AIAP: An Overview of Technical AI Alignment in 2018 and 2019 with Buck Shlegeris and Rohin Shah

Just a year ago we released a two part episode titled An Overview of Technical AI Alignment with Rohin Shah. That conversation provided details on the views of central AI alignment research organizations and many of the ongoing research efforts for designing safe and aligned systems. Much has happened in the past twelve months, so we've invited Rohin — along with fellow researcher Buck Shlegeris — back for a follow-up conversation. Today's episode focuses especially on the state of current research efforts for beneficial AI, as well as Buck's and Rohin's thoughts about the varying approaches and the difficulties we still face. This podcast thus serves as a non-exhaustive overview of how the field of AI alignment has updated and how thinking is progressing.

 Topics discussed in this episode include:

-Rohin's and Buck's optimism and pessimism about different approaches to aligned AI

-Traditional arguments for AI as an x-risk

-Modeling agents as expected utility maximizers

-Ambitious value learning and specification learning/narrow value learning

-Agency and optimization

-Robustness

-Scaling to superhuman abilities

-Universality

-Impact regularization

-Causal models, oracles, and decision theory

-Discontinuous and continuous takeoff scenarios

-Probability of AI-induced existential risk

-Timelines for AGI

-Information hazards

You can find the page for this podcast here: https://futureoflife.org/2020/04/15/an-overview-of-technical-ai-alignment-in-2018-and-2019-with-buck-shlegeris-and-rohin-shah/

Timestamps: 

0:00 Intro

3:48 Traditional arguments for AI as an existential risk

5:40 What is AI alignment?

7:30 Back to a basic analysis of AI as an existential risk

18:25 Can we model agents in ways other than as expected utility maximizers?

19:34 Is it skillful to try and model human preferences as a utility function?

27:09 Suggestions for alternatives to modeling humans with utility functions

40:30 Agency and optimization

45:55 Embedded decision theory

48:30 More on value learning

49:58 What is robustness and why does it matter?

01:13:00 Scaling to superhuman abilities

01:26:13 Universality

01:33:40 Impact regularization

01:40:34 Causal models, oracles, and decision theory

01:43:05 Forecasting as well as discontinuous and continuous takeoff scenarios

01:53:18 What is the probability of AI-induced existential risk?

02:00:53 Likelihood of continuous and discontinuous take off scenarios

02:08:08 What would you both do if you had more power and resources?

02:12:38 AI timelines

02:14:00 Information hazards

02:19:19 Where to follow Buck and Rohin and learn more

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