Keynote: AI in the Real World: Today and Tomorrow
summary
Argues that practical AI is about production patterns not sci fi, showing concrete wins and failures: Google Maps traffic as a boring, deployed ML system; a persistent named entity disambiguation bug that keeps misattributing photos; prototype natural language generation that writes real estate ads and lets a bank auto‑write ~80% of compliance reports; deep learning successes and odd misclassifications (crabs vs burgers); workflow: validate, optimize algorithms, then operationalize, with interpretability and ethics tools integrated.
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chapters (4)
- 0:00Start
- 8:00A personal example of being an edge case.
- 14:43Validate against robust criteria
- 16:37Progressively explore risky capabilities.
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