What We're Uncovering in the Real World of AI Research and Development

WWT's data scientists routinely participate in artificial intelligence (AI) research and development (R&D) projects that we believe will be the next big area of focus for our customers.

Real AI and ML insights

Most of our customers do not have the scientists, technologists or processing capacity—not to mention the time—to take on complex R&D projects that can have massive impacts on their business strategies. Our goal is to take on this lofty task and share our findings with our customers through a series of white papers.

The knowledge gained through R&D helps our team stay on the bleeding-edge of this ever-changing space, and ultimately create differentiated capabilities in the AI and ML space. Through our Advanced Technology Center, WWT data scientists can develop and demonstrate these new and exciting ideas.

A new kind of AI white paper

Most of the thought leadership and white papers produced in the AI and ML fields are produced in academic settings. The results may have applicability to industry, but they're not driven by industry needs. While informative, they're not being proven out in the real world. WWT's data scientists come from these academic and research-focused organizations, but our test ground is industry.

We have the privilege of experimenting with some of the most fascinating data sets and technologies because we're working and partnering with the biggest organizations in the world, and they are betting on us to open new lines of business through AI and ML.

We are excited to embark on this R&D effort and share some of our most insightful findings real-time. The white papers will be deeply technical, but all grounded in practical challenges we are seeing with our customers.

White papers

• Improving Mining Shovel Tooth Failure Detection Using Computer Vision-Based Methodologies
• Deep Learning with NVIDIA DGX-1
• Recommendation Engine: ATC Connect
• Leveraging Neural Network Autoencoders and K-Means Clustering to Segment Energy Consumers Based on Meter Reading Data
• Image Classification of Race Cars
• Obscuring and Analyzing Sensitive Information With Generative Adversarial Networks
• Privately Training an AI Model Using Fake Images Generated by Generative Adversarial Networks
• Machine Learning Models for Route Consolidation