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cuSpatial Accelerates Geospatial and Spatiotemporal Processing
The Internet of Things (IOT) has spawned explosive growth in sensor data. Location is some of the most important information generated by sensors, and dynamic location is vital in the case of mobile sensors. Examples include: mobile phones (GPS), vehicles, robots, and cameras. [Related Article: The Best Machine Learning... Read more
What Your Business Needs to Know About Data Variety
The bigger your business, the more likely your data is…interesting. Old methods of data collection put data in silos across several departments. The computing power to handle data in all its forms wasn’t there, so breaking it up in the name of agile operations was key. Now, we’ve got... 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
Machine Learning Model Fairness in Practice
Editor’s Note: See Jakub’s talk about Machine Learning “Model Fairness in Practice” at ODSC West 2019 In the last few years, the interest around fairness in machine learning has been gaining a lot of momentum. Rightfully so: our models are becoming more and more prevalent in our daily lives,... 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
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
What Do Managers and Decision Makers Need to Know About AutoML?
You want to be working in machine learning and artificial intelligence, but you don’t have the talent yet. You’re telling your board members that you’re using AI when you’re really just doing some basic data analysis. You feel like everyone is working in ML and you’re here just trying... Read more
Adapting Machine Learning Algorithms to Novel Use Cases
Editor’s Note: Kirk will present his talk “Adapting Machine Learning Algorithms to Novel Use Cases” at ODSC West 2019. If there was a metric for success in the data science profession, it would require a multi-dimensional scoring model. This metric would cover a data scientist’s technical skills and talents,... Read more