  Associative memory and probabilistic modeling are two fundamental topics in
artificial intelligence. The first studies recurrent neural networks designed
to denoise, complete and retrieve data, whereas the second studies learning and
sampling from probability distributions. Based on the observation that
associative memory's energy functions can be seen as probabilistic modeling's
negative log likelihoods, we build a bridge between the two that enables useful
flow of ideas in both directions. We showcase four examples: First, we propose
new energy-based models that flexibly adapt their energy functions to new
in-context datasets, an approach we term \textit{in-context learning of energy
functions}. Second, we propose two new associative memory models: one that
dynamically creates new memories as necessitated by the training data using
Bayesian nonparametrics, and another that explicitly computes proportional
memory assignments using the evidence lower bound. Third, using tools from
associative memory, we analytically and numerically characterize the memory
capacity of Gaussian kernel density estimators, a widespread tool in
probababilistic modeling. Fourth, we study a widespread implementation choice
in transformers -- normalization followed by self attention -- to show it
performs clustering on the hypersphere. Altogether, this work urges further
exchange of useful ideas between these two continents of artificial
intelligence.
