Automation Machine Learning and AI, Oh My!

Let’s talk robots.

The world of freight is generally a world of “if it ain’t broke, don’t fix it.” The industry at large is slow to change and slower still to like it, an attitude that makes the incoming robot apocalypse particularly daunting for shippers, brokers, and carriers alike. In the minefield of tech buzzwords like “AI”, “machine learning”, and “automation,” it’s hard to keep up with the conversation surrounding the future of freight.   

But keep up we must, or we all risk being left behind. Like horse-drawn carriages. Or disco.

While it’s impossible to know for sure which examples of today’s cutting-edge technology will revolutionize logistics, and harder still to cover every single innovation in one blog post, it is possible to learn the basic concepts behind the buzzwords.

AI (Artificial Intelligence)

According to Techopedia, the most basic definition of AI is “an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include speech recognition, learning, planning, and problem solving”

Basically, AI is what happens when a computer can think, learn, and make independent decisions like a human. A very, very smart human.

What could this mean for the future of freight?

True AI, the “I’m sorry Dave, I’m afraid I can’t do that” kind, would be groundbreaking for just about every industry on the planet. Combining the raw computational power of a supercomputer with the complete autonomy of a human being opens all kinds of doors to innovation, and asks a lot of big questions about consciousness, morality, and what it means to be alive.

It also means, with enough data, AI could provide near-perfect shipping time predictions and down-to-the-penny pricing. It could draw up the optimal transport routes for every single plane, train, and automobile in the world. Every haul would have a perfectly timed backhaul, every truck would have a computerized co-pilot anticipating and avoiding delays, and every individual palette could be tracked at every moment.

AI could, in theory, synthesize every piece of data in the world to predict, and shape, the future.

A large, dark lens is centered on a black background, staring into the distance.

Their lenses will see all

Okay, but what does this actually mean for freight?

Not much, at present. AI, despite many claims to the contrary, doesn’t exist. Outside of a few university research labs and Google HQ, true AI research is rare and extremely expensive to conduct. Meaning we’re a long way off from computers that think and feel and independently draw up optimal lanes. There are a lot of logistics platforms and software systems that can complete analysis like a true AI might. For example, drawing on pricing data from an extensive database using specific search terms, or anticipating travel delays due to reported traffic accidents.

But these predictions exist on a much smaller scale than the kind of holistic analysis you might see with an AI, and only then with direct human intervention. AI doesn’t need to be coded to interpret freight data, but consumer apps and shipping software do.

“Simple AI” like the virtual assistants Siri and Alexa are about as close as the general public can get to true AI, and neither Alexa nor Siri are capable of conversation beyond making pre-programmed jokes and offering instance-specific responses.

All told, if we’re being pedantic, true AI in Freight isn’t a thing. Yet.

ML (Machine Learning)

First things first – Machine Learning (ML) is not the same as AI. It’s under the umbrella of AI, but it’s a distinct subset that warrants its own discussion.

A diagram explaining machine learning an AI. A large circle is labeled "AI" and contains a smaller circle labeled "Machine Learning"

The definition of Machine Learning, according to TechTarget, is “[building] algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.”

Chances are good if you’ve ever used a website’s “chat support” feature you’ve interacted with a bot using Machine Learning. These robotic assistants are programmed with basic commands, like “if asked for prices on dish soap, redirect to the dish soap page” but take that programming a step further with ML to analyze thousands of user interactions and find the best way to get from user question to bot answer.

Another more fun (and terrifying) example of Machine Learning is Google DeepMind’s AlphaGo, a computer program that defeated Lee Sedol (the Go world champion) back in 2016.

AlphaGo didn’t master Go by analyzing the moves of top players, like IBM’s Deep Blue did to defeat chess world champion Garry Kasparov in 1997. Instead, it mastered the game by practicing against itself millions of times to learn, improve, and eventually, win.

What does this mean for freight?

Unlike true AI, which exists solely in the realm of science fiction, Machine Learning is not only available, it’s practical.

Machine Learning is already helping shippers make better choices in moving their freight. As the “machine” of a TMS continually gathers and analyzes millions of data points, it starts to make more frequent and relevant predictions to solve problems before they happen.

A good example of ML in Freight is lane planning.  Historically, computers would evaluate a handful of data points then spit out a recommendation based on a limited number of inputs. Now, with ML, some companies can dynamically analyze attributes like weather or traffic and learn to recognize patterns humans are unable to see.

Overall, ML is helping shippers, brokers, carriers and other freight industry professionals reduce risk, improve routes, and learn new lanes faster than ever before. With Machine Learning you can optimize a lane in no time.  What used to take six months, now takes minutes.

Automation

Automation is a little different than AI and Machine Learning because automation is the product of both. Returning to Techopedia, the definition of automation is, “the creation of technology and its application in order to control and monitor the production and delivery of various goods and services. It performs tasks that were previously performed by humans.”

That last line, the whole “performing tasks that were previously performed by humans” part, is why automation is both the most exciting, and terrifying, piece of the future tech puzzle. Automation is meant to do the jobs humans are already doing, and that sounds like a raw deal if you’re one of the humans.

A large, industrial robot packs a box of retail goods on an automated assembly line.

What does this mean for freight?

A lot. And not much. Automation is already in full swing, and only promises to absorb even more of the human element within the logistics space. But that’s not particularly new or worrying when most brokers, shippers, and carriers are already using automation in their day to day. Computers have already “replaced” the jobs of switchboard operators, human computers, and typists, improving efficiency and productivity for all sides of the freight equation and creating new jobs like IT specialist, programmer, and data scientist.

Which brings us to the reason you’re here: the robots.

Warehouses are some of the earliest adopters of advanced robotics, using Automated Storage and Retrieval Systems, Automated Guided Carts, and even Autonomous Mobile Robots to locate, track, and sort inventory. In fact, robots are so good at warehouse management that Tractica Research estimates the global sales of warehousing and logistics robots will hit $22.4 billion by the end of 2021.  

That’s cool, how do I start using automation?

While the idea of a fully automated warehouse staffed exclusively by robots straight out of Wall-E is very cool, automation doesn’t need to be so dramatic to be effective.

If your goal is to create a more efficient shipping process, there are already automated platforms that allow you to move freight without the necessary legwork of a traditional brokerage.

For example, SCOUT by Forager is an automated, instant pricing and booking platform that allows shippers to move cross-border freight and check in on their loads without constantly calling a broker and waiting for hours or days for a quote.

It’s just another example of how technology is improving the freight industry and improving the customer experience.  But maybe we’re a little biased.

The bottom line is that technology and automation are changing the face of the world as we know it, and that includes the freight and logistics industry.  Machine learning pricing, Next-Gen GPS systems, and automatic cross-border pricing platforms like SCOUT mean the future isn’t coming, it’s already here.

Long live the robots.