AI Safety Fundamentals: Alignment

BlueDot Impact
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AI Safety Fundamentals: Alignment

Listen to resources from the AI Safety Fundamentals: Alignment course!https://aisafetyfundamentals.com/alignment

  1. JUL 19

    Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

    This paper explains Anthropic’s constitutional AI approach, which is largely an extension on RLHF but with AIs replacing human demonstrators and human evaluators.Everything in this paper is relevant to this week's learning objectives, and we recommend you read it in its entirety. It summarises limitations with conventional RLHF, explains the constitutional AI approach, shows how it performs, and where future research might be directed.If you are in a rush, focus on sections 1.2, 3.1, 3.4, 4.1...

    32 min

  2. JUL 19

    Constitutional AI Harmlessness from AI Feedback

    This paper explains Anthropic’s constitutional AI approach, which is largely an extension on RLHF but with AIs replacing human demonstrators and human evaluators.Everything in this paper is relevant to this week's learning objectives, and we recommend you read it in its entirety. It summarises limitations with conventional RLHF, explains the constitutional AI approach, shows how it performs, and where future research might be directed.If you are in a rush, focus on sections 1.2, 3.1, 3.4, 4.1...

    1h 2m

  3. JUL 19

    Illustrating Reinforcement Learning from Human Feedback (RLHF)

    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.

    23 min

  4. JUN 17

    Chinchilla’s Wild Implications

    This post is about language model scaling laws, specifically the laws derived in the DeepMind paper that introduced Chinchilla. The paper came out a few months ago, and has been discussed a lot, but some of its implications deserve more explicit notice in my opinion. In particular: Data, not size, is the currently active constraint on language modeling performance. Current returns to additional data are immense, and current returns to additional model size are miniscule; indeed, most recent l...

    25 min

  5. JUN 17

    Deep Double Descent

    We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don’t yet fully understand why it happens, and view further study of this phenomenon as an important research direction.Source:https://openai.com/research/deep-double-de...

    8 min

  6. JUN 17

    Eliciting Latent Knowledge

    In this post, we’ll present ARC’s approach to an open problem we think is central to aligning powerful machine learning (ML) systems: Suppose we train a model to predict what the future will look like according to cameras and other sensors. We then use planning algorithms to find a sequence of actions that lead to predicted futures that look good to us.But some action sequences could tamper with the cameras so they show happy humans regardless of what’s really happening. More generally, ...

    1 hr

  7. JUN 17

    Intro to Brain-Like-AGI Safety

    (Sections 3.1-3.4, 6.1-6.2, and 7.1-7.5)Suppose we someday build an Artificial General Intelligence algorithm using similar principles of learning and cognition as the human brain. How would we use such an algorithm safely?I will argue that this is an open technical problem, and my goal in this post series is to bring readers with no prior knowledge all the way up to the front-line of unsolved problems as I see them.If this whole thing seems weird or stupid, you should start right in on Post ...

    1h 2m

  8. JUN 17

    ABS: Scanning Neural Networks for Back-Doors by Artificial Brain Stimulation

    This paper presents a technique to scan neural network based AI models to determine if they are trojaned. Pre-trained AI models may contain back-doors that are injected through training or by transforming inner neuron weights. These trojaned models operate normally when regular inputs are provided, and mis-classify to a specific output label when the input is stamped with some special pattern called trojan trigger. We develop a novel technique that analyzes inner neuron behaviors by determini...

    16 min
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Listen to resources from the AI Safety Fundamentals: Alignment course!https://aisafetyfundamentals.com/alignment

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