Breaking the bonds of generative artificial intelligence by minimizing the maximum entropy

The emergence of generative artificial intelligence (GenAI), comprising large language models, text-to-image generators, and AI algorithms for medical drug and material design, had a transformative impact on society. However, despite an initial exponential growth surpassing Moore's law, progress is now plateauing, suggesting we are approaching the limits of current technology. Indeed, these models are notoriously data-hungry, prone to overfitting, and challenging to direct during the generative process, hampering their effective professional employment. To cope with these limitations, we propose a paradigm shift in GenAI by introducing an ab initio method based on the minimal maximum entropy principle. Our approach does not fit the data. Instead, it compresses information in the training set by finding a latent representation parameterized by arbitrary nonlinear functions, such as neural networks. The result is a general physics-driven model, which is data-efficient, resistant to overfitting, and flexible, permitting to control and influence the generative process. Benchmarking shows that our method outperforms variational autoencoders (VAEs) with similar neural architectures, particularly on undersampled datasets. We demonstrate the methods effectiveness in generating images, even with limited training data, and its unprecedented capability to customize the generation process a posteriori without the need of any fine-tuning or retraining.