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Boltzmann machine

Boltzmann machine

Boltzmann machine definition

Boltzmann machines are a type of artificial intelligence that learns complex patterns by trying out different combinations and figuring out which ones work best, similar to learning through trial and error. Boltzmann machines are versatile tools used for tasks that involve understanding complex patterns in data, like recognizing shapes in image processing, finding patterns in datasets, and enhancing personalization by recommending products, music, or movies that users might like.

These machines were introduced by Geoffrey Hinton and Terry Sejnowski in 1985 and are named after the physicist Ludwig Boltzmann, who made significant contributions to statistical mechanics, a field that influences the mathematical foundations of these machines.

See also: artificial intelligence, machine learning

How Boltzmann machines work

A Boltzmann machine consists of a network of units (or nodes), which can either be on (1) or off (0). These units are interconnected, with each connection having a weight that determines the strength and nature of the connection. The network includes visible units, which interact with the environment (i.e., input data), and hidden units, which capture the internal representations and correlations in the data.

A Boltzmann machine works in two main phases, leveraging its network of on/off nodes and their weighted connections:

  1. Positive phase (learning from data). In this phase, the machine is exposed to actual data and observes patterns where certain nodes are on or off. Based on this data, the machine adjusts the weights of its connections to increase the probability that its network will represent similar patterns. Essentially, it’s reinforcing connections that lead to patterns matching the data.
  2. Negative phase (learning to generate). Here, the machine operates without new input data. Instead, it activates its nodes based on the weights of its connections, generating its own patterns. The machine then adjusts its weights to reduce the likelihood of generating these self-created patterns if they don’t match the data observed in the positive phase. This step is crucial for the machine to differentiate between what patterns are actually present in the data versus patterns it might have generated on its own.

The core of learning in a Boltzmann machine lies in adjusting the weights of the connections. Weights are increased or decreased based on the discrepancy between the patterns the machine expects and the patterns it actually observes or generates. The goal is to minimize this discrepancy, making the machine’s generated patterns as close to the observed data patterns as possible.

Further reading

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