Christoph Mertz, the principal project scientist at Carnegie Mellon University's Robotics Institute, started taking pictures of the hills overlooking Pittsburgh's West End on his smartphone. "Every day, for months, I was collecting images of these hillsides," Mertz said. "I wanted to see if I could use these pictures as a way to predict the next landslide." Landslides are natural phenomena, but many of the conditions that can increase their likelihood are caused by human activity, such as directing surface runoff to an area or altering natural slopes for the construction of buildings and roads. Combined with increased rainfall rates related to climate change, landslides in the United States have become more common and more severe.

In December 2014, I asked whether we were at the beginning of "the end of the Hadoop bubble." I kept updating my Hadoop bubble watch (here and here) through the much-hyped IPOs of Hortonworks and Cloudera. The question was whether an open-source distributed storage technology which Google invented (and quickly replaced with better tools) could survive as a business proposition at a time when enterprises have moved rapidly to adopting the cloud and "AI"--advanced machine learning or deep learning. In January 2019, perennially unprofitable Hortonworks closed an all-stock $5.2 billion merger with Cloudera.

AS THE NAVY plane swooped low over the jungle, it dropped a bundle of devices into the canopy below. Some were microphones, listening for guerrilla footsteps or truck ignitions. Others were seismic detectors, attuned to minute vibrations in the ground. Strangest of all were the olfactory sensors, sniffing out ammonia in human urine. Tens of thousands of these electronic organs beamed their data to drones and on to computers.

In recent years, computer-driven image recognition systems that automatically recognize and classify human subjects have become increasingly widespread. These algorithmic systems are applied in many settings – from helping social media sites tell whether a user is a cat owner or dog owner to identifying individual people in crowded public spaces. A form of machine intelligence called deep learning is the basis of these image recognition systems, as well as many other artificial intelligence efforts. This essay on the lessons we learned about deep learning systems and gender recognition is one part of a three-part examination of issues relating to machine vision technology. Interactive: How does a computer "see" gender?

The field of artificial intelligence (AI) has progressed rapidly in recent years, matching or, in some cases, even surpassing human accuracy at tasks such as image recognition, reading comprehension, and translating text. The intersection of AI and GIS is creating massive opportunities that weren't possible before. AI, machine learning, and deep learning are helping us make the world better by helping, for example, to increase crop yield through precision agriculture, fight crime by deploying predictive policing models, and predict when the next big storm will hit and being better equipped to handle it. Broadly speaking, AI is the ability of computers to perform a task that typically requires some level of human intelligence. Machine learning is one type of engine that makes this possible.

Convolutional neural networks (CNNs) are a type of neural network most often used for image recognition and classification. CNNs excel at these tasks because they are designed to automatically learn how to recognize spatial hierarchies in an image. Once these algorithms are trained, they can'infer' the next best prediction for the task at hand. Beyond basic image recognition, CNNs are used for many different real world applications. We're beginning to see applications like China's TikTok that present AI as a product to consumers, where a recommendation engine infers which 60-second video they'd like to see next (with next to zero human guidance telling the app what to do).

In January of this year, Wired Magazine published an article about a collaboration between The Department of Veterans Affairs (VA) and Google parent Alphabet's DeepMind unit to create software powered by artificial intelligence that attempts to predict which patients in the intensive care unit (ICU) are likely to develop acute kidney injury (AKI). The article stated that more than 50% of adults admitted to an ICU end up getting AKI, which is life-threatening. According to the article, the Department of Veterans Affairs contributed 700,000 medical records to the project. The goal of the project was to test whether artificial intelligence could be developed to help doctors better predict which patients were at risk for developing AKI so preventative measures could be taken sooner. It looks like artificial intelligence may in fact be able to help doctors identify which patients in the ICU are at serious risk of AKI.

Are you ready to take that next big step in your machine learning journey? Working on toy datasets and using popular data science libraries and frameworks is a good start. But if you truly want to stand out from the competition, you need to take a leap and differentiate yourself. A brilliant way to do this is to do a project on the latest breakthroughs in data science. Want to become a Computer Vision expert?

Computer chips are usually small. The processor that powers the latest iPhones and iPads is smaller than a fingernail; even the beefy devices used in cloud servers aren't much bigger than a postage stamp. Then there's this new chip from a startup called Cerebras: It's bigger than an iPad all by itself. The silicon monster is almost 22 centimeters--roughly 9 inches--on each side, making it likely the largest computer chip ever, and a monument to the tech industry's hopes for artificial intelligence. Cerebras plans to offer it to tech companies trying to build smarter AI more quickly.

KYOTO – A team including Kyoto University researchers has said it succeeded in automatically recognizing the faces of wild chimpanzees with an accuracy of over 90 percent using an artificial intelligence system. Kyoto University conducts research on wild chimpanzees in a field site in Guinea in West Africa set up in 1976. Using the AI system's deep-learning methods, the University of Oxford analyzed a total of 50 hours of footage recorded between 2000 and 2013 by three cameras at the field site on the top of a small mountain. The system detected some 10 million images of chimpanzee faces from the footage, according to the team, whose study is to be published Thursday on an electronic edition of the U.S. journal Science Advances. The system improved its face-recognition ability as time advanced, recognizing the identities of 23 individuals with an accuracy of 92.5 percent.