LessWrong (Curated & Popular)

AI expert Adam David Long discusses the three-sided debate about AI, involving AI pragmatists, doomers, and boosters. He argues that this framework clarifies the public debate and helps policymakers navigate the complexities of AI impact on society.

Explore ARCE Val's report on evaluating language model agents' abilities in acquiring resources, replicating, and adapting to challenges. Learn about the impact of fine-tuning on GPT-4's performance in autonomous tasks, emphasizing the need for continuous improvement and scaffolding for enhancing ARA capabilities.

I believe that sharing information about the capabilities and limits of existing ML systems, and especially language model agents, significantly reduces risks from powerful AI—despite the fact that such information may increase the amount or quality of investment in ML generally (or in LM agents in particular).Concretely, I mean to include information like: tasks and evaluation frameworks for LM agents, the results of evaluations of particular agents, discussions of the qualitative strengths and weaknesses of agents, and information about agent design that may represent small improvements over the state of the art (insofar as that information is hard to decouple from evaluation results).Source:https://www.lesswrong.com/posts/fRSj2W4Fjje8rQWm9/thoughts-on-sharing-information-about-language-modelNarrated for LessWrong by TYPE III AUDIO.Share feedback on this narration.[125+ Karma Post] ✓[Curated Post] ✓

Some early biologist, equipped with knowledge of evolution but not much else, might see all these crabs and expect a common ancestral lineage. That’s the obvious explanation of the similarity, after all: if the crabs descended from a common ancestor, then of course we’d expect them to be pretty similar.… but then our hypothetical biologist might start to notice surprisingly deep differences between all these crabs. The smoking gun, of course, would come with genetic sequencing: if the crabs’ physiological similarity is achieved by totally different genetic means, or if functionally-irrelevant mutations differ across crab-species by more than mutational noise would induce over the hypothesized evolutionary timescale, then we’d have to conclude that the crabs had different lineages. (In fact, historically, people apparently figured out that crabs have different lineages long before sequencing came along.)Now, having accepted that the crabs have very different lineages, the differences are basically explained. If the crabs all descended from very different lineages, then of course we’d expect them to be very different.… but then our hypothetical biologist returns to the original empirical fact: all these crabs sure are very similar in form. If the crabs all descended from totally different lineages, then the convergent form is a huge empirical surprise! The differences between the crab have ceased to be an interesting puzzle - they’re explained - but now the similarities are the interesting puzzle. What caused the convergence?To summarize: if we imagine that the crabs are all closely related, then any deep differences are a surprising empirical fact, and are the main remaining thing our model needs to explain. But once we accept that the crabs are not closely related, then any convergence/similarity is a surprising empirical fact, and is the main remaining thing our model needs to explain.Source:https://www.lesswrong.com/posts/qsRvpEwmgDBNwPHyP/yes-it-s-subjective-but-why-all-the-crabsNarrated for LessWrong by TYPE III AUDIO.Share feedback on this narration.[125+ Karma Post] ✓

This month I lost a bunch of bets.Back in early 2016 I bet at even odds that self-driving ride sharing would be available in 10 US cities by July 2023. Then I made similar bets a dozen times because everyone disagreed with me.Source:https://www.lesswrong.com/posts/ZRrYsZ626KSEgHv8s/self-driving-car-betsNarrated for LessWrong by TYPE III AUDIO.Share feedback on this narration.[125+ Karma Post] ✓[Curated Post] ✓

In the early 2010s, a popular idea was to provide coworking spaces and shared living to people who were building startups. That way the founders would have a thriving social scene of peers to percolate ideas with as they figured out how to build and scale a venture. This was attempted thousands of times by different startup incubators. There are no famous success stories.In 2015, Sam Altman, who was at the time the president of Y Combinator, a startup accelerator that has helped scale startups collectively worth $600 billion, tweeted in reaction that “not [providing coworking spaces] is part of what makes YC work.” Later, in a 2019 interview with Tyler Cowen, Altman was asked to explain why.Source:https://www.lesswrong.com/posts/R5yL6oZxqJfmqnuje/cultivating-a-state-of-mind-where-new-ideas-are-bornNarrated for LessWrong by TYPE III AUDIO.Share feedback on this narration.[Curated] ✓

The podcast explores the concept that rationality extends beyond winning, discussing the importance of feedback loops, applying rationality in specific domains, and avoiding generic self-help. It reflects on the Rationality movement and proposes refocusing on rationality as a professional tool. The speaker emphasizes the need for clear definitions and discusses the consequences of solely focusing on winning. They question the relationship between rationality, winning, and epistemics, highlighting the importance of appropriate cognitive algorithms and tool selection.

The Lightspeed application asks:  “What impact will [your project] have on the world? What is your project’s goal, how will you know if you’ve achieved it, and what is the path to impact?”LTFF uses an identical question, and SFF puts it even more strongly (“What is your organization’s plan for improving humanity’s long term prospects for survival and flourishing?”). I’ve applied to all three grants of these at various points, and I’ve never liked this question. It feels like it wants a grand narrative of an amazing, systemic project that will measurably move the needle on x-risk. But I’m typically applying for narrowly defined projects, like “Give nutrition tests to EA vegans and see if there’s a problem”. I think this was a good project. I think this project is substantially more likely to pay off than underspecified alignment strategy research, and arguably has as good a long tail.  But when I look at “What impact will [my project] have on the world?” the project feels small and sad. I feel an urge to make things up, and express far more certainty for far more impact than I  believe. Then I want to quit, because lying is bad but listing my true beliefs feels untenable.I’ve gotten better at this over time, but I know other people with similar feelings, and I suspect it’s a widespread issue (I encourage you to share your experience in the comments so we can start figuring that out).I should note that the pressure for grand narratives has good points; funders are in fact looking for VC-style megabits. I think that narrow projects are underappreciated, but for purposes of this post that’s beside the point: I think many grantmakers are undercutting their own preferred outcomes by using questions that implicitly push for a grand narrative. I think they should probably change the form, but I also think we applicants can partially solve the problem by changing how we interact with the current forms.My goal here is to outline the problem, gesture at some possible solutions, and create a space for other people to share data. I didn’t think about my solutions very long, I am undoubtedly missing a bunch and what I do have still needs workshopping, but it’s a place to start. Source:https://www.lesswrong.com/posts/FNPXbwKGFvXWZxHGE/grant-applications-and-grand-narrativesNarrated for LessWrong by TYPE III AUDIO.Share feedback on this narration.[Curated Post] ✓

Previously Jacob Cannell wrote the post "Brain Efficiency" which makes several radical claims: that the brain is at the pareto frontier of speed, energy efficiency and memory bandwith, that this represent a fundamental physical frontier.Here's an AI-generated summaryThe article “Brain Efficiency: Much More than You Wanted to Know” on LessWrong discusses the efficiency of physical learning machines. The article explains that there are several interconnected key measures of efficiency for physical learning machines: energy efficiency in ops/J, spatial efficiency in ops/mm^2 or ops/mm^3, speed efficiency in time/delay for key learned tasks, circuit/compute efficiency in size and steps for key low-level algorithmic tasks, and learning/data efficiency in samples/observations/bits required to achieve a level of circuit efficiency, or per unit thereof. The article also explains why brain efficiency matters a great deal for AGI timelines and takeoff speeds, as AGI is implicitly/explicitly defined in terms of brain parity. The article predicts that AGI will consume compute & data in predictable brain-like ways and suggests that AGI will be far more like human simulations/emulations than you’d otherwise expect and will require training/education/raising vaguely like humans1.Jake further has argued that this has implication for FOOM and DOOM. Considering the intense technical mastery of nanoelectronics, thermodynamics and neuroscience required to assess the arguments here I concluded that a public debate between experts was called for. This was the start of the Brain Efficiency Prize contest which attracted over a 100 in-depth technically informed comments. Now for the winners! Please note that the criteria for winning the contest was based on bringing in novel and substantive technical arguments as assesed by me. In contrast, general arguments about the likelihood of FOOM or DOOM while no doubt interesting did not factor into the judgement. And the winners of the Jake Cannell Brain Efficiency Prize contest areEge ErdilDaemonicSigilspxtr... and Steven Byrnes!Source:https://www.lesswrong.com/posts/fm88c8SvXvemk3BhW/brain-efficiency-cannell-prize-contest-award-ceremonyNarrated for LessWrong by TYPE III AUDIO.Share feedback on this narration.[125+ Karma Post] ✓

This post is not about arguments in favor of or against cryonics. I would just like to share a particular emotional response of mine as the topic became hot for me after not thinking about it at all for nearly a decade.Recently, I have signed up for cryonics, as has my wife, and we have made arrangements for our son to be cryopreserved just in case longevity research does not deliver in time or some unfortunate thing happens.Last year, my father died. He was a wonderful man, good-natured, intelligent, funny, caring and, most importantly in this context, loving life to the fullest, even in the light of any hardships. He had a no-bullshit-approach regarding almost any topic, and, being born in the late 1940s in relative poverty and without much formal education, over the course of his life he acquired many unusual attitudes that were not that compatible with his peers (unlike me, he never tried to convince other people of things they could not or did not want to grasp, pragmatism was another of his traits). Much of what he expected from the future in general and technology in particular, I later came to know as transhumanist thinking, though neither was he familiar with the philosophy as such nor was he prone to labelling his worldview. One of his convictions was that age-related death is a bad thing and a tragedy, a problem that should and will eventually be solved by technology.Source:https://www.lesswrong.com/posts/inARBH5DwQTrvvRj8/cryonics-and-regretNarrated for LessWrong by TYPE III AUDIO.Share feedback on this narration.[125+ Karma Post] ✓