• 236 Posts
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Joined 3 years ago
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Cake day: June 30th, 2023

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  • The world’s pretty absurd. I’d like avoid participating in the absurdity as best as I can.

    It’s pretty absurd that we impregnate cows just to milk them.
    It’s pretty absurd that we slaughter pigs who have intelligence on par with three-year-old children.
    It’s pretty absurd that 80% of agricultural land is used for livestock even though 83% of our calories come from plant-based foods.
    It’s pretty absurd that our cattle herds are so large that they produce multiple gigatonnes of greenhouse gases from their digestion alone.
    It’s pretty absurd that we’ve bred chickens to lay 29 times more eggs per year than their wild counterparts.
    It’s pretty absurd that we do all of this at subsidized, unsustainable, industrial levels.
    It’s pretty absurd that we do this mostly for pleasure, taste, and tradition, not necessity. Especially when the vast majority of us have access to perfectly viable and often delicious alternatives.



  • If you’re referring to American billionaires, I’d probably agree. But I think this section from the article is more accurate and less to do with billionaires:

    Confusion and fear are possible reasons why people are becoming less accepting of gender diversity, said Wayne Bernakevitch, founder of the Regina Civic Awareness Action Network, an organization that lobbied in favour of the Parents’ Bill of Rights.

    “They’re concerned with finding a house to live in, you know, how do you pay for the next week’s groceries? And so getting into all of this is just a great distraction in their life,” Bernakevitch said in an interview with CBC’s Blue Sky.

    Politicians play a critical role in the creation of these attitudes, Leah Hamilton, a professor of business, communication studies and aviation at Mount Royal University, told Blue Sky.

    “We have seen over and over throughout history, especially during periods of perceived or real competition for resources like jobs and housing and other things, that politicians will often create scapegoats,” Hamilton said.







  • ARC Prize maintains multiple tracks around their benchmarks. They have “verified” leaderboards, “community” leaderboards, and they also run the ARC Prize competition.

    They update the “verified” leaderboards when they test raw LLMs without sophisticated harnesses. They seem to update this sporadically and only occasionally do press releases or blog posts about new scores. For example, the latest score from Claude Opus 5 (High) is 30% at $20,000, and they didn’t post about that as far as I know. Again, this just the raw LLM without an agentic or world-model harness.

    The ARC Prize competition has a harder set of criteria. Participants have to use smaller, open models with a limited compute budget, with open source code, and of course the solutions are verified by ARC Prize at the end of the competition.

    The “community” leaderboards, which is what this post is about, are self-reported and not verified by ARC Prize. There are no restrictions on what model is used or limitations on compute. So naturally they aren’t going to make official news releases about those, unless they decide to verify them at some point.

    The only reason I chose to post this is that the top solutions seem legitimate, with source code released, and two of them have associated papers.


  • It think it’s still unwise to talk about these topics in broad terms like AGI and even “intelligence”. We still have to pick the capabilities apart to have useful discussions about them. I agree these games are better tests than many benchmarks, but it’s also important to note that these solutions use a combination of well-designed deterministic harnesses, as well as LLMs. So it’s inaccurate to say that “LLMs have achieved AGI” (not sure if that’s what you were getting at). This feels like an important milestone, but we’ll have to continue to probe for failure cases in other categories of problems.

    Aside from emotional intelligence, experience, embodiment, etc., these ARC-AGI-3 solutions all rely on the sandbox being a safe environment to fail. The solutions iterate through the problem thousands of times before coming to a final solution. Many real-world human problems cannot be re-tried safely or efficiently.














  • I kinda agree with this, except the machine learning field should bear some responsibility for begetting LLMs. In particular, they got very used to the idea of scraping the internet for huge amounts of data needed for all types of models, and paid less and less attention to how much energy their training and inference was costing versus the value the models were providing. The seeds of the problems with LLMs existed before they landed on the scene.