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Bye Bye Big Data Era, the Insight Era is Here
For the last decade, we’ve ridden the roller coaster of big data. We’ve developed tools to mine, structure, and visualize data in ways never before possible. We’ve refined machine learning and artificial intelligence to discover amazing use cases for data that were not previously clear to the naked eye.... Read more
Solving Problems in Machine Learning with scAlign
Editor’s Note: See Nelson’s tutorial on this subject of  solving problems in machine learning at his talk “Data Harmonization for Generalizable Deep Learning Models: From Learning to Hands-On Tutorial” at ODSC West 2019. One of many common problems in machine learning (ML) is to learn models that work well... Read more
ML and Behavioral Economics for Personalized Choice Architecture
Personalized choice architecture is a new and interesting field, surrounding the idea that we can predict and influence consumers’ choices, ensuring that they get the best product or best outcome possible. In a recent paper by Emir Hrnjic and Nikodem Tomczak (researchers at NUS Business School and the National... Read more
How AI is Changing Creativity, and How You Can Use it Too
The limits and expectations of creativity are always being expanded upon: new tools are being created, new techniques discovered, new artists entering the scene. Now more than ever, AI is being considered in all three of those aspects. It begs the question, how is AI changing creativity? And moreover,... Read more
Machine Learning for Continuous Integration
Editor’s Note: Andrea Frittoli and Kyra Wulffert are presenting their talk“Machine Learning for Continuous Integration” at ODSC 2019 Europe. Continuous Integration and Data As more applications move to a DevOps model with CI/CD pipelines, the testing required for this development model to work inevitably generates lots of data. This... Read more
The Best Machine Learning Research of Summer 2019
Academic institutions, AI labs, and research departments of other organizations are constantly generating novel insights into data science, whether it’s machine learning, deep learning, NLP, or other disciplines. Summer 2019 generated some interesting machine learning research, and here are a few of our top picks. [Related Article: The Best... Read more
Interoperable AI: High-Performance Inferencing of ML and DNN Models Using Open-Source Tools
TensorFlow? PyTorch? Keras? There are many popular frameworks to choose from when working with deep learning and machine learning models, each with its own pros and cons for practical usability in product development or research. Once you decide which to use to train your model, you need to figure... Read more
FSGAN: Subject Agnostic Face Swapping and Reenactment
Generative adversarial networks are getting a lot of press as the general public raises their fears on the forgery techniques, but the data science community has been tracking their development, precision, benefits, and threats for years. Recently though, the team of Yuval Nirkin, Yosi Keller (both of Bar-Ilan University),... Read more
The History and Future of Machine Learning at Reddit
Let’s take a walk through the history of machine learning at Reddit from its original days in 2006 to where we are today, including the pitfalls and mistakes made as well as their current ML projects and future efforts in the space. Based on a talk given by Anand... Read more
How to Prepare for an Automated Future: 7 Steps to Machine Learning
The increasingly digital economy requires boards and executives to have a solid understanding of the rapidly changing digital landscape. Naturally, artificial intelligence (AI) is an important stakeholder. Those organisations that want to prepare for an automated future should have a thorough understanding of AI. However, AI is an umbrella term that... Read more