AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Emerging computer hardware often trades reliability for energy efficiency; here we show that large-language models (LLMs) can be trained to tolerate this unreliability, and that rather than degrading, their error resilience actually increases as they grow. Modified neural scaling laws inferred from 40,000 GPU-hours of...
It is found that though continual learning methods help, fine-tuning reduces robustness across pretrained models, suggesting that starting with the strongest foundation model is not necessarily the best approach for performance on specialist tasks.
It is found that a structured entorhinal-hippocampal memory model, which provides a content-independent metric scaffold based on grid cells for storing experience, coupled to a policy recurrent network, succeeds at all three forms of generalization.
Comparisons are fundamental to science: experiment against model, one organism against another, a system against itself across time. Because many systems, from brains to climate, are characterized by how they evolve in time, it is a natural goal to compare their dynamics. Dynamical systems comparison is well defined, b...
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