By: Warren Powell - Co-Founder, Optimal Dynamics; Professor Emeritus, Princeton University; Executive-in-Residence, Rutgers Business School
Artificial intelligence has become one of the most buzzworthy topics in business circles and with good reason. PwC’s Global Artificial Intelligence Study indicates that AI could make a $15.7 trillion contribution to the global economy by 2030 and that local economies will experience a 26% boost in their GDP by the same year.
In the business world, organizations are already using AI to automate processes, analyze data, improve decision-making, and generally streamline operations to save time and money.
But what about the trucking and transportation industries? Trucks move about 72.6% of freight in the United States as measured by weight. The trucking industry employs 8.4 million people and contributes more than $792 billion to the U.S. GDP. It stands to reason that AI could be an accelerator for the industry, unlocking benefits and helping streamline trucking activity.
I’ve been researching and working on solving complex challenges with sophisticated math toolsets in the trucking and transportation space for 40-plus years, first as a professor and lecturer at Princeton, the Massachusetts Institute of Technology, and Rutgers, and now as a co-founder of Optimal Dynamics.
The application of AI in the trucking and transportation industry has evolved over time, and Optimal Dynamics has played a pioneering role by creating real-world applications for AI within the transportation sector. This article explores what I know of AI’s evolution from simple, rule-based logic to sophisticated pattern recognition, machine learning, and decision-making. It also highlights how Optimal Dynamics has played a leadership role in the application of AI for trucking and transportation companies — and how AI works in our platform today.
The trucking and transportation industry is currently operating in what’s known as “Freight 4.0.” Freight 4.0 follows Freight 1.0 (the use of water and steam to transport goods), 2.0 (the technical revolution powered by electricity and machines), and 3.0 (the digital revolution that started with computers in the 1970s).
Freight 4.0 builds on the age of the computer by incorporating:
There’s some overlap between the different eras of freight. For example, research and work on AI dates back to the 1960s and 1970s, a time when Freight 3.0 was still just emerging. Below, explore the evolution of AI from that starting point in the mid-20th century through where AI stands today.
This is my own classification of the levels of AI, and it is subject to updating and change. I first wrote about these levels of AI on LinkedIn, and then I provided a brief update just a few months later. Here’s a look at my thinking on the different levels of AI, where we are right now, and where we may go in the future.
There’s nothing new about the concept of AI. In the 1960s and 1970s, AI was going to take over the world (according to experts, the media, etc.). This first iteration of AI represented simple behaviors and was built on what I think of as “rule-based learning.” That initial iteration of AI did not indeed take over the world, but rule-based logic remains an important component of how AI is used in the 21st century.
The second, third, and fourth levels of AI can all be lumped into a category I think of as “supervised machine learning.” Why separate these seemingly similar concepts into different levels? Because they are each unique and emerged at different points in history. Here’s a timeline:
Levels 2–4 are almost like cousins — they are part of the same machine-learning family but unique enough in time and capabilities to be categorized separately.
Level 5 is classic deterministic optimization, which means specifying a model of a physical problem and then using powerful algorithms to identify the optimal (or near-optimal) solutions. These models require a great deal of work, and they can only solve a single problem or a narrow class of problems with the same structure. That said, Level 5 will always outperform what a human can accomplish alone.
Level 6 is also in the optimization family alongside Level 5, but it applies to sequential decision problems that represent a much broader class of problems. We must specify the problem’s physics or dynamics for the AI to perform. The AI can then automate any process for which it has been properly engineered.
This is a huge step toward true intelligence. A computer can outperform a solution most of the time, but that outcome is not guaranteed.
I call Level 7 the “Finger of God” because it refers to AI’s ability to replace people, cure cancer, and possibly launch attacks on society. Think about truly complex problems like managing climate change or responding to a pandemic. The solutions to these problems demand real creativity.
Today, humans must specify the types of decisions that can be made in order for a computer to select the best decision. For example, asking a robot to explore its environment is ineffective if it does not know it’s inside an office, on a street corner, or in the middle of a desert.
I’m skeptical that computers will ever reach Level 7. There’s no economic need for them to develop these skills, and computers cannot develop these advanced skills on their own. In the future, people will most likely provide the advanced capabilities that computers will always lack, but they will partner with AI tools to solve complex problems that are beyond human knowledge and capabilities.
We’ve come a long way from the simple, rule-based systems that characterized the early days of AI in the trucking and transportation industry. And what’s mentioned above is only scratching the surface of how AI is currently being used in this space.
At Optimal Dynamics, we maximize the application of AI in its most advanced form to solve real-world problems for trucking and transportation companies, everything from planning around unknown variables and complex business needs to dynamic automation for execution (including building ideal tours and making the best dispatch decisions) to identifying and bidding on the lanes that are uniquely valuable to your business.
You can experience how AI is transforming this industry right now. Schedule an Optimal Dynamics demo to see firsthand how AI can impact your organization’s operations by maximizing performance and profitability.
About the Author
Warren Powell is Co-Founder and Chief Innovation Officer at Optimal Dynamics, Executive-in-Residence at Rutgers Business School, and Professor Emeritus at Princeton University, where he taught for 39 years. Warren also served as the founder and director of CASTLE Lab, which focused on stochastic optimization with applications to freight transportation, energy systems, health, e-commerce, finance, and laboratory sciences. His work includes the development of a universal framework that can be used to model any sequential decision problem, including the identification of four classes of policies that span every possible method for making decisions.