Artificial intelligence (AI) and deep-learning-capable machines are rapidly making their way into the hearts of industries around the world. Day-to-day operations and data collection are among the primary uses of machine learning, but some tech companies are gradually beginning to use this technology as a foundation for their business.

Lincoln, situated in what Hudl co-founder David Graff once referred to as the Silicon Prairie, is home to two such companies that make use of deep learning in their own particular way.

Hudl, the booming sports software and analytics company based in the Haymarket, recently began implementing AI into its analytic techniques.

Ben Cook, the research director at Hudl, said the company began exploring machine learning roughly three years ago.

“It was slow moving at first, but we’ve started to pick up steam,” Cook said.

Hudl has continued to explore AI and machine learning over the years, designating a research and development team specially devoted to the growth of deep-learning applications in sports. According to Cook, while Hudl continues to find different research and development techniques, the technology is already being utilized in the form of computer vision.

Computer vision is one of the leading deep-learning techniques in the world, essentially working by converting digital images into data for a computer to see a relatively accurate representation of the real-life image. The company employs a preliminary AI-based computer vision algorithm to track players’ locations on a professional soccer pitch. This method, according to Cook, is being tested on a select few European teams.

“You know not just where a player is at any given time, but also where he was over the course of several seconds,” Cook said.

The final output, Cook said, gives the analyst a clear view of where each player was during a match, how fast each player was moving, total distance covered and a surplus of other analytical data. This allows for more advanced analytics, such as computing the probability a team will score a goal given the speed and position of each player on the field at any particular time.

“We have a representation of the entire game, which you can use to answer different questions,” Cook said.

Cook said another idea the company is exploring is the development of a deep-learning model that can predict the next second of a video — an AI neural network that learns from visual cues and “hallucinates” a predicted outcome.

Known as a generative adversary network (GAN), this technique uses two different neural networks to produce superficially authentic photographs. Recently, research teams, such as the one at Hudl, have begun using GAN to model patterns of motion in video. From a sports perspective, Cook said this application can be useful for identifying different players and separating them from other objects within a video.

“If you can have the GAN learn on hundreds of hours of video, you can take a much smaller amount of bounding boxes, and it can learn [the players] a lot more quickly,” Cook said.

Ultimately, having an AI that can quickly distinguish individual players from the surroundings and accurately predict their next movements will have game-changing effects on how coaches, players and fans participate in the sport. According to Cook, Hudl continues to test different deep-learning computer vision algorithms for small, selective applications with the goal being to eventually implement these techniques across a wide gamut of sports at every level as technology advances.

“[Computer vision] won’t be the only thing we do, but it’s going to be a more and more important part of what we do,” Cook said.

While Hudl utilizes AI and deep learning to grow an already successful business, another Lincoln tech company uses these computer vision technologies as its foundation and backbone in a different industry.

Ocuvera, also based in the Haymarket, applies deep-learning computer vision to the medical field. The company’s primary goal is to position depth-sensing cameras within hospital rooms to predict when a person will fall out of bed.

Josh Brown-Kramer is an applied mathematician at Ocuvera who primarily works with developing algorithms necessary for computer vision machine-learning cameras. Like every company investing in AI and deep learning, Brown-Kramer said the first step in Ocuvera’s journey was to acquire data. To do this, the company recorded and studied over 100,000 hours of patients in hospital beds to give the algorithms plenty of data for learning and making predictions.

“We developed a lot of tools and techniques to hone in on interesting events,” Brown-Kramer said. “After a year, we had a body of video we were able to look at and conclude that we would be able to provide a large lead time [before falls].”

To do this, Brown-Kramer said these raw signals obtained from video recordings were put into machine-learning algorithms to find answers for a large variety of binary questions.

“We’re taking a bunch of signals that we think are of importance, with regard to the question, ‘Is this person going to be exiting the bed soon?’’” Brown-Kramer said.

Computer vision algorithms search for patterns in movement over time to predict when falls could occur. These include subtle changes in position, whether a person is sitting or lying down, how close they are to the edge of the bed and a multitude of other signals.

While the continued collection of data is paramount to the growing accuracy of fall detection within the algorithms, Brown-Kramer said human intuition can help speed the rate of progress.

“When we see that the [algorithm] makes this mistake, we can fix that now by interjecting our own code,” Brown-Kramer said.

According to Adam Hunke, who works with business operations at Ocuvera, the overall intent of these predictions is to give hospital staff ample time to intervene before a fall ever happens — a tool that can not only save lives, but save hospitals enormous amounts of time and money.

“There’s a lot of ‘fall’ products out there that are reactive, and we wanted to make something that’s just plain better,” Hunke said.

With the technology tested and sound, Hunke said more local, regional and national hospitals are becoming interested in Ocuvera’s cameras and deep-learning algorithms. The goal of the company, Hunke said, has shifted to distributing its product to hospitals and medical care providers who are unaware of the medical and economic benefits AI and deep learning can have. Along with that, Hunke said Ocuvera will continue to improve its learning algorithms to include more applications, such as chair exits, sleep patterns and circadian rhythm patterns.

“There will continually be research and development on the computer vision side to add to its capabilities,” Hunke said.

The state of artificial intelligence and machine learning can be seen here in Lincoln, Nebraska, and as companies such as Hudl and Ocuvera continue to research and develop this burgeoning technology, more people will begin to see its effects on a national and local scale.

But Cook remains cautiously optimistic.

“I think it’s a little overhyped right now, in my opinion,” Cook said. “There’s this sense that AI can solve any problem. There are constraints people are not being totally honest about, but at the same time, I think it is an extremely powerful technique. But the idea of an AI apocalypse is dramatically overblown.”

However, he said he’s excited to see the continued local adoption of AI in recent years in Nebraska.

“There’s no reason Nebraska companies shouldn’t be taking advantage of it,” Cook said.