Very interesting article from Anthropic about how, thanks to AI, they're able to streamline their process of researching and training better AI. Or as it's colloquially known: closing the loop
MACHINE LEARNING
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How to Reduce AI Model Costs with Self-Supervised Learning
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How to Reduce #AI Model Costs with Self-Supervised Learning by @antgrasso #ArtificialIntelligence #MachineLearning #ML
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Nemotron 3 Ultra open-weight release boasts impressive efficiency ratio
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And another open-weight release. Nemotron 3 Ultra has an ultra impressive capability:efficiency ratio! Design-wise, it carries forward the Mamba-2-attention hybrid stack and LatentMoE introduced in the previous Super variant. But everything is a bit bigger.
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AI’s core is context; solve context to solve any field.
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AI at its core is a context problem. Solve context, and you can solve any field.
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Challenge of feeling AI acceleration despite real model improvements
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A real problem with feeling the acceleration viscerally is that current models are really good and it is hard to feel the vibe difference on most individual tasks with new models, even as AIs continue to increase in ability by large amounts (which they actually are doing).
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Predictive maintenance data security risks vs. continuous sensor needs
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Predictive maintenance algorithms require continuous sensor and equipment data but pose security risks if they can communicate back to production systems.
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AI Research: Claude’s Improved Decision-Making Outperforms Humans
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AI research is a series of next-step decisions. We looked at sessions where a human researcher took a wrong turn, showed Claude the session up to that point, and asked it what to do next. Mythos Preview improved on humans 64% of the time—up from 22% in 2024.
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AI Self-Improvement Plausible if Trends Continue, But Research Judgment Lacks
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None of this guarantees recursive self-improvement is on the horizon. It’s not yet clear that Claude is capable of research judgment—of choosing the right problems to work on. But if these trends continue, AI systems designing and building their own successors is plausible. This
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Claude’s coding success jumps 50 points, rivaling human quality
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The speedup isn’t just in volume. On open-ended coding problems where answers are unclear, Claude’s success rate is now 76%—a 50 point jump in just 6 months. Many engineers also say Claude’s code quality is now on par with human code; we expect it to be better within the year.
