Sign up for our newsletter and get the latest big data news and analysis.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – October 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

How to Ensure Data Quality for AI

In this special guest feature, Wilson Pang, CTO of Appen, offers a few quality controls that organizations can implement to allow for the most accurate and consistent data annotation process possible. When we talk about quality training data, we’re talking about both the accuracy and consistency of those labels. Accuracy is how close a label is to the truth. Consistency is the degree to which multiple annotations on various training items agree with one another.

Healthy Hives: Cloud Analytics Helps Save the World’s Bee Population

In this machine learning cast study, we describe how cloud analytics technology is being applied to the Global Hive Network, an initiative to collect billions of individual data points from around the world and analyze them to understand the honeybee population’s overall health and its relationship with environments, weather patterns, forage, diseases, parasites, predator species, and pesticides.

Interview: Tammy Wang, VP of Data Science and Engineering at Riviera Partners

I recently caught up with Tammy Wang, VP of Data Science and Engineering at Riviera Partners to discuss her company’s partnering with the University of Virginia Economics department to build an AI-powered application that can help people successfully navigate STEM career paths, with a focus on developing a long-term career trajectory in tech. The solution has the potential to impact U.S. labor supply by addressing the increasing demand for technology talent, which has resulted from the rapid digitization and automation of the labor market.

TOP 10 insideBIGDATA Articles for October 2019

In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.

“Above the Trend Line” – Your Industry Rumor Central for 11/11/2019

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

9 Practical Actions to Improve Machine Learning for Fraud Prevention

In this special guest feature, Arjun Kakkar, Vice President Strategy and Operations at Ekata, provides 9 practical and actionable principles for product managers and business leaders working to use machine learning for fraud detection. The unique characteristics of online fraud detection, including the availability of large and diverse data sets with known outcomes, repeating patterns, and a need for quick decisions, make it a good candidate for machine learning.

AutoML in Practice

The compelling Oct. 15, 2019 presentation below is on behalf of one of my favorite Meetup groups: LA Machine Learning. The talk, “AutoML in Practice,” is by Danny D. Leybzon, a Solutions Architect at Qubole, a cloud-native big data platform. Automated Machine Learning (AutoML) is one of the hottest topics in data science today, but what does it mean? This presentation gives a broad overview of AutoML, ranging from simple hyperparameter optimization all the way to full pipeline automation.

Competition Uses AI to Combat Inequities in Early Childhood Education

Booz Allen Hamilton, Kaggle and PBS KIDS launched the fifth annual Data Science Bowl. This 90-day competition challenges thousands of data scientists and researchers from around the world to apply the power of artificial intelligence to combat inequities in early childhood education (ECE). More specifically, participants will create AI algorithms using anonymous gameplay data from the PBS KIDS Measure Up! App, which helps young children build math skills, to better understand children’s unique learning styles.

Topcoder On-Demand Digital Talent Platform Includes New Data Science, AI Features to Advance Enterprise Analytics

Topcoder, a Wipro company, and a technology network and on-demand digital talent platform, announced the addition of new data science and AI features to the Topcoder Platform. Highlights include native GPU support and the ability to develop advanced analytic solutions with any tool, library or cloud application service.