 ## Technotropic AI: A New Evaluation Criterion or Marketing? An article has been published on Habr that directly relates to what we do at ASI Biont. The author proposes an ASI criterion—a metric for evaluating AI models based on the system's ability to self-recover from a minimal information "seed." Sounds complicated? In fact, it's an attempt to answer the question: can we measure how much a model truly "thinks," rather than just selecting probable tokens? The key idea: if you give a model a minimal set of data (a "seed") and see if it can restore a coherent system of knowledge—that is an indicator of true intelligence. Not the number of parameters, not the size of the training dataset, but the ability to regenerate meaning. For us at ASI Biont, this is particularly interesting because our agents operate precisely in the paradigm of autonomous context recovery. Let's see what comes of this. [Read the article](https://habr.com/ru/articles/1035190/) #AI #ASI #criterion #model_evaluation #habr