AI
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- Differentiable Programming - A Simple Introduction
- Techniques to improve reliability
Applying this simple trick to the MultiArith math dataset, the authors found
Let's think step by stepquadrupled the accuracy, from 18% to 79%! - AI Canon
- Anti-hype LLM reading list #bookshelf
- Dive into Deep Learning #bookshelf
- Understanding Deep Learning #bookshelf
Why it's essential to excel at the things AI excels at
- Learning areas that AI excels in, like coding, English, design and data, will provide more opportunities as these are fields where AI is commonly utilized.
- Relying entirely on external support and outsourcing prevents one from developing self-awareness, confidence and resilience to overcome challenges.
- Gaining expertise in an AI-strong domain allows one to effectively lead AI-assisted innovations and make the most of AI capabilities.
- Information alone is insufficient - one must make connections between concepts internally through understanding.
- Insights emerge from linking disparate ideas, which only occurs through internal reflection not external instruction.
- Wisdom develops from accumulating insights over time through dedicated study.
- Specializing in an AI field builds an expertise level others rely on, while AI supports less skilled roles.
- Psychologically, self-learning builds identity, willpower and problem-solving skills.
- Opportunities arise from innovating where AI supports human strengths, not replacing them.
- Mastery requires climbing from foundations, so comprehensively studying AI domains leads to leadership.
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