Data science

IT modernization and innovative new technologies change the healthcare industry significantly. This blog series explores how data streaming with Apache Kafka enables real-time data processing and business process automation. Real-world examples show how traditional enterprises and startups increase efficiency, reduce cost, and improve the human experience across the healthcare value chain, including pharma, insurance, providers, retail, and manufacturing. This is part five: Machine Learning and Data Science. Examples…

Artificial intelligence is continuously evolving to benefit many different industries. Machines are wired using a cross-disciplinary approach based on mathematics, computer science, linguistics, psychology, and more. Algorithms often play a very important part in the structure of artificial intelligence, where simple algorithms are used in simple applications, while more complex ones help frame strong artificial intelligence. As technology advances, previous benchmarks that defined artificial intelligence become outdated.…

A New York Times piece last year addressed the question, "Are you ready to fly without a human pilot?" which sparked a vigorous conversation on Facebook. Many users responded with comments that were quite emotional, and other users also volunteered their expertise on the aviation sector. Others claimed that people were more amenable to accepting autonomous automobiles than pilotless aircraft, maybe because driving feels safer than flying. Technologies like computer vision power the automation and self-driving cars, but…

Machine learning (ML) has been developed and growing fast. It also depends on an expert in ML to develop the ML model, i.e. pre-processing, building model, feature engineering, tuning of hyper-parameters, etc. Data scientists have many demanding responsibilities, and challenges. These challenges include the selection of the best algorithm, modification of this algorithm over many iterations, and identification and tuning of the hyper-parameters many times. Despite going through difficult processes, it is worth noting to…

                                Figure 1 GNN Demo Using PyTorck Lightning and PyTorch Geometric Using PyTorch Lightning with Graph Neural Networks In the world of deep learning, Python rules. But while the Python programming language on its own is swift to develop in a so-called “high-productivity” language, execution speed pales in comparison to compiled and lower-level languages like C++ or FORTRAN. One of the fundamental drivers of the neural network renaissance of the 2010s to today, aka the advent of deep learning,…

Introduction Attempts at hand-crafting algorithms for understanding human-generated content have generally been unsuccessful. For example, it is difficult for a computer to “grasp” the semantic content of an image - e.g., a car, cat, coat, etc....… - purely by analyzing its low-level pixels. Color histograms and feature detectors worked to a certain extent, but they were rarely accurate for most applications. In the past decade, the combination of big data and deep learning has fundamentally changed the way we approach…

Choosing the right project management methodology can be crucial for your project development. It will help you avoid mistakes, speed up the whole process, and support in discovering the problems of your target groups. The last issue is fundamental. Only after a deep understanding of the needs of your target group will you be able to develop a solution that will solve their problems. There are many approaches to project management focusing on discovering problems, and design thinking is one of them. AI is becoming a more…