~/talk

Challenges of Generative AI

on YouTube 27m
Talkat DLD Munich 23 · 2023-01-12..01-14· [large-language-models, ai-assisted-development]

speakers

name:J. Clippinger
links:none on file
name:JP Rangaswami
links:none on file
name:M. Ruiz
links:none on file
name:R. Socher
links:none on file
name:J. Thornhill
links:none on file

resources

video:https://youtu.be/3xJZs39n4Sc
slides:none on file

summary

ChatGPT hits 1 million users in a week and investors predict trillions in value, yet models routinely hallucinate facts and admit biased training data; OpenAI flags gender and race bias as concrete risks. Stable Diffusion proves image generation can be democratized but raises job and copyright conflicts. Workarounds discussed include retrieval-augmented models to keep smaller on-device systems, decentralised identity to let people control their data, and adding citations so models refuse to answer when uncertain.

AI-written summary · about

chapters (7)

  1. 0:00Generative AI challenges
  2. 3:00AI model accessibility
  3. 7:14Understanding the limits of large language models
  4. 13:45AI dependency risks
  5. 17:15AI bias and impact
  6. 20:04Ethical and technical challenges
  7. 24:18Environmental and regulatory concerns

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