AI Safety Fundamentals

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AI Safety Fundamentals

Listen to resources from the AI Safety Fundamentals courses!https://aisafetyfundamentals.com/

  1. JAN 4

    A Short Introduction to Machine Learning

    Despite the current popularity of machine learning, I haven’t found any short introductions to it which quite match the way I prefer to introduce people to the field. So here’s my own. Compared with other introductions, I’ve focused less on explaining each concept in detail, and more on explaining how they relate to other important concepts in AI, especially in diagram form. If you're new to machine learning, you shouldn't expect to fully understand most of the concepts explained here just af...

    18 min

  2. JAN 4

    AGI Ruin: A List of Lethalities

    I have several times failed to write up a well-organized list of reasons why AGI will kill you. People come in with different ideas about why AGI would be survivable, and want to hear different obviously key points addressed first. Some fraction of those people are loudly upset with me if the obviously most important points aren't addressed immediately, and I address different points first instead.Having failed to solve this problem in any good way, I now give up and solve it poorly with a po...

    1h 2m

  3. JAN 4

    AGI Safety From First Principles

    This report explores the core case for why the development of artificial general intelligence (AGI) might pose an existential threat to humanity. It stems from my dissatisfaction with existing arguments on this topic: early work is less relevant in the context of modern machine learning, while more recent work is scattered and brief. This report aims to fill that gap by providing a detailed investigation into the potential risk from AGI misbehaviour, grounded by our current knowledge of machi...

    13 min

  4. JAN 4

    AI Safety via Debate

    Abstract:To make AI systems broadly useful for challenging real-world tasks, we need them to learn complex human goals and preferences. One approach to specifying complex goals asks humans to judge during training which agent behaviors are safe and useful, but this approach can fail if the task is too complicated for a human to directly judge. To help address this concern, we propose training agents via self play on a zero sum debate game. Given a question or proposed action, two agents take ...

    40 min

  5. JAN 4

    AI Safety via Red Teaming Language Models With Language Models

    Abstract: Language Models (LMs) often cannot be deployed because of their potential to harm users in ways that are hard to predict in advance. Prior work identifies harmful behaviors before deployment by using human annotators to hand-write test cases. However, human annotation is expensive, limiting the number and diversity of test cases. In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases (“red teaming”) using another LM. We ev...

    7 min

  6. JAN 4

    AI Watermarking Won’t Curb Disinformation

    Generative AI allows people to produce piles upon piles of images and words very quickly. It would be nice if there were some way to reliably distinguish AI-generated content from human-generated content. It would help people avoid endlessly arguing with bots online, or believing what a fake image purports to show. One common proposal is that big companies should incorporate watermarks into the outputs of their AIs. For instance, this could involve taking an image and subtly changing many pix...

    8 min

  7. JAN 4

    Acquisition of Chess Knowledge in Alphazero

    Abstract:What is learned by sophisticated neural network agents such as AlphaZero? This question is of both scientific and practical interest. If the representations of strong neural networks bear no resemblance to human concepts, our ability to understand faithful explanations of their decisions will be restricted, ultimately limiting what we can achieve with neural network interpretability. In this work we provide evidence that human knowledge is acquired by the AlphaZero neural network as ...

    22 min

  8. JAN 4

    Biological Anchors: A Trick That Might Or Might Not Work

    I've been trying to review and summarize Eliezer Yudkowksy's recent dialogues on AI safety. Previously in sequence: Yudkowsky Contra Ngo On Agents. Now we’re up to Yudkowsky contra Cotra on biological anchors, but before we get there we need to figure out what Cotra's talking about and what's going on.The Open Philanthropy Project ("Open Phil") is a big effective altruist foundation interested in funding AI safety. It's got $20 billion, probably the majority of money in the field, so its deci...

    1h 11m
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About

Listen to resources from the AI Safety Fundamentals courses!https://aisafetyfundamentals.com/

Information

  • Creator
    BlueDot Impact
  • Years Active
    2023 - 2025
  • Episodes
    147
  • Rating
    Clean
  • Copyright
    © 2025 AI Safety Fundamentals
  • Show Website
    AI Safety Fundamentals

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