This is the second of a two-post series on foom (previous post) and doom (this post).

The last post talked about how I expect future AI to be different from present AI. This post will argue that this future AI will be of a type that will be egregiously misaligned and scheming, not even ‘slightly nice’, absent some future conceptual breakthrough.

I will particularly focus on exactly how and why I differ from the LLM-focused researchers who wind up with (from my perspective) bizarrely over-optimistic beliefs like “P(doom) ≲ 50%”.[1]

In particular, I will argue that these “optimists” are right that “Claude seems basically nice, by and large” is nonzero evidence for feeling good about current LLMs (with various caveats). But I think that future AIs will be disanalogous to current LLMs, and I will dive into exactly how and why, with a [...]

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Outline:

(00:12) 2.1 Summary & Table of contents

(04:42) 2.2 Background: my expected future AI paradigm shift

(06:18) 2.3 On the origins of egregious scheming

(07:03) 2.3.1 Where do you get your capabilities from?

(08:07) 2.3.2 LLM pretraining magically transmutes observations into behavior, in a way that is profoundly disanalogous to how brains work

(10:50) 2.3.3 To what extent should we think of LLMs as imitating?

(14:26) 2.3.4 The naturalness of egregious scheming: some intuitions

(19:23) 2.3.5 Putting everything together: LLMs are generally not scheming right now, but I expect future AI to be disanalogous

(23:41) 2.4 I'm still worried about the 'literal genie' / 'monkey's paw' thing

(26:58) 2.4.1 Sidetrack on disanalogies between the RLHF reward function and the brain-like AGI reward function

(32:01) 2.4.2 Inner and outer misalignment

(34:54) 2.5 Open-ended autonomous learning, distribution shifts, and the 'sharp left turn'

(38:14) 2.6 Problems with amplified oversight

(41:24) 2.7 Downstream impacts of Technical alignment is hard

(43:37) 2.8 Bonus: Technical alignment is not THAT hard

(44:04) 2.8.1 I think we'll get to pick the innate drives (as opposed to the evolution analogy)

(45:44) 2.8.2 I'm more bullish on impure consequentialism

(50:44) 2.8.3 On the narrowness of the target

(52:18) 2.9 Conclusion and takeaways

(52:23) 2.9.1 If brain-like AGI is so dangerous, shouldn't we just try to make AGIs via LLMs?

(54:34) 2.9.2 What's to be done?

The original text contained 20 footnotes which were omitted from this narration.

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First published:

Source:

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Narrated by TYPE III AUDIO.

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