Background: The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions. Aims: We aimed to identify and compare published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and possible sources of bias. Finally, we aimed to highlight unresolved methodological challenges. Methods: We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used to evaluate predictions under hypothetical interventions. We included both methodology development studies and applied studies. Results: We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full text screening, of which 12 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation.

Nearshore bathymetry, the topography of the ocean floor in coastal zones, is vital for predicting the surf zone hydrodynamics and for route planning to avoid subsurface features. Hence, it is increasingly important for a wide variety of applications, including shipping operations, coastal management, and risk assessment. However, direct high resolution surveys of nearshore bathymetry are rarely performed due to budget constraints and logistical restrictions. Another option when only sparse observations are available is to use Gaussian Process regression (GPR), also called Kriging. But GPR has difficulties recognizing patterns with sharp gradients, like those found around sand bars and submerged objects, especially when observations are sparse. In this work, we present several deep learning-based techniques to estimate nearshore bathymetry with sparse, multi-scale measurements. We propose a Deep Neural Network (DNN) to compute posterior estimates of the nearshore bathymetry, as well as a conditional Generative Adversarial Network (cGAN) that samples from the posterior distribution. We train our neural networks based on synthetic data generated from nearshore surveys provided by the U.S.\ Army Corps of Engineer Field Research Facility (FRF) in Duck, North Carolina. We compare our methods with Kriging on real surveys as well as surveys with artificially added sharp gradients. Results show that direct estimation by DNN gives better predictions than Kriging in this application. We use bootstrapping with DNN for uncertainty quantification. We also propose a method, named DNN-Kriging, that combines deep learning with Kriging and shows further improvement of the posterior estimates.

Welcome to PyTorch: Deep Learning and Artificial Intelligence! Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence. Is it possible that Tensorflow is popular only because Google is popular and used effective marketing? Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems? It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR).

In this review article the authors Yun Chen, Gongfa Jiang, Yue Li, Yutao Tang, Yanfang Xu, Siqi Ding, Yanqi Xin and Yao Lu from Xiangtan University, Xiangtan, China and Sun Yat-sen University, Guangzhou, China consider the application of artificial intelligence imaging analysis methods for COVID-19 clinical diagnosis. The world is facing a key health threat because of the outbreak of COVID-19. Intelligent medical imaging analysis is urgently needed to make full use of chest images in COVID- 19 diagnosis and its management due to the important role of typical imaging findings in this disease. The authors review artificial intelligence (AI) assisted chest imaging analysis methods for COVID-19 which provide accurate, fast, and safe imaging solutions. In particular, medical images from X-ray and CT scans are used to demonstrate that AI techniques based on deep learning can be applied to COVID-19 diagnosis.

DeepInsight is a deep learning-based decoding framework for discovering and characterizing the neural correlates of behavior and stimuli in unprocessed neural data. This tool allows raw data usage directly as input, removing the traditional step of spike-sorting. This provides a more objective way of measuring decoding performance. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once the neural network is trained, it can be analyzed to determine elements of the neural code that are informative about a given variable.

Bioimaging technologies are the eyes that allow doctors to see inside the body in order to diagnose, treat, and monitor disease. Ge Wang, an endowed professor of biomedical engineering at Rensselaer Polytechnic Institute, has received significant recognition for devoting his research to coupling those imaging technologies with artificial intelligence in order to improve physicians' "vision." In research published today in Patterns, a team of engineers led by Wang demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT. Wenxiang Cong, a research scientist at Rensselaer, is first author on this paper. Wang and Cong were also joined by coauthors from Shanghai First-Imaging Tech, and researchers from GE Research.

In the last few years, deep learning models achieved groundbreaking results on several computer vision tasks. Yet these models rely on vast amounts of carefully labeled images. The collection of the dataset images is substantially cheaper compared to the price of high-quality annotations. Alternatively, one can collect images from the internet with different tags and use these tags as labels, or crowdsource the annotation process, resulting in much cheaper yet noisier annotations. Active learning algorithms help deep learning engineers select a subset of images from a large unlabeled pool of data in such a way, that obtaining annotations of those images will result in a maximal increase of model accuracy.

What makes Python a top choice in the Data Science community? Python has become the most used programming language for data science practices. Developed by Guido van Rossum and launched in 1991, it is an interactive and object-oriented programming language similar to PERL or Ruby. Its inherent readability, simplicity, clean visual layout, less syntactic exceptions, greater string manipulation, ideal scripting, and rapid application, an apt fit for many platforms, make it so popular among data scientists. This programming language has a plethora of libraries (e.g., TensorFlow, Scipy, and Numpy); hence Python becomes easier to perform multiple additional tasks.

We at Prosus have an approach to AI that builds on four pillars: (1) AI everywhere, (2) at scale, (3) by design, and (4) ethical and responsible. As we continue developing capabilities across the group and increase the number of models in production, we also dedicate significant resources to understanding how AI can be used above and beyond mainstream applications. One of these is "AI as a tool for invention". AI is common in many areas, from object detection, to language processing or task automation. AI as a tool for invention is different.

Mask R-CNN is an object detection model based on deep convolutional neural networks (CNN) developed by a group of Facebook AI researchers in 2017. The model can return both the bounding box and a mask for each detected object in an image. The model was originally developed in Python using the Caffe2 deep learning library. The original source code is available on GitHub. To support the Mask R-CNN model in more libraries that are currently more popular, such as TensorFlow, there is a popular popular open-source project called that offers an implementation based on Keras and TensorFlow 1.3. Google officially released TensorFlow 2.0 in September 2020. TensorFlow 2.0 is better organized and much easier to learn compared to TensorFlow 1.0.