Can We Scale Human Feedback for Complex AI Tasks?

AI Safety Fundamentals: Alignment

#Tech #Society #Philosophy #Blue Dot Impact

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Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique for steering large language models (LLMs) toward desired behaviours. However, relying on simple human feedback doesn’t work for tasks that are too complex for humans to accurately judge at the scale needed to train AI models. Scalable oversight techniques attempt to address this by increasing the abilities of humans to give feedback on complex tasks.

This article briefly recaps some of the challenges faced with human feedback, and introduces the approaches to scalable oversight covered in session 4 of our AI Alignment course.
Source:
https://aisafetyfundamentals.com/blog/scalable-oversight-intro/
Narrated for AI Safety Fundamentals by Perrin Walker

A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.

Разделы

1. Can We Scale Human Feedback for Complex AI Tasks? (00:00:00)

2. Why do we need better human feedback? (00:00:51)

3. What is scalable oversight? (00:02:44)

4. Why might scalable oversight not work? (00:18:24)

85 эпизодов

Can We Scale Human Feedback for Complex AI Tasks?

AI Safety Fundamentals: Alignment

published год назад

MP3•Главная эпизода

Архивные серии ("Канал не активен" status)

When? This feed was archived on February 21, 2025 21:08 (17d ago). Last successful fetch was on January 02, 2025 12:05 (2M ago)

A podcast by BlueDot Impact.
Learn more on the AI Safety Fundamentals website.

Разделы

1. Can We Scale Human Feedback for Complex AI Tasks? (00:00:00)

2. Why do we need better human feedback? (00:00:51)

3. What is scalable oversight? (00:02:44)

4. Why might scalable oversight not work? (00:18:24)

85 эпизодов

#Tech #Society #Philosophy #Blue Dot Impact

Все серии

1
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Our introduction introduces common mech interp concepts, to prepare you for the rest of this session's resources. Original text: https://aisafetyfundamentals.com/blog/introduction-to-mechanistic-interpretability/ Author(s): Sarah Hastings-Woodhouse A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…

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This more technical article explains the motivations for a system like RLHF, and adds additional concrete details as to how the RLHF approach is applied to neural networks. While reading, consider which parts of the technical implementation correspond to the 'values coach' and 'coherence coach' from the previous video. A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.…

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Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback 32:19

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1
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38 weeks назад11:32

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