Search a title or topic

Over 20 million podcasts, powered by 

Player FM logo
Artwork

Content provided by Conviction. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Conviction or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

Will we have Superintelligence by 2028? With Anthropic’s Ben Mann

41:25
 
Share
 

Manage episode 488306266 series 3444082
Content provided by Conviction. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Conviction or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

What happens when you give AI researchers unlimited compute and tell them to compete for the highest usage rates? Ben Mann, Co-Founder, from Anthropic sits down with Sarah Guo and Elad Gil to explain how Claude 4 went from "reward hacking" to efficiently completing tasks and how they're racing to solve AI safety before deploying computer-controlling agents. Ben talks about economic Turing tests, the future of general versus specialized AI models, Reinforcement Learning From AI Feedback (RLAIF), and Anthropic’s Model Context Protocol (MCP). Plus, Ben shares his thoughts on if we will have Superintelligence by 2028.

Sign up for new podcasts every week. Email feedback to [email protected]

Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @8enmann

Links:

Chapters:

00:00 Ben Mann Introduction

00:33 Releasing Claude 4

02:05 Claude 4 Highlights and Improvements

03:42 Advanced Use Cases and Capabilities

06:42 Specialization and Future of AI Models

09:35 Anthropic's Approach to Model Development

18:08 Human Feedback and AI Self-Improvement

19:15 Principles and Correctness in Model Training

20:58 Challenges in Measuring Correctness

21:42 Human Feedback and Preference Models

23:38 Empiricism and Real-World Applications

27:02 AI Safety and Ethical Considerations

28:13 AI Alignment and High-Risk Research

30:01 Responsible Scaling and Safety Policies

35:08 Future of AI and Emerging Behaviors

38:35 Model Context Protocol (MCP) and Industry Standards

41:00 Conclusion

  continue reading

119 episodes

Artwork
iconShare
 
Manage episode 488306266 series 3444082
Content provided by Conviction. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Conviction or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

What happens when you give AI researchers unlimited compute and tell them to compete for the highest usage rates? Ben Mann, Co-Founder, from Anthropic sits down with Sarah Guo and Elad Gil to explain how Claude 4 went from "reward hacking" to efficiently completing tasks and how they're racing to solve AI safety before deploying computer-controlling agents. Ben talks about economic Turing tests, the future of general versus specialized AI models, Reinforcement Learning From AI Feedback (RLAIF), and Anthropic’s Model Context Protocol (MCP). Plus, Ben shares his thoughts on if we will have Superintelligence by 2028.

Sign up for new podcasts every week. Email feedback to [email protected]

Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @8enmann

Links:

Chapters:

00:00 Ben Mann Introduction

00:33 Releasing Claude 4

02:05 Claude 4 Highlights and Improvements

03:42 Advanced Use Cases and Capabilities

06:42 Specialization and Future of AI Models

09:35 Anthropic's Approach to Model Development

18:08 Human Feedback and AI Self-Improvement

19:15 Principles and Correctness in Model Training

20:58 Challenges in Measuring Correctness

21:42 Human Feedback and Preference Models

23:38 Empiricism and Real-World Applications

27:02 AI Safety and Ethical Considerations

28:13 AI Alignment and High-Risk Research

30:01 Responsible Scaling and Safety Policies

35:08 Future of AI and Emerging Behaviors

38:35 Model Context Protocol (MCP) and Industry Standards

41:00 Conclusion

  continue reading

119 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Copyright 2025 | Privacy Policy | Terms of Service | | Copyright
Listen to this show while you explore
Play