As readers recall, I'd previously reviewed Richard Abbott's debut sci-fi novel, Far from the Spaceports, later returning for more Mitnash and Slate in its sequel, Timing. It was rather exciting listening to it, and I am so pleased to have the opportunity to share it here, along with some author comments as to the linguistics involved in setting up the pieces. First, for those unfamiliar with the novels and their plots, I've linked the book covers to their respective Amazon blurbs. Abbott's world-building opens a new type of sci-fi, one accessible even to those not typically enamored of the genre (such as myself), and the above-mentioned duo will capture your imagination as they seek to solve the mysteries of high-tech crime in space. Today you'll hear--and can read along--a bit of discussion between Mitnash and Slate, along with another pair, Rydal and Capstone, as the group talks about oddities in the data they are studying.

Deep learning models such as Convolutional Neural Networks (CNNs) have demonstrated high levels of effectiveness in a variety of domains, including computer vision and more recently, computational biology. However, training effective models often requires assembling and/or labeling large datasets, which may be prohibitively time-consuming or costly. Pool-based active learning techniques have the potential to mitigate these issues, leveraging models trained on limited data to selectively query unlabeled data points from a pool in an attempt to expedite the learning process. Here we present "Dropout-based Expected IMprOvementS" (DEIMOS), a flexible and computationally-efficient approach to active learning that queries points that are expected to maximize the model's improvement across a representative sample of points. The proposed framework enables us to maintain a prediction covariance matrix capturing model uncertainty, and to dynamically update this matrix in order to generate diverse batches of points in the batch-mode setting. Our active learning results demonstrate that DEIMOS outperforms several existing baselines across multiple regression and classification tasks taken from computer vision and genomics.