Now in AI - How We Got Here
summary
Claims that AI speeds many tasks get measured: code generation can produce a working Python script writing to Aerospike and author reports 20% faster software development using copilot, thanks to transformers, large deep nets, big data and RL fine-tuning (ChatGPT + RLHF). Practical builds include generated Excel financial-model steps and code snippets; failures include ensemble limits — can't predict stock market, personal-health or autonomous parenting and grocery automation. Progress relies on simulated environments, reinforcement learning and realtime data stacks.
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chapters (15)
- 0:00AI's current landscape
- 1:24Faster AI-assisted tasks
- 2:27AI quality and human collaboration
- 3:49AI limitations in complex domains
- 5:16AI's historical evolution
- 6:42Big data and deep learning
- 7:47Narrow vs general AI
- 9:14Generative vs discriminative AI
- 9:56AI: assistive, augmented, autonomous
- 11:02Machine learning methods
- 13:31Reinforcement learning basics
- 15:35Simulated AI training
- 16:16Modern AI game changers
- 16:59When to use AI
- 17:21AI's future predictions
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