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The 80,000 Hours Podcast on Artificial Intelligence (September 2023)

En podcast av: 80,000 Hours

A compilation of ten key episodes on artificial intelligence and related topics from 80,000 Hours. Together they'll help you learn about how AI looks from a broadly longtermist, existential risk, or effective altruism flavoured point of view

14 Avsnitt

Zero: What to expect in this series
Publicerades 2023-09-02

One: Brian Christian on the alignment problem
Publicerades 2023-09-02
Two: Ajeya Cotra on accidentally teaching AI models to deceive us
Publicerades 2023-09-02
Three: Paul Christiano on finding real solutions to the AI alignment problem
Publicerades 2023-09-02
Four: Rohin Shah on DeepMind and trying to fairly hear out both AI doomers and doubters
Publicerades 2023-09-02
Five: Chris Olah on what the hell is going on inside neural networks
Publicerades 2023-09-02
Six: Richard Ngo on large language models, OpenAI, and striving to make the future go well
Publicerades 2023-09-02
Seven: Ben Garfinkel on scrutinising classic AI risk arguments
Publicerades 2023-09-01
Eight: Tom Davidson on how quickly AI could transform the world
Publicerades 2023-09-01
Nine: Helen Toner on emerging technology, national security, and China
Publicerades 2023-09-01
Ten: Nova DasSarma on why information security may be critical to the safe development of AI systems
Publicerades 2023-09-01
Eleven: Catherine Olsson & Daniel Ziegler on the fast path into high-impact ML engineering roles
Publicerades 2023-09-01
Bonus: Preventing an AI-related catastrophe (Article)
Publicerades 2023-09-01
Bonus: China-related AI safety and governance paths (Article)
Publicerades 2023-09-01

14 Avsnitt

Zero: What to expect in this series

One: Brian Christian on the alignment problem

Two: Ajeya Cotra on accidentally teaching AI models to deceive us

Three: Paul Christiano on finding real solutions to the AI alignment problem

Four: Rohin Shah on DeepMind and trying to fairly hear out both AI doomers and doubters

Five: Chris Olah on what the hell is going on inside neural networks

Six: Richard Ngo on large language models, OpenAI, and striving to make the future go well

Seven: Ben Garfinkel on scrutinising classic AI risk arguments

Eight: Tom Davidson on how quickly AI could transform the world

Nine: Helen Toner on emerging technology, national security, and China

Ten: Nova DasSarma on why information security may be critical to the safe development of AI systems

Eleven: Catherine Olsson & Daniel Ziegler on the fast path into high-impact ML engineering roles

Bonus: Preventing an AI-related catastrophe (Article)

Bonus: China-related AI safety and governance paths (Article)