The Revolutionary Work of AI & Machine Learning in Media
summary
KQED builds voice-first experiences by repackaging existing broadcasts into algorithmic playlists and building pipelines for rapid reformatting rather than inventing new shows. Examples include a California voter guide with structured Q&A and a geolocated podcast that stitches national and local segments for listeners. A small production team that includes newsroom-trained producers experiments with discovery, finds proper nouns break ML models, and treats multimodal displays as the easier path to correct results.
AI-written summary · about
chapters (10)
- 0:00Introduction and role overview
- 1:27Voice and AI at KQED
- 3:57Shift to digital audio access
- 6:22Content discovery challenges
- 10:15Opportunities in voice interactivity
- 11:44Building a voice team
- 14:10Future of localized content
- 16:40Multimodal media experiences
- 19:29Public understanding of AI
- 22:59Advice for new voice professionals
related talks
24mApplications of AI in news and media
29mAI in Media: Balancing Innovation, Quality, and Responsibility
24mMachine Intelligence gets real
29mEngineering Machine Learning Technologies
36mKeynote: AI, ML, Cloud for ML
22mKeynote: The Future of AI is Data...In More Ways than You Think
26mKeynote: AI in the Real World: Today and Tomorrow
22mThe media processing pipelines behind AI