A recurrent network is used to generate neural network descriptions, which are then trained using reinforcement learning to maximize expected accuracy on a validation set. This method successfully designed a novel network architecture for the CIFAR-10 dataset that outperformed existing ones in accuracy and speed. Furthermore, the approach was applied to the Penn Treebank dataset, where a novel recurrent cell was created that surpassed the LSTM cell performance. This newly designed cell achieved superior perplexity on the Penn Treebank and was transferable to character language modeling with state-of-the-art results. Overall, the research demonstrates a method for automated and effective neural architecture search using reinforcement learning.
Generative search engines provide answers citing sources, and their optimization (GEO) depends on the user's location. The study audited how user location affects the sources cited by ChatGPT and Gemini. ChatGPT localizes results based on declared locations, whereas Gemini follows the network's country, leading to different source visibility. This demonstrates that visibility in generative search is location-specific, requiring audits to consider multiple channels. Consequently, location significantly influences the results generated by these engines.