Shahar Avin–Intelligence Rising, AI Governance
The Inside View
The Future of Humanity
In his past life, Demis Asabis was a Google engineer. Now he spends most of his time thinking about how to prevent the risk if companies like Google deploy powerful AI systems. We will explore in depth what we mean by powerful AI systems in this episode.
Shahar is a senior researcher at the Center for the Study of Existential Risk in Cambridge. In his past life, he was a Google Engineer, though right now he spends most of your time thinking about how to prevent the risks that occur if companies like Google end up deploying powerful AI systems, by organizing AI Governance role-playing workshops.
In this episode, we talk about a broad variety of topics, including how we could apply the lessons from running AI Governance workshops to governing transformative AI, AI Strategy, AI Governance, Trustworthy AI Development and end up answering some Twitter questions.
Youtube: https://youtu.be/3T7Gpwhtc6Q
Transcript: https://theinsideview.ai/shahar
Host: https://twitter.com/MichaelTrazzi
Shahar: https://www.shaharavin.com
Outline
(00:00) Highlights
(01:20) Intelligence Rising
(06:07) Measuring Transformative AI By The Scale Of Its Impact
(08:09) Comprehensive AI Services
(11:38) Automating CEOs Through AI Services
(14:21) Towards A "Tech Company Singularity"
(15:58) Predicting AI Is Like Predicting The Industrial Revolution
(19:57) 50% Chance Of Human-brain Performance By 2038
(22:25) AI Alignment Is About Steering Powerful Systems Towards Valuable Worlds
(23:51) You Should Still Worry About Less Agential Systems
(28:07) AI Strategy Needs To Be Tested In The Real World To Not Become Theoretical Physics
(31:37) Playing War Games For Real-time Partial-information Advesarial Thinking
(34:50) Towards World Leaders Playing The Game Because It’s Useful
(39:31) Open Game, Cybersecurity, Government Spending, Hard And Soft Power
(45:21) How Cybersecurity, Hard-power Or Soft-power Could Lead To A Strategic Advantage
(48:58) Cybersecurity In A World Of Advanced AI Systems
(52:50) Allocating AI Talent For Positive R&D ROI
(57:25) Players Learn To Cooperate And Defect
(01:00:10) Can You Actually Tax Tech Companies?
(01:02:10) The Emergence Of Bilateral Agreements And Technology Bans
(01:03:22) AI Labs Might Not Be Showing All Of Their Cards
(01:06:34) Why Publish AI Research
(01:09:21) Should You Expect Actors To Build Safety Features Before Crunch Time
(01:12:39) Why Tech Companies And Governments Will Be The Decisive Players
(01:14:29) Regulations Need To Happen Before The Explosion, Not After
(01:16:55) Early Regulation Could Become Locked In
(01:20:00) What Incentives Do Companies Have To Regulate?
(01:23:06) Why Shahar Is Terrified Of AI DAOs
(01:27:33) Concrete Mechanisms To Tell Apart Who We Should Trust With Building Advanced AI Systems
(01:31:19) Increasing Privacy To Build Trust
(01:33:37) Sensibilizing To Privacy Through Federated Learning
(01:35:23) How To Motivate AI Regulations
(01:37:44) How Governments Could Start Caring About AI risk
(01:39:12) Attempts To Regulate Autonomous Weapons Have Not Resulted In A Ban
(01:40:58) We Should Start By Convincing The Department Of Defense
(01:42:08) Medical Device Regulations Might Be A Good Model Audits
(01:46:09) Alignment Red Tape And Misalignment Fines
(01:46:53) Red Teaming AI systems
(01:49:12) Red Teaming May Not Extend To Advanced AI Systems
(01:51:26) What Climate change Teaches Us About AI Strategy
(01:55:16) Can We Actually Regulate Compute
(01:57:01) How Feasible Are Shutdown Swi
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