Designing ways to account for the quirks of AI models’ behavior is becoming ever-more important: as the models’ capabilities on real-world tasks get better, there’ll be a lot of value in setting them up for success.
MACHINE LEARNING
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Humachinology Optimizing Human Machine Innovation and Creativity
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In the world of humachineology and humachinekind, humachineologically inspired innovation and creativity can optimize the role of humans and machines in shaping reality.
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Humans and Machines Evolving Together in Humachineological Systems
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In the world of humachineology and humachinekind, humans and machines are living, working, and evolving together in humachineological systems.
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Federated Learning: Balancing Collective Intelligence with Data Sovereignty
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When sensitive data must remain in place, yet collective intelligence is needed, a distributed approach to AI emerges. Federated learning enables this balance, allowing models to improve across networks without compromising data sovereignty or transparency. Microblog @antgrasso
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Scaling Laws and Symmetry in Neural Networks Research
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Scaling Laws and Symmetry The common belief is that scaling outperforms inductive biases. Give the model enough data and compute, and it will learn the structure on its own. But this new research finds the opposite. Researchers conducted comprehensive scaling experiments on
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AI Benchmarks Threaten Job Security and Professional Safety
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You always think you're safe until your job becomes a benchmark.
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Understanding Probabilistic Response Generation in LLMs
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The real breakthrough isn't the technique itself it's understanding why it works.
— God of Prompt (@godofprompt) 18 décembre 2025
LLMs generate responses probabilistically. The first answer is just the highest-probability path through their training data. But "most probable" doesn't mean "most correct."
When you introduce… pic.twitter.com/6aANnq7UHNThe real breakthrough isn't the technique itself it's understanding why it works. LLMs generate responses probabilistically. The first answer is just the highest-probability path through their training data. But "most probable" doesn't mean "most correct." When you introduce
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Improving AI Model Reasoning Through Adversarial Prompting Techniques
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But here's where it gets insane. The technique doesn't just improve accuracy on the current problem. It actually teaches the model better reasoning patterns for future questions.
When you force devil's advocate mode repeatedly, the model starts internalizing that adversarial -
Elastic Reasoning: Cognitive-Inspired Approach for Large Language Models
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Beyond Fast and Slow: Cognitive-Inspired Elastic Reasoning for Large Language Models Paper: https://
arxiv.org/abs/2512.15089 -
Tesla has best world models for training robots
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Yeah, world models are being used to train robots. Awesome, huh? And who has the best world models? Tesla.