Hunt Dinosaurs with Intel® AI Technology

Explore an end-to-end AI application with the goal of creating a likelihood map for dinosaur bone beds to help guide your next dinosaur hunt. Use machine learning algorithms, powered by oneAPI, such as Intel® Extension for Scikit-learn*, NumPy, and Intel® Distribution of OpenVINO™ toolkit as part of this AI dinosaur adventure.

First use Intel Extension for Scikit-learn to preprocess metadata and experiment with semi-supervised learning and clustering techniques on known dinosaur bone beds.

Then apply metadata labeling to aerial photos that are fed to a convolutional neural network. Use the Intel® oneAPI Deep Neural Network Library to classify the aerial images.

Finally, use Intel Distribution of OpenVINO toolkit to streamline the inference capability on Intel® CPUs. They process thousands of images and create a bone likelihood map that can be applied to other regions of the globe than the one they were initially trained on.

Note Since dinosaur bones on US Federal lands are protected, the actual map is blurred.


Robert Chesebrough is a technical evangelist for oneAPI at Intel. His educational background is in physics. For over three decades, Robert has worked in software development, and application and performance engineering for Fortune 100 companies and national laboratories. He is a data scientist who uses machine learning and deep learning.

Raymond Lo is a software evangelist at Intel focusing on the OpenVINO™ toolkit. Previously, he was the founder and chief technology officer (CTO) of an augmented reality company Meta, the technology evangelist for Samsung NEXT, and technical solutions consultant for Google* Cloud AI. During his PhD, Raymond worked with Professor Steve Mann (from MIT Media Lab), who is widely recognized as the father of wearable computing. Together they published numerous of research papers on the topic of GPGPUs, open source HDR videos, and open source wearable computing projects in the University of Toronto.

Rahul Nair is a machine learning architect at Intel with ten years of experience in data science and machine learning, and four years in applied deep learning. He joined Intel in the OpenStack Cloud team after finishing a master’s degree in computer engineering at the University of Texas. As a core member of the OpenStack security group, Rahul built tools for automated security analysis on the cloud.