Posing for posterity at the #AISafetySummit but above all serving humanity and pledging alliance to those who work for the safety of AI to be guaranteed for the future
→ View original post on X — @inma_martinez, 2023-11-01 21:14 UTC
ETHICS
-
AI Research Regulation Versus Product Deployment Control
By
–
Regulating product deployment is fine.
In the case of life-critical applications like driving assistance, it's necessary. What we are opposed to is regulating AI research and development.
Particularly with arbitrary limits on computation levels. -
Regulating AI R&D Versus Products: Freedom to Compute Debate
By
–
An excellent piece by my dear NYU colleague @togelius pointing out that regulating AI R&D (as opposed to products) would lead to unacceptable levels of policing and restrictions on the "freedom to compute"
-
Nuclear Experts and AI Risk Assessment Expertise
By
–
Nonetheless, even under your proposal, the best person to understand analogies to nuclear risk are nuclear experts. And that is who I quoted. You're a marvelous DL researcher and I've learned so much from you — but perhaps there are also people you can learn from?
-
Academic Research on AI Risk Management and Safety
By
–
Actually many academics have spent their lives dedicated to exactly this question. Whether or not it's "normal risk management" it's nonetheless a well trodden path. You are ignoring years of research and study.
-
Open Source AI Platforms Against Catastrophe Risk Claims
By
–
An article about my vociferous support of open source AI platforms.
Demis Hassabis, Dario Amodei, and Sam Altman (among others) have scared governments about what they claim are risks of AI-fueled catastrophes. I know that Demis, at least, is sincere in his claims, but I think -
Controlling More Intelligent Agents: Literature and Challenges
By
–
As Raza Habib said, there is plenty of literature on this. My main reason is simply that I know of no cases where a much more intelligent agent is controlled by a much less intelligent one.
-
VIS4ML Tools: Analysis of 52 Papers on Limitations
By
–
Novel #VIS4ML tools aid human-in-the-loop ML work, but fall short of bold claims. An analysis of 52 papers by @HariSubramonyam and @JessicaHullman shows limitations and risks.