Beyond the AI Hype: What's Real, What's Next
summary
Traces AI from neural nets to large language models, citing ImageNet 84%→100% as the turning point, GPU-driven Azure scale, and GPT3 375 billion parameters leading to GitHub Copilot. Notes Microsoft’s ~$13B OpenAI spend and 100 million users in two months for ChatGPT as the market shock, observes radiology demand rising rather than shrinking, warns of deepfakes and mental‑health harms, and highlights a concrete win: AlphaFold 200M proteins 90% accuracy and public release driving real vaccines and drug research.
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chapters (13)
- 0:00🚀 Build Stuff Conference – Get Your Tickets!
- 0:28The Origins of Artificial Intelligence and Why the Name Matters
- 3:10From Sci-Fi to Software: HAL, Terminator, and AI in Culture
- 5:20Geoffrey Hinton, Neural Nets, and the Road to Modern AI
- 8:10ImageNet and the Breakthrough in Image Recognition
- 11:00Google Brain, OpenAI, and the Push to Democratize AI
- 14:10Why AI Didn’t Replace Radiologists—and What That Teaches Us
- 20:10GPT-2, GPT-3, and Microsoft’s Investment in OpenAI
- 24:15Scaling Laws, Azure Infrastructure, and the Birth of the AI Boom
- 31:10Copilots Everywhere: Microsoft’s AI Strategy in Practice
- 34:00DeepSeek, Coding Tools, and the Future of Software Development
- 36:40The Limits of Bigger Models and the Cracks in the Hype Cycle
- 39:10Final Reflection: The Future of AI Depends on What We Choose to Build
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