Decoding AI: A Go Programmer's Perspective
summary
A Go-centric, code-first view of AI that names what actually works: convolutional neural networks applied to spectrograms for audio classification, production recommendation engines and deepfake detection running at the BBC, Transformer LLMs like ChatGPT and MidJourney, and concrete scale metrics such as 175 billion parameters (GPT-3) stored in ~350 GB. Examples include AlphaZero learning chess by self-play in hours and AlphaFold predicting protein structure; generative models help scaffold routine code while full applications still demand human-driven design.
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chapters (11)
- 0:00Introduction
- 1:03Why AI for Go programmers
- 4:36Personal AI history
- 9:10AI timeline overview
- 14:07Neural networks basics
- 18:23Large language models and Transformer architecture
- 22:18AI in code generation
- 25:46AI creative limits
- 28:17AI energy costs
- 29:20Debunking AI hype
- 30:50Q&A
