Tag Archives: Powered by TensorFlow

The creative coder adding color to machine learning

Machine learning is already revolutionizing the way we solve problems across almost every industry and walk of life, from photo organization to cancer detection and flood prediction. But outside the tech world, most people don’t know what an algorithm is or how it works, let alone how they might start training one of their own.

Parisian coder Emil Wallner wants to change that. Passionate about making machine learning easier to get into, he came up with an idea that fused his fascination with machine learning with a love of art. He built a simple, playful program that learns how to add color to black-and-white photos.

Emil ML

Emil used TensorFlow, Google’s open-source machine learning platform, to build the simplest algorithm he could, forcing himself to simplify it until it was less than 100 lines of code.

The algorithm is programmed to study millions of color photos and use them to learn what color the objects of the world should be. It then hunts for similar patterns in a black-and-white photo. Over time, it learns that a black-and-white object shaped like a goldfish should very likely be gold.

The more distinctive the object, the easier the task. For example, bananas are easy because they’re almost always yellow and have a unique shape. Moons and planets can be more confusing because of similarities they share with each other, such as their shape and dark surroundings. In these instances, just like a child learning about the world for the first time, the algorithm needs a little more information and training.

ML banana moon

Emil’s algorithm brings the machine learning process to life in a way that makes it fun and visual. It helps us to understand what machines find easy, what they find tricky and how tweaks to the code or dataset affect results.

Thousands of budding coders and artists have now downloaded Emil’s code and are using it to understand the fundamentals of machine learning, without feeling like they’re in a classroom.

“Even the mistakes are beautiful, so it’s a satisfying algorithm to learn with,” Emil says.

When Iowa’s snow piles up, TensorFlow can keep roads safe

Iowa may be heaven, but it’s a snowy one. With an average of around 33 inches of snow every year, keeping roads open and safe is an important challenge. Car accidents tend to spike during the winter months each year in Iowa, as do costly delays. And dangerous commutes can mean hazards for people and commerce alike: the state is one of the country’s largest producers of agricultural output, and much of that is moved on roads.

To improve road safety and efficiency, the Iowa Department of Transportation has teamed up with researchers at Iowa State University to use machine learning, including our TensorFlow framework, to provide insights into traffic behavior. Iowa State’s technology helps analyze the visual data gathered from stationary cameras and cameras mounted on snow plows. They also capture traffic information using radar detectors. Machine learning transforms that data into conclusions about road conditions, like identifying congestion and getting first responders to the scenes of accidents faster..

This is just one recent example of TensorFlow being used to make drivers’ lives easier across the United States. In California, snow may not be an issue, but traffic certainly is, and college students there used TensorFlow to identify pot holes and dangerous road cracks in Los Angeles.

Officials in Iowa say machine learning could also be used to predict crash risks and travel speeds, and better understand drivers’ reactions or failures behind the wheel. But that doesn’t mean drivers will be off the hook. Iowa’s transportation and public safety departments constantly spread the same message: when it’s winter, slow down. Add some time onto your daily commute, and don’t use cruise control during a storm. That way, both drivers and state officials can work together to make winter travel less dreary—and a lot safer.

How machine learning can drive change in traffic-packed L.A.

There's nothing quite like driving through Los Angeles on a perfectly sunny day. But for drivers, the beauty of Southern California’s great weather and scenery is ruined by one thing: traffic.

According to a report by INRIX, my hometown is the worst city in the world for traffic, with a record of 102 hours of congestion during peak hours in 2017. My classmate, Ericson Hernandez, comes from New York City, which is ranked third globally for its traffic woes. Together, we decided to use machine learning to figure out the roots of bad traffic, including elements like road damage from potholes and cracks, and make rides around our beautiful cities enjoyable again.

As Ericson and I started studying electrical engineering at Loyola Marymount University, we began to develop an interest in a relatively new topic to the engineering world: machine learning. Our professor, Dr. Lei Huang, encouraged us to pick a project that we were passionate about, and Ericson and I wanted to use technology to tackle problems in the real world—such as helping the communities around us with road development.

This summer, we looked at previous research projects on detecting road cracks, and pondered how we could improve the algorithm and apply it to Los Angeles communities. We decided to use TensorFlow, Google’s open-source machine learning platform, to train a model that could quickly identify potholes and dangerous road cracks from camera footage of L.A. roads.

Students mount their camera before heading out to collect data.

Students mount their camera before heading out to collect data. 

Construction companies and cities could use this technology to identify which roads need fixing the most. With safer driving conditions and efficient road-work repairs, traffic in major cities could dramatically decrease, allowing for people to travel in a quick, safe and enjoyable manner. 

And that way, driving through Los Angeles can be about enjoying the view, not grumbling at the traffic.