Fifteen years after a magnitude 9.1 earthquake and tsunami struck off the coast of Indonesia, killing more than 200,000 people in over a dozen countries, geologists are still working to understand the complex fault systems that run through Earth's crust. While major faults are easy for geologists to spot, these large features are connected to other, smaller faults and fractures in the rock. Identifying these smaller faults is painstaking, requiring weeks to study individual slices from a 3D image. Researchers at the University of Texas at Austin are shaking up the process with deep learning models that identify geologic fault systems from 3D seismic images, saving scientists time and resources. The developers used NVIDIA GPUs and synthetic data to train neural networks that spot small, subtle faults typically missed by human interpreters.

Abdominal ultrasound tests for organ abnormalities haven't changed much in the past decade, with a doctor moving a wand over the patient's abdomen to gaze at blurry images. But the process could get accelerated by a thousand times with improved accuracy, based on deep learning work by U.S. researchers. Tests typically take around half an hour. That may not seem like a long time, but given that hospitals do thousands of these scans every year, they end up spending massive amounts of time providing this procedure when they could be seeing more patients. These examinations, done to diagnose abnormalities in various internal organs including the kidneys, liver or gallbladder, require substantial effort from practitioners who must find the right angle for ultrasound imaging, annotate these views in text and record relevant measurements.

Cue the sad tuba and attorney jokes: Machines just landed the hurt on lawyers. LawGeex, an Israel-based startup focused on automating contract reviews, released a study showing its AI software pummels lawyers in document review accuracy. The AI service outperformed 20 corporate lawyers at identifying legal risks in nondisclosure agreement contracts. But don't worry, the machines got no papercuts. Undisclosed, however, is whether the lawyers involved in the study have sent their billable hours invoices to the machines for payment.

If there were a World Series of big data baseball analytics, Claudio Silva would be in the starting lineup. By tracking every movement of every player and the ball throughout the game, it's changed how coaches evaluate and train players and how fans watch the game. But Silva is swinging for the fences. He's now using GPU-accelerated deep learning to reveal minute details of player behavior and game patterns, which has the potential to revolutionize how coaches manage players and plan strategy. It could even give them the ability to make predictions about some aspects of the game.