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The term “AGI” is currently a vague, moving goalpost. To ground the discussion, we propose a comprehensive, testable definition of AGI. Using it, we can quantify progress: GPT-4 (2023) was 27% of the way to AGI. GPT-5 (2025) is 58%. Here’s how we define and measure it: 🧵
This video is a great illustration of scenarios from @DKokotajlo et al’s “AI 2027,” highlighting the major risks of the race toward AGI. AI development needs to be steered towards safer, more beneficial outcomes. youtube.com/watch?v=5KVDDf…
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We're Not Ready for Superintelligence
What does it mean when 6 of the most recent data points are above the trend line? Asking for a friend. - Big thanks to #metr for their awesome work in this space!
GPT5 calendar preview thoughts: I wonder how many people will show up at Bluebottle on Fillmore tomorrow at 1030am 😂
Is this an IRL prompt injection? 😂
Meanwhile, @Apple 'intelligence'... yes Apple, ok. Ah-huh. Thats lovely, well done dear. Keep trying! 🥔
I asked o3 to analyse and critique Apple's new "LLMs can't reason" paper. Despite its inability to reason I think it did a pretty decent job, don't you?
Oh wow oh wow oh wow! 4D Gaussian Splatting built on top of the @PlayCanvas Engine by Chinese company 4DV. 😍
Creating high quality 3DGS objects or scenes is simpler than ever, yet rendering them still lacks robust support in many toolchains. Today, we're excited to unveil Forge (forge.dev), a cutting-edge 3DGS renderer that effortlessly integrates with THREE.js.
Software is undergoing a phase transition into a network of networks. AI is becoming the primordial soup that connects all the loose nodes. Commoditized intelligence unlocks networks of networks for a new paradigm of scale and personalization. Link: sashakrecinic.substack.com/p/the-future-o…
🚨 Last call for AI founders + builders! We're going live (virtually) from AWS today, 2–4:30PM PT ⚡ Our founder will be sharing how to turn your janky AI prototype into a multi-tenant SaaS empire, powered by our Model API :) Topics include: 👉 "Agentic repos" (yes, it's a…
1.5 yrs ago, we set out to answer a seemingly simple question: what are we *actually* getting out of RL in fine-tuning? I'm thrilled to share a pearl we found on the deepest dive of my PhD: the value of RL in RLHF seems to come from *generation-verification gaps*. Get ready to🤿!
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