and formalize
PRACTICAL APPLICATIONS
- Scientific domains benefit from models trained on sequence data to discover hidden patterns
- Business applications can understand customer behavior without expensive manual profiling
- Foundation models create reusable intelligence that applies across multiple use cases
- The approach works because it captures the fundamental relational structure underlying different domains
I have curated Top 20 AI Books: cooldeepai.gumroad.com/l/aib…
Sir, do tools like NotebookLM or other LLMs that summarize video content cause people to miss the story or thought process behind a product that sparks curiosity? I understand concepts better when instructors explain the why and how, which LLMs might overlook in summaries. They save time, but are people missing something more?
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