Most existing interpretable methods explain a black-box model in a post-hoc manner, which uses simpler models or data analysis techniques to interpret the predictions after the model is learned. However, they (a) may derive contradictory explanations on the same predictions given different methods and data samples, and (b) focus on using simpler models to provide higher descriptive accuracy at the sacrifice of prediction accuracy. To address these issues, we propose a hybrid interpretable model that combines a piecewise linear component and a nonlinear component. The first component describes the explicit feature contributions by piecewise linear approximation to increase the expressiveness of the model. The other component uses a multi-layer perceptron to capture feature interactions and implicit nonlinearity, and increase the prediction performance. Different from the post-hoc approaches, the interpretability is obtained once the model is learned in the form of feature shapes. We also provide a variant to explore higher-order interactions among features to demonstrate that the proposed model is flexible for adaptation. Experiments demonstrate that the proposed model can achieve good interpretability by describing feature shapes while maintaining state-of-the-art accuracy.

Heart failures in the United States cost an estimated 30.7 billion dollars annually and predictive analysis can decrease costs due to readmission of heart failure patients. Deep learning can predict readmissions but does not give reasons for its predictions. Ours is the first study on a deep-learning approach to explaining decisions behind readmission predictions. Additionally, it provides an automatic patient stratification to explain cohorts of readmitted patients. The new deep-learning model called a point-wise linear model is a meta-learning machine of linear models. It generates a logistic regression model to predict early readmission for each patient. The custom-made prediction models allow us to analyze feature importance. We evaluated the approach using a dataset that had 30-days readmission patients with heart failures. This study has been submitted in PLOS ONE. In advance, we would like to share the theoretical aspect of the point-wise linear model as a part of our study.

This will make this area of data science even more commonplace not only among top tech companies, but also small and medium-sized businesses across various verticals. However, one aspect which is potentially underrated when looking at the big trends, in terms of the future of data science, is around language frameworks used to make the everyday data science tasks possible. Today, there are two major frameworks, R or Python (or in more pragmatic data science circles, both!). One is praised for having the most beautifully designed data wrangling syntax and plotting libraries, the other for its expressiveness and having the best deep learning libraries available today. However, both suffer from being relatively slow as they're higher level languages.

Two years ago, I graduated college where I studied Economics and Finance. I was all set for a career in finance. Investment Banking and Global Markets -- those were the dream jobs. Months into the job, I picked up some Excel VBA and learnt how to use Tableau, Power BI and UiPath (a Robotics Process Automation software). I realized I was more interested in picking up these tools and learning to code rather than learning about banking products.

IMAGE: Researchers of the ICAI Group -- Computational Intelligence and Image Analysis -- of the University of Malaga (UMA) have designed an unprecedented method that is capable of improving brain images... view more Researchers of the ICAI Group -Computational Intelligence and Image Analysis- of the University of Malaga (UMA) have designed an unprecedented method that is capable of improving brain images obtained through magnetic resonance imaging using artificial intelligence. This new model manages to increase image quality from low resolution to high resolution without distorting the patients' brain structures, using a deep learning artificial neural network -a model that is based on the functioning of the human brain- that "learns" this process. "Deep learning is based on very large neural networks, and so is its capacity to learn, reaching the complexity and abstraction of a brain", explains researcher Karl Thurnhofer, main author of this study, who adds that, thanks to this technique, the activity of identification can be performed alone, without supervision; an identification effort that the human eye would not be capable of doing. Published in the scientific journal Neurocomputing, this study represents a scientific breakthrough, since the algorithm developed by the UMA yields more accurate results in less time, with clear benefits for patients. "So far, the acquisition of quality brain images has depended on the time the patient remained immobilized in the scanner; with our method, image processing is carried out later on the computer", explains Thurnhofer.

Deep learning is a complicated process that's fairly simple to explain. A subset of machine learning, which is itself a subset of artificial intelligence, DL is one way of implementing machine learning (automated data analysis) via what are called artificial neural networks -- algorithms that effectively mimic the human brain's structure and function. And while it remains a work in progress, there is unfathomable potential. In this article, we'll briefly explain how deep learning works and introduce the best companies in 2020. Deep learning is a subcategory of machine learning methods powered by artificial intelligence technologies. These systems first develop a deep domain insight and then provide this information to the end-users in a timely, natural, and usable way.

For e-retailers, deep learning offers immense opportunities to increase conversion rates and improve the brand image through positive customer experience. According to a 2018 U.S. Census Bureau report, the e-Commerce industry has shown impressive growth in the past one decade. The online revenues have steadily eaten into an additional 1.5% of the yearly retail sales for the past several years. Tech giant Amazon is projected to make up half of all eCommerce sales in the US by 2021. Statista predicts the global e-Commerce sales will amount to $ 4.88 trillion; thereby, showcasing a yearly growth of 20%.

Modern science requires modern technological solutions. As we prise the natural world apart in search of answers to ever more complex questions, we need to be thinking in new ways about our approach to the problems we are faced with. Several technologies have been developed over the past few years that are pushing the boundaries of our scientific knowledge to new heights. As these technologies develop scientists are looking into ways of using them in tandem, to produce more accurate results and new ways of approaching the problems of the modern scientific industry. Two such technologies that can be combined to produce a better understanding of biological systems are localization microscopy and deep learning.

Researchers have designed an unprecedented method that is capable of improving brain images obtained through magnetic resonance imaging using artificial intelligence. This new model manages to increase image quality from low resolution to high resolution without distorting the patients' brain structures, using a deep learning artificial neural network -a model that is based on the functioning of the human brain- that "learns" this process. The study was published in the scientific journal Neurocomputing. "Deep learning is based on very large neural networks, and so is its capacity to learn, reaching the complexity and abstraction of a brain", explains researcher Karl Thurnhofer, main author of this study, who adds that thanks to this technique, the activity of identification can be performed alone, without supervision; an identification effort that the human eye would not be capable of doing. This study represents a scientific breakthrough, since the algorithm developed by the UMA yields more accurate results in less time, with clear benefits for patients.

A common analogy in artificial intelligence(AI) circles is that training data is the new oil for machine learning models. Just like the precious commodity, training data is scarce and hard to get at scale. While these type of models are relatively easy to create compare to other alternatives, they have a strong dependency in training data that results prohibited for most organizations. This problem becomes bigger with the scale of the machine learning models. Recently, Uber engineers published a paper proposing a new method called Generative Teaching Networks(GTNs) that create learning algorithms that automatically generate training data.