Interconnects Interviewing Arvind Narayanan on making sense of AI hype
9 snips
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.
AI Snips
Chapters
Transcript
Episode notes
Balancing Criticism and Optimism
- Arvind Narayanan balances AI criticism with optimism about its potential.
- He believes addressing potential harms is crucial for realizing AI's positive impacts.
Challenges in AI Policy
- Policy discussions about AI can be frustrating due to the challenge of disentangling different types of AI and their harms.
- Existing harms from predictive AI complicate discussions about foundation models.
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.