Navigating Generative AI and Its Risks
summary
Generative AI uses GANs and autoregressive models trained on massive unlabeled public data to create images, audio and protein/drug candidates; synthetic medical images increase labeled training volume for radiology models. Deepfakes exploit realism to enable blackmail, fraud and misinformation; detection systems target lighting, blinking and temporal inconsistencies with detection via multimodal inconsistency analysis and large synthetic/real training sets. Proposed mitigations include blockchain-based provenance and real-time verification (content hashes, provenance chains, financial incentives), and industry commitments (Microsoft, Meta signed election-related accords). Continuous R&D and cross‑sector standards remain necessary.
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chapters (10)
- 0:00intro
- 0:26preamble
- 1:28understanding generative vs. discriminative ai
- 2:56applications of generative ai
- 4:40the threat of deepfakes
- 7:05combating deepfakes: technological approaches
- 7:58combating deepfakes: regulatory and ethical measures
- 9:26blockchain solutions for deepfake verification
- 11:02collaborative efforts to combat deepfakes
- 12:39conclusion: balancing innovation and ethics
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