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.
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Links:
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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
