  We have constructed a generative artificial intelligence model to predict
dual gravity solutions when provided with the input of holographic entanglement
entropy. The model utilized in our study is based on the transformer algorithm,
widely used for various natural language tasks including text generation,
summarization, and translation. This algorithm possesses the ability to
understand the meanings of input and output sequences by utilizing multi-head
attention layers. In the training procedure, we generated pairs of examples
consisting of holographic entanglement entropy data and their corresponding
metric solutions. Once the model has completed the training process, it
demonstrates the ability to generate predictions regarding a dual geometry that
corresponds to the given holographic entanglement entropy. Subsequently, we
proceed to validate the dual geometry to confirm its correspondence with the
holographic entanglement entropy data.
