LessWrong (Curated & Popular)

LessWrong (Curated & Popular)

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LessWrong (Curated & Popular) episodes

  • [HUMAN VOICE] "My Clients, The Liars" by ymeskhout

    Support ongoing human narrations of LessWrong's curated posts:
    www.patreon.com/LWCurated

    Source:
    https://www.lesswrong.com/posts/h99tRkpQGxwtb9Dpv/my-clients-the-liars

    Narrated for LessWrong by Perrin Walker.

    Share feedback on this narration.

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    125+ Karma Post] ✓

    14 min
  • [HUMAN VOICE] "CFAR Takeaways: Andrew Critch" by Raemon

    Support ongoing human narrations of LessWrong's curated posts:
    www.patreon.com/LWCurated

    Source:
    https://www.lesswrong.com/posts/Jash4Gbi2wpThzZ4k/cfar-takeaways-andrew-critch

    Narrated for LessWrong by Perrin Walker.

    Share feedback on this narration.

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    125+ Karma Post] ✓

    10 min
  • Many arguments for AI x-risk are wrong
    Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.The following is a lightly edited version of a memo I wrote for a retreat. It was inspired by a draft of Counting arguments provide no evidence for AI doom, although the earlier draft contained some additional content. I personally really like the earlier content, and think that my post covers important points not made by the published version of that post.

    Thankful for the dozens of interesting conversations and comments at the retreat.

    I think that the AI alignment field is partially founded on fundamentally confused ideas. I’m worried about this because, right now, a range of lobbyists and concerned activists and researchers are in Washington making policy asks. Some of these policy proposals seem to be based on erroneous or unsound arguments.[1]

    The most important takeaway from this essay is that [...]

    The original text contained 8 footnotes which were omitted from this narration.

    ---

    First published:
    March 5th, 2024

    Source:
    https://www.lesswrong.com/posts/yQSmcfN4kA7rATHGK/many-arguments-for-ai-x-risk-are-wrong

    ---

    Narrated by TYPE III AUDIO.

    21 min
  • Tips for Empirical Alignment Research
    Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.TLDR: I’ve collected some tips for research that I’ve given to other people and/or used myself, which have sped things up and helped put people in the right general mindset for empirical AI alignment research. Some of these are opinionated takes, also around what has helped me. Researchers can be successful in different ways, but I still stand by the tips here as a reasonable default.

    What success generally looks like

    Here, I’ve included specific criteria that strong collaborators of mine tend to meet, with rough weightings on the importance, as a rough north star for people who collaborate with me (especially if you’re new to research). These criteria are for the specific kind of research I do (highly experimental LLM alignment research, excluding interpretability); some examples of research areas where this applies are e.g. scalable oversight [...]

    ---

    First published:
    February 29th, 2024

    Source:
    https://www.lesswrong.com/posts/dZFpEdKyb9Bf4xYn7/tips-for-empirical-alignment-research

    ---

    Narrated by TYPE III AUDIO.

    40 min
  • Timaeus’s First Four Months
    Timaeus was announced in late October 2023, with the mission of making fundamental breakthroughs in technical AI alignment using deep ideas from mathematics and the sciences. This is our first progress update.

    In service of the mission, our first priority has been to support and contribute to ongoing work in Singular Learning Theory (SLT) and developmental interpretability, with the aim of laying theoretical and empirical foundations for a science of deep learning and neural network interpretability.

    Our main uncertainties in this research were:

    • Is SLT useful in deep learning? While SLT is mathematically established, it was not clear whether the central quantities of SLT could be estimated at sufficient scale, and whether SLT's predictions actually held for realistic models (esp. language models). 
    • Does structure in neural networks form in phase transitions? The idea of developmental interpretability was to view phase transitions as a core primitive in the [...]
    The original text contained 1 footnote which was omitted from this narration.

    ---

    First published:
    February 28th, 2024

    Source:
    https://www.lesswrong.com/posts/Quht2AY6A5KNeZFEA/timaeus-s-first-four-months

    ---

    Narrated by TYPE III AUDIO.

    12 min
  • Contra Ngo et al. “Every ‘Every Bay Area House Party’ Bay Area House Party”
    This is a linkpost for https://bayesshammai.substack.com/p/contra-ngo-et-al-every-every-bayWith thanks to Scott Alexander for the inspiration, Jeffrey Ladish, Philip Parker, Avital Morris, and Drake Thomas for masterful cohosting, and Richard Ngo for his investigative journalism.

    Last summer, I threw an Every Bay Area House Party themed party. I don’t live in the Bay, but I was there for a construction-work-slash-webforum-moderation-and-UI-design-slash-grantmaking gig, so I took the opportunity to impose myself on the ever generous Jeffrey Ladish and host a party in his home. Fortunately, the inside of his house is already optimized to look like a parody of a Bay Area house party house, so not much extra decorating was needed, but when has that ever stopped me?

    Attendees could look through the window for an outside viewRichard Ngo recently covered the event, with only very minor embellishments. I’ve heard rumors that some people are doubting whether the party described truly happened, so [...]

    ---

    First published:
    February 22nd, 2024

    Source:
    https://www.lesswrong.com/posts/mmYFF4dyi8Kg6pWGC/contra-ngo-et-al-every-every-bay-area-house-party-bay-area

    Linkpost URL:
    https://bayesshammai.substack.com/p/contra-ngo-et-al-every-every-bay

    ---

    Narrated by TYPE III AUDIO.

    8 min

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