Scaling lessons from Bayesian modeling and a challenged NeuroAI assumption

After writing three papers on streaming variational inference for Bayesian nonparametric models, a researcher says he learned that sophisticated methods that don’t scale lose to simple methods that do. He also describes a paper challenging what he calls a prevailing NeuroAI myth: that training networks to path integrate would yield the same neural representations found in mammalian brains.