Humor generation is a challenging task in natural language processing due to limited resources and the quality of existing datasets. Available humor language resources often suffer from toxicity and duplication, limiting their effectiveness for training robust models. This paper proposes CleanComedy, a specialized, partially annotated toxicity-filtered corpus of English and Russian jokes collected from various sources. We study the effectiveness of our data filtering approach through a survey on humor and toxicity levels in various joke groups. In addition, we study advances in computer humor generation by comparing jokes written by humans with various groups of generative jokes, including our baseline models trained on the CleanComedy datasets.

A new variant of Newton's method - named Backtracking New Q-Newton's method (BNQN) - which has strong theoretical guarantee, is easy to implement, and has good experimental performance, was recently introduced by the third author. Experiments performed previously showed some remarkable properties of the basins of attractions for finding roots of polynomials and meromorphic functions, with BNQN. In general, they look more smooth than that of Newton's method. In this paper, we continue to experimentally explore in depth this remarkable phenomenon, and connect BNQN to Newton's flow and Voronoi's diagram. This link poses a couple of challenging puzzles to be explained. Experiments also indicate that BNQN is more robust against random perturbations than Newton's method and Random Relaxed Newton's method.

A new modification of the Neural Additive Model (NAM) called SurvNAM and its modifications are proposed to explain predictions of the black-box machine learning survival model. The method is based on applying the original NAM to solving the explanation problem in the framework of survival analysis. The basic idea behind SurvNAM is to train the network by means of a specific expected loss function which takes into account peculiarities of the survival model predictions and is based on approximating the black-box model by the extension of the Cox proportional hazards model which uses the well-known Generalized Additive Model (GAM) in place of the simple linear relationship of covariates. The proposed method SurvNAM allows performing the local and global explanation. A set of examples around the explained example is randomly generated for the local explanation. The global explanation uses the whole training dataset. The proposed modifications of SurvNAM are based on using the Lasso-based regularization for functions from GAM and for a special representation of the GAM functions using their weighted linear and non-linear parts, which is implemented as a shortcut connection. A lot of numerical experiments illustrate the SurvNAM efficiency.

Getty Images has released an artificial intelligence (AI) tool for publishers that recommends the best choice of images to accompany a news story. 'Panels by Getty Images' uses customisable filters and a self-improving algorithm that learns how an editor selects an image and optimises its performance over time. "The AI starts working for you as soon as you copy/paste your article into the tool," says Getty Images senior vice president of data and insights Andrew Hamilton. Artificial intelligence allows natural language processing, an established technique used to understand written text and interpreting or deriving insights from it. The tool works like a picture editor -- it reads the text and tries to understand what the story is about. It then offers the first round of picture suggestions based not only on individual keywords but the meaning of sentences and paragraphs.

In some cases, it could be just 0 or 1, with 1 meaning that there is a training set data point close to the location in question in the grid, 0 meaning that you are far enough away from any neighbor. To compute density estimates on each cell of the grid, draw a 3x3 window around each yellow cell, and add 1 to all locations (cells) in that 3x3 window. In our example, the number of groups (g 3: low risk, medium risk, high risk of default) will be multiplied by 2 (M / F) x 3 (young / medium / old), resulting in 18 groups, for instance "young females with medium risk of default" being one of these 18 groups. Of course, accessing a cell in the grid (represented by a 2-dim array), while extremely fast and not depending on the number of observations, still requires a tiny bit of time, but it is entirely dependent only on the size of the array, and its dimension.

We propose here a simple, robust and scalable technique to perform supervised clustering on numerical data. It can also be used for density estimation, and even to define a concept of variance that is scale-invariant. This is part of our general statistical framework for data science. Here we discuss clustering and density estimation on the grid. The grid can be seen as an 2-dimensional or 3-dimensional array.