Magic AI is galloping into the internet of horses arena.
The Seattle-based startup, an angel-funded team of five, has been developing AI for stable managers and riders to monitor the health and security of horses from video feeds.
Image recognition has been a boon to agriculture businesses, including those in the cattle industry. Magic AI corrals algorithms for its AI-powered software to monitor video and help better manage horses, streaming the video to its servers for processing.
Magic AI founder and CEO Alexa Anthony knows the needs of horse owners. The daughter of a horse trainer, she grew up riding in the Seattle area and is a former NCAA national champion in horse jumping.
“If you have a Lamborghini, you have it in a garage with an alarm. Horses are often times in a barn in remote places without any security that they are ok when you are sleeping,” she said.
Magic AI’s StableGuard, a system of cameras that works with a mobile app to keep tabs on horses, provides GPU-driven video monitoring and emergency alerts. StableGuard can be configured to recognize riders and staff of stables as well as to send alerts if strangers enter.
Horse Data Hurdle
Building StableGuard wasn’t easy. The developers at Magic AI initially couldn’t find enough publicly available horse images to adequately train its deep neural networks. They began with MXNet and horse images from the classic ImageNet that proved problematic.
“They actually trained abysmally because the angle of our cameras is overhead, very different than ImageNet,” said Kyle Lampe, vice president of engineering at Magic AI “That really threw off most of the things we used to train.”
The startup enlisted convolutional neural networks and other machine learning and vision techniques as well as some motion analysis, he said.
Magic AI’s developers relied heavily on transfer learning to add to a number of different image classification networks. Lampe said that with enough new data — “terabytes and terabytes” of video images — they were able to successfully build on top of networks that had already been trained and used in competitions.
“When you do transfer learning, you’re putting images in after the fact and it is applying everything that it has learned before,” he said.
Developers at Magic AI relied on GPUs on desktop as well as on AWS to handle the hefty training workloads on the deep neural networks.
Horse Health Results
The original inspiration for Magic AI came when Anthony’s horse died of colic. Colic symptoms are fairly easy to spot — rolling on the ground, kicking at the stomach, pawing on the ground — and can be identified with image classification algorithms.
Today, Magic AI is adding to a growing list of health indicators for customers to track using its StableGuard system. StableGuard enables customers to keep track of how often horses are eating and drinking, on their feet, and whether they are blanketed when it’s cold, offering more ways to support horse wellness.
The company can also alert horse owners to signs that an animal is close to giving birth. “We can see signs that are indicative of birth. And then you can look at the live feed on your phone,” said Anthony.
Magic AI has a pilot customer, Thunderbird Show Park, in British Columbia, Canada. On that site, StableGuard is in 120 horse stalls. It offers the horse-monitoring service for $15 a day to those there for horse-jumping tournaments and other events.
Most of these sites are powered by a GPU on site, sizable hard drive storage and other computing resources to run Magic AI’s service. “I am excited to see how this technology can improve the wellness of animals globally,” said Anthony.