LessWrong (Curated & Popular)

Support ongoing human narrations of LessWrong's curated posts:www.patreon.com/LWCuratedSource:https://www.lesswrong.com/posts/sJPbmm8Gd34vGYrKd/deep-atheism-and-ai-riskNarrated for LessWrong by Perrin Walker.Share feedback on this narration.[Curated Post] ✓

A public defender discusses the challenges of representing clients who lie. Stories of clients Marcel and Kyle and navigating their deceit. Unraveling a client fixated on a dubious flyer and legal limitations. Ethics, investigations, and difficulties of dealing with deceptive clients.

Support ongoing human narrations of LessWrong's curated posts:www.patreon.com/LWCuratedSource:https://www.lesswrong.com/posts/2sLwt2cSAag74nsdN/speaking-to-congressional-staffers-about-ai-riskNarrated for LessWrong by Perrin Walker.Share feedback on this narration.[Curated Post] ✓[125+ Karma Post] ✓

The podcast delves into Andrew Krich's insights on challenges in numeracy, inner simulation, and human desires. It explores teaching strategies like visualizations and internal family systems, while emphasizing the importance of cognitive and emotional skills for high performance.

Exploring misconceptions in AI X-risk arguments, critiquing flawed claims, and advocating for evidence-based evaluations. Scrutinizing counting arguments and challenges in predicting AI behavior. Addressing errors in existential risk arguments and emphasizing the need for neutral terminology in discussions.

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 likeHere, 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.

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.

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.

Support ongoing human narrations of LessWrong's curated posts:www.patreon.com/LWCuratedSource:https://www.lesswrong.com/posts/8yCXeafJo67tYe5L4/and-all-the-shoggoths-merely-players Narrated for LessWrong by Perrin Walker.Share feedback on this narration.[Curated Post] ✓[125+ Karma Post] ✓

The podcast explores the complexities of updatelessness in decision-making, highlighting limitations and strategic implications. It delves into decision strategies in uncertain environments, cooperation with counterfactual selves, and the trade-offs in decision-making under uncertainty. The discussions cover scenarios like counterfactual mugging and the game of chicken, emphasizing the challenges of committing to a strategy without being exploited. It also explores the impact of updateful and updateless agents in diverse AI training scenarios, highlighting the complexities of super-intelligent interactions.