From ChatGPT to a virtual girlfriend, a range of weird and wonderful chatbots have emerged in recent months amid the proliferation of artifical intelligence (AI). But the latest AI-powered app is arguably the most bizarre yet. The app, called Text With Jesus, is designed for'devoted Christians seeking a deeper connection with the Bible's most iconic figures', according to its developers. As the name suggests, users can'text' with Jesus, as well as a number of other figures including Mary, Joseph, Peter and Matthew. And while the basic app is free, users can opt to pay $2.99/month (£2.35/month) to speak to Satan. Text With Jesus is designed for'devoted Christians seeking a deeper connection with the Bible's most iconic figures', according to its developers Text With Jesus was trained on all publicly available versions of the Bible, including the King James Version, the New International Version and the New American Standard Bible, according to its developers.

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Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation. Such an approach requires either some heuristics to sample a batch of design points at one active learning iteration, or retraining the neural network after adding each data point, which is computationally inefficient. Moreover, uncertainty estimates for neural networks sometimes are overconfident for the points lying far from the training sample. In this work we propose to approximate Bayesian neural networks (BNN) by Gaussian processes, which allows us to update the uncertainty estimates of predictions efficiently without retraining the neural network, while avoiding overconfident uncertainty prediction for out-of-sample points. In a series of experiments on real-world data including large-scale problems of chemical and physical modeling, we show superiority of the proposed approach over the state-of-the-art methods.

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