Interconnects Interviewing Arvind Narayanan on making sense of AI hype
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Oct 17, 2024 
Arvind Narayanan, a computer science professor at Princeton and director of the Center for Information Technology Policy, delves into the realities of AI amidst the hype. He discusses the pitfalls of AI policy, emphasizing the need for harm-focused research. The conversation covers the risks of open-source foundation models, critiques of traditional AI in risk prediction, and the implications of scaling laws. Narayanan also sheds light on the balance between innovation and societal impact, highlighting the necessary collaboration between researchers and policymakers.
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Risks of Open-Source Models
- Open-source foundation models pose risks, particularly regarding non-consensual deepfakes.
- Researchers need to analyze existing defenses against AI-generated threats.
Generative AI Use Cases
- Arvind Narayanan uses generative AI for coding, data analysis, and building single-use apps.
- He created a random clock face generator to teach his daughter how to tell time.
CoreBench for AI Scientists
- CoreBench aims to evaluate automatable tasks in research to save researchers time.
- It focuses on computational reproducibility rather than automating the entire research process.