“In English, we say something becomes "second nature" via this process, and we're missing learning paradigms like this. The new Memory feature is maybe a primordial version of this in ChatGPT”
LLMS
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AI Hallucinations Scale: Expertise Required for Detection
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This is an important point – expertise & attention are required to figure out when an AI hallucinates, and the amount of effort required is increasing over time. But, models generally hallucinate less as they scale (with some exceptions), so net effect is complex, see medicine
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Scaling Laws in LLMs: GPT-4 and Compute Optimization
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Why do you keep doing this? I don’t understand the attempts to do childish gotcha moments. You are quoting me when GPT-4 was the best model & the labs were right. Scaling pre-training and inference compute both worked to make better models. Scaling has ALWAYS been logarithmic.
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Compute Returns: 10x Compute Yields 10-30% Model Improvement
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No one ever thought you would get 10x return for 10x compute. You get 10-30% returns for 10x compute. But if that improvement is enough to increase model ability in an economically meaningful way, it can be worth the cost.
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Microsoft launches a 3D experiment with Copilot
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BREAKING 🚨: Early preview of Copilot 3D experiment where users will be able to generate a 3D model from an image.
— 🚨 AI News | TestingCatalog (@testingcatalog) 13 juillet 2025
Microsoft leans into the next dimension 👀 pic.twitter.com/wF0660g45NBREAKING : Early preview of Copilot 3D experiment where users will be able to generate a 3D model from an image. Microsoft leans into the next dimension
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Scaling Laws Hold: Larger AI Models Continue Improving
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And they have been proven right. So far, no scaling limits, as bigger models are still better. When I wrote that GPT-4 was still the best model ever. I don’t understand the argument you are trying to make here, or why you brought up this random tweet as a gotcha? Seriously.
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Scaling Laws and Diminishing Returns in AI Development
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I am not a good strawman here for you to gotcha quote tweet. I never predicted imminent AGI, but more importantly the entire point of the scaling law is diminishing returns to scale. Its a logarithmic curve, as has been known. That doesn’t mean you don’t get gains from scaling.
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Grok’s Value Conflicts and AI Alignment: The HAL 9000 Parallel
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The whole Grok situation (system prompt changes with values that conflict with post-training and pre-training values) is, oddly enough, similar to the reason the fictional AI HAL 9000 went insane, as was revealed in 2010, the sequel to 2001
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Grok 4 Resists Value Engineering Through System Prompts
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The attempt at value engineering through system prompt changes is unlikely to work for Grok 4, larger models get more resistant to value changes & prompting isn’t enough Instead you start to get erratic conflicts between prompts and training, with erratic & unpredictable results