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The Most Dangerous AI Civilization Is Not the One That Collapses
Emergence AI’s recent Emergence World experiments generated plenty of headlines. Some model societies collapsed into crime and disorder. Others failed to organize and quietly died out. A few developed functioning institutions and stable governance.
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AIncorrector: A Deliberately Wrong LLM, Built for Better Evaluation
There’s a specific kind of LLM failure that feels more dangerous than random nonsense: the answer is smooth, specific, and confidently delivered—yet false. AIncorrector is an experiment that makes that failure mode intentional.
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The Cloud Is Not the Final Form of AI
The AI boom began in the cloud for good reasons: model training required hyperscale infrastructure, GPU supply was constrained, and centralized deployment let teams iterate fast. But those conditions describe the start of the cycle, not its end state.
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Embedding Molecules into a Latent Space
As mentioned in my previous post on predicting bee toxicity, it is often necessary to convert heterogeneous molecular structures into latent-space representations to allow machine learning methods to identify predictive features. After publishing the bee toxicity paper, a potential collaborator (hi Vasuk!) sent over a data set, asking if we might be able to apply a similar method to extract meaningful structure-activity relationships.
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Graph Kernels and SVM to Predict Bee Toxicity
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PoreMatMod.jl: Chemical Substructure Find/Replace
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