![[Week 3] "The alignment problem from a deep learning perspective" (Sections 2, 3 and 4) by Richard Ngo, Lawrence Chan & Sören Mindermann](https://storage.buzzsprout.com/b7xzejx9cm8dkgtgmzsz6gx8fu4f?.jpg)
[Week 3] "The alignment problem from a deep learning perspective" (Sections 2, 3 and 4) by Richard Ngo, Lawrence Chan & Sören Mindermann
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Show Notes
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client: agi_sf
project_id: core_readings
feed_id: agi_sf__alignment
narrator: pw
qa: mds
qa_time: 1h00m
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Within the coming decades, artificial general intelligence (AGI) may surpass human capabilities at a wide range of important tasks. We outline a case for expecting that, without substantial effort to prevent it, AGIs could learn to pursue goals which are undesirable (i.e. misaligned) from a human perspective. We argue that if AGIs are trained in ways similar to today's most capable models, they could learn to act deceptively to receive higher reward, learn internally-represented goals which generalize beyond their training distributions, and pursue those goals using power-seeking strategies. We outline how the deployment of misaligned AGIs might irreversibly undermine human control over the world, and briefly review research directions aimed at preventing this outcome.
Original article:
https://arxiv.org/abs/2209.00626
Authors:
Richard Ngo, Lawrence Chan, Sören Mindermann
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This article is featured on the AGI Safety Fundamentals: Alignment course curriculum.
Narrated by TYPE III AUDIO on behalf of BlueDot Impact.