Artificial vs Natural Intelligence in Annotation Tasks
summary
Compares manual annotation workflows with modern AI approaches across vision, language and audio: covers SAM for detection/segmentation, vision-language models for image/video captioning, and SOTA NLP methods for description, summarization and rewriting. Discusses audio tasks (transcription, single- and multi‑speaker synthesis), cross‑modal video/audio labeling, the impact of LLMs to speed annotation and human–AI collaboration, specialized annotation needs, use of synthetic data and data-cleaning strategies.
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chapters (9)
- 0:00Introduction
- 2:14Pre-foundation models era
- 10:49Crowdsourcing role now
- 16:12Models assisting annotators
- 20:45Expert annotation and domain knowledge
- 29:29Synthetic data and future
- 42:05Will AI replace annotators
- 46:16Annotation quality validation
- 50:17Public datasets and community
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