Last month, Canadian Mining & Energy magazine published an interview with Motion Metrics President and CEO Shahram Tafazoli. The article explains how Motion Metrics has used artificial intelligence (AI) to improve our existing products and penetrate new markets. Dr. Tafazoli also shared exciting new plans to bring our technology to an unlikely frontier – outer space.
Motion Metrics has achieved significant growth in the past three years, effectively doubling its revenue. Our continued success is partly due to our focus on incorporating artificial intelligence into our product line. “Our investment in AI has grown in our company, says Dr. Tafazoli. “We have now brought deep learning to all of our products and we are essentially an applied AI based company for tough mining challenges. Today, AI defines us.”
Deep learning technology has also helped Motion Metrics enter the oil sands mining market. Until recently, unique environmental challenges have prevented Motion Metrics from offering ShovelMetrics™ as a viable missing tooth detection solution for oil sands applications. Oil sand is a viscous, sticky substance that can accumulate in the GET components of shovel buckets as they dig; this buildup obscures the bucket teeth and lip shrouds, making it difficult to capture and analyze images. To overcome these challenges, Motion Metrics modified the hardware packaged with our conventional ShovelMetrics™ system and worked to improve the system performance using AI. “That means we designed and developed the deep learning networks and manually labelled a large set of bucket images,” explains Dr. Tafazoli. “Thanks to AI and improved engineering, we are starting to capture a market we previously had challenges with. The last few months have been an opening in the oilsands for us.” You can learn more about ShovelMetrics™ Oil Sands Edition on our product page.
Another product that has been shaped by AI is our new BeltMetrics™ system, a ruggedized fragmentation analysis and empty belt detection solution designed for mining operations worldwide. Using a rugged stereo camera and deep learning algorithms, BeltMetrics™ can detect rocks as small as 0.6 cm and operates 24 hours per day without interrupting the production cycle. For more information about this new solution, visit the BeltMetrics™ product page.
Dr. Tafazoli also unveiled plans to assess the feasibility of using our deep learning technology in the commercial space sector. There have been many small-scale ventures in gathering samples of celestial material to date and, given our expertise in vision-based AI, Motion Metrics has a wealth of relevant knowledge to share. “We are finding a good overlap of what we have done and what can be useful in space mining,” explains Dr. Tafazoli. This announcement follows a January meeting with Professor Dante Lauretta, the principle investigator of NASA’s Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) asteroid sample return mission. To read more about the meeting, visit our blog.
The full article is available here, courtesy of Canadian Mining & Energy.