When AI Learns to Dream

Computers were initially seen as logical machines for producing rigid and unambiguous solutions, lacking creativity or ambiguity. However, a shift occurred with the emergence of generative AI, leading to the exploration of uncertainty and randomness in machine learning.

Boltzmann machines, developed in the 1980s, introduced the integration of uncertainty and randomness into machine learning, departing from rigid data storage and deterministic computations. They aimed to reconcile probability and inherent uncertainty with computer operations.

A brief refresher on Hopfield networks, which are models of associative memory inspired by the brain's ability to recall patterns from partial or noisy inputs. These networks operate by assigning energy values to each state and recalling the best-matching stored memory.

Hopfield networks excel at pattern completion but lack the ability to create new patterns or understand underlying data structures. In contrast, Boltzmann machines, like jazz musicians, are more flexible and can learn underlying probability distributions.

Exploration of the technical modifications needed to transform a Hopfield network into a Boltzmann machine, focusing on stochasticity and hidden units. These modifications enable Boltzmann machines to capture probability distributions and escape local minima in the energy landscape.

Detailed explanation of the Boltzmann distribution, linking energy to probability, and the stochastic learning rule in Boltzmann machines. The contrastive learning rule introduces the concept of preserving realistic patterns and avoiding undesirable states during learning.

Shift in learning objectives from memorizing patterns to capturing underlying data distributions in Boltzmann machines. Introduction of hidden units and the iterative learning process to optimize weights for better modeling of data.

Introduction to Restricted Boltzmann Machines (RBM) as a modification of Boltzmann machines where connections between visible and hidden units are allowed. RBMs offer computational efficiency and parallel updates, enhancing learning and inference processes.