When you hear the words "artificial intelligence," you may think of the 2001 Steven Spielberg movie "AI" about a robotic boy, or the 2013 Spike Jonze film "Her," where the protagonist falls in love with his AI personal assistant. But artificial intelligence isn't just a science-fiction movie concept anymore. In fact it and related technologies are getting ready to play a larger role in trucking.

For our March issue, I've been working on the third part in a series on data analytics, focusing on emerging and future technologies, including predictive analytics, blockchain, edge analytics, and more.

While researching these emerging technologies, I kept hearing terms such as deep learning, machine learning, and artificial intelligence. What do those mean, and what do they mean for trucking?

Deep learning and trucking

Tim Leonard, chief technology officer and executive vice president at TMW Systems, says deep learning is “one of the cornerstones that will take us to the future,” giving us “the ability to dive into masses of volumes of data and pick out anomalies we’ve never seen before,” leading to the development of new business intelligence algorithms. In deep learning, he says, algorithms “go down deep and interact with huge volumes of data.”

For instance, he says, for 12 years Trimble (TMW’s parent company) has been building a market rate index. “Inside that data is driver data,  every dispatch, every invoice,” and so on, he says. Cross-indexing that data with news and social media data allows for actionable insights into data patterns around driver recruiting and retention.

“We’re seeing for example that older generations, 40 and up, tend to stay with trucking companies that offer better benefits and long-term retirement packages," Leonard explains. “Some newer drivers are looking for higher pay.”

Machine learning and AI

Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning has given us self-driving cars, practical speech recognition, effective web search, and more.

A related term, artificial intelligence, revolves around “intelligent agents,” devices that perceive their environment and take actions that maximize the chance of success at some goal. Colloquially, the term AI is applied when a machine mimics “cognitive” functions that humans associate with human minds, such as learning and problem solving. It’s a term that tends to change definition along with advancements in the field.

Brad Taylor, Omnitracs vice president of data/Internet of Things, says some of the cognitive algorithms already in use in time-of-delivery, navigation, and routing software are “sort of on the low end of artificial intelligence.”

For instance, he explains, the cognitive algorithm’s decision-making process is a lot like what the driver would have done before navigation systems were invented. And they can “learn,” he points out. “It’s not a true kind of self consciousness, AI, but the idea is that if you give me an updated set of data, I can use it in my updated calculations like a human.”

Up-and-coming “Uber for trucking” technologies also use AI. For instance, Loadsmart executives believe that the future lies in deep neural networks that allow any platform to go beyond pricing and sourcing.

“Loadsmart relies on machine learning processes to enable shippers to instantly book a truck nationwide, and complex algorithms to, after shipper booking, identify the best truck positioned to move each load,” the company noted in a recent press release. “As more data becomes available and volume increases, deep neural networks will claim a bigger part of the success on the right pricing and sourcing computational-processes.”

More importantly, it says, these neuron-mimicking flows will also change repetitive human responsibilities to computer-generated tasks, without the need for task-specific programming.

Now my head is spinning

But you don’t have to understand deep neural networks, machine learning, artificial intelligence, blockchain, or other technical terms to benefit from them, says Eric Witty, vice president of product management at PeopleNet.

“What it’s really all about is, I don’t have to know everything and do everything. I have to tell you what it is I’m trying to figure out and the problems I'm trying to solve, and the machine will figure it out and be able to provide guidance and the answer to the questions that you need, in near real time.”