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محتوای ارائه شده توسط LessWrong. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط LessWrong یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
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“Foom & Doom 2: Technical alignment is hard” by Steven Byrnes

56:38
 
اشتراک گذاری
 

Manage episode 491972312 series 3364758
محتوای ارائه شده توسط LessWrong. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط LessWrong یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

2.1 Summary & Table of contents

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 [...]
---
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.
---
First published:
June 23rd, 2025
Source:
https://www.lesswrong.com/posts/bnnKGSCHJghAvqPjS/foom-and-doom-2-technical-alignment-is-hard
---
Narrated by TYPE III AUDIO.
---
Images from the article:
The “Literal Genie” fiction trope. (Image modified from Skeleton Claw)
  continue reading

582 قسمت

Artwork
iconاشتراک گذاری
 
Manage episode 491972312 series 3364758
محتوای ارائه شده توسط LessWrong. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط LessWrong یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

2.1 Summary & Table of contents

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 [...]
---
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.
---
First published:
June 23rd, 2025
Source:
https://www.lesswrong.com/posts/bnnKGSCHJghAvqPjS/foom-and-doom-2-technical-alignment-is-hard
---
Narrated by TYPE III AUDIO.
---
Images from the article:
The “Literal Genie” fiction trope. (Image modified from Skeleton Claw)
  continue reading

582 قسمت

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