One of the most critical components in machine learning projects is the quality of an organization’s database management system. And as artificial intelligence (AI) continues to grow more complex, access to adequate data is an increasingly important component of a company’s success. For deep learning, forward-thinking companies must choose to

Another year of remarkable AI (deep learning) advances is upon us – one filled with remarkable progress, disagreements, and, of course, conflicts. Here are a few of the most significant overarching trends that marked this year in deep learning. 1.SCALE CONTINUES TO BE A MAJOR FACTOR. Deep learning’s desire to

A new deep-learning framework developed at the Department of Energy’s Oak Ridge National Laboratory is speeding up the process of inspecting additively manufactured metal parts using X-ray computed tomography, or CT, while increasing the accuracy of the results. The reduced costs for time, labor, maintenance and energy are expected to

One of the most critical components in machine learning projects is the quality of an organization’s database management system. And as artificial intelligence (AI) continues to grow more complex, access to adequate data is an increasingly important component of a company’s success. For deep learning, forward-thinking companies must choose to

Deep learning models trained on data collected from past earthquakes can analyze real-time seismic data and issue advanced earthquake warnings. Large-magnitude earthquakes cause catastrophic damage, often triggering other natural disasters like landslides, tsunamis and fires. A recent example of an earthquake is the 6.1 magnitude earthquake in Afghanistan, which killed 1000