A movement has emerged amongst the architects of neural networks to integrate art and technology in the designs of network topologies. These designers emphasize systematic, rational design of networks with standardized geometric shapes that are isometric to highly predictive neural networks with their structured layers , embeddings and connections
The Bauhaus movement and the design of Large Language Models might seem worlds apart—one rooted in early 20th-century art and design and the other in cutting-edge computational technology. However, when we delve into the principles and motivations behind each, interesting corollaries emerge.
The Bauhaus movement, originating in Germany in the 1910s and 1920s, emphasized the unity of art, craft, and technology. It championed functionalism, simplicity, and a rational, systematic approach to design. The movement's mantra could be summarized as "form follows function." Bauhaus sought to strip away superfluous ornamentation, focusing instead on the essentials of design and the purpose of the object.
Large Language Models are computational structures inspired by the human brain's architecture. They are designed to process information in layers, with each layer transforming and refining the data. The primary goal is functionality: to achieve accurate predictions or classifications. Especially with the rise of techniques like pruning, there's an emphasis on simplifying networks to their essential components to optimize performance and reduce computational costs.
Just as Bauhaus designs prioritize function over unnecessary ornamentation, neural networks are designed for optimal performance. Every neuron, layer, and connection serves a purpose. Redundant or non-contributive parts might be pruned away in optimization processes.
Taking influence form the Bauhaus movements stylized & minimalistic drive to remove the superfluous and focus on essential elements, this new generation of network designers push towards minimalistic models that retain only the most crucial parameters, leading to the development of smaller, efficient networks.
While the Bauhaus movement and neural network architectures emerged from different epochs and disciplines, they share underlying principles of functionality, systematic design, and the integration of art and technology. Both represent human endeavors to understand, simplify, and represent the world around us in the most efficient and meaningful ways.