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cover of episode Ep 54: Princeton Researcher Arvind Narayanan on the Limitations of Agent Evals, AI’s Societal Impact & Important Lessons from History

Ep 54: Princeton Researcher Arvind Narayanan on the Limitations of Agent Evals, AI’s Societal Impact & Important Lessons from History

2025/1/30
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Unsupervised Learning

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Arvind Narayanan: 我认为当前AI模型在数学、编程等领域表现出色,但其泛化能力还有待观察。未来发展可能停留在这些狭窄领域,也可能扩展到更广泛的领域。评估AI模型不应仅依赖基准测试结果,更要关注其在实际应用中的表现和对人类生产力的提升。AI模型在标准化考试中的出色表现并不意味着其能够胜任律师或医生等实际工作。当验证器不完善时,推理扩展方法的有效性会受到限制,无法实现大幅提升。

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Arvind Narayanan is one of the leading voices in AI when it comes to cutting through the hype. As a Princeton professor and co-author of AI Snake Oil, he’s one of the most thoughtful voices cautioning against both unfounded fears and overblown promises in AI. In this episode, Arvind dissects the future of AI in education, its parallels to past tech revolutions, and how our jobs are already shifting toward managing these powerful tools. Some of our favorite take-aways:

 

[0:00] Intro[0:46] Reasoning Models and Their Uneven Progress[2:46] Challenges in AI Benchmarks and Real-World Applications[5:03] Inference Scaling and Verifier Imperfections[7:33] Agentic AI: Tools vs. Autonomous Actions[12:07] Future of AI in Everyday Life[15:34] Evaluating AI Agents and Collaboration[24:49] Regulatory and Policy Implications of AI[27:49] Analyzing Generative AI Adoption Rates[29:17] Educational Policies and Generative AI[30:09] Flaws in Predictive AI Models[31:31] Regulation and Safety in AI[33:47] Academia's Role in AI Development[36:13] AI in Scientific Research[38:22] AI and Human Minds[46:04] Economic Impacts of AI[49:42] Quickfire

 

With your co-hosts: 

@jacobeffron 

  • Partner at Redpoint, Former PM Flatiron Health

 

@patrickachase 

  • Partner at Redpoint, Former ML Engineer LinkedIn

 

@ericabrescia 

  • Former COO Github, Founder Bitnami (acq’d by VMWare)

 

@jordan_segall 

  • Partner at Redpoint