IoT Sensor Data Fueling Interest in AI
Tractica’s Kirkpatrick said the evolution toward autonomous vehicles in the logistics sector provides one example of how much an AI system needs to learn. “If you were to think about how truck drivers make decisions on the road, they have a lot of experience driving, so they know when cars cut in front of them how to react. They also know that if they see on Google Maps that there is congestion ahead, they will want to find an alternate route. But, they also have learned that you can’t take trucks on certain side streets because of weight limits. So, there is a huge knowledge repository of rules of the road.”
In addition, when trucks arrive at their destinations on schedule, it helps to keep entire supply chains on schedule. There might even be a financial benefit to the truck driver for arriving ahead of schedule. Part of feeding AI algorithms the data the need is also providing it with rules to follow, and tweaking the algorithm when the AI needs to value a specific rule more highly than a specific desired outcome, Kirkpatrick said.
Human involvement in training and fine-tuning AI algorithms allows companies to make sure unintended results don’t occur. “From taking in years of data on how to get to a particular destination, the algorithm, if unsupervised, may pick up on the idea that it can get the truck to its destination more quickly if it speeds or breaks traffic laws,” Kirkpatrick said. “You can train the algorithm to discard or devalue certain pieces of data in the aiming of meeting its goals.”
While companies undertaking AI projects may be tempted to train their AI systems on their own, Tractica’s Kirkpatrick said it is likely that most firms in specific vertical industries like transportation and logistics will need help from AI specialists.
“Unlike many other technologies, AI needs constant care to provide full value. As such it is very much not a technology you just take out of a box and then forget about,” said Alexander Hoffman, managing director and co-founder of TNX Logistics, which provides an AI platform for third-party logistics companies that helps dispatchers strategically tender their transport jobs to trucking firms. For example, its software-as-a-service is used by New Zealand logistics firm Coda Group to plan jobs across 66 carriers.
“It’s not as if AI needs to rediscover the basics of the transport industry by itself,” Hoffman said. “Humans have vast experience in the industry and have discovered so much more than just a basic understanding of it. The transformative power of AI is to take the very best of that knowledge, insight, and intuition, and make it scalable.”
As AI algorithms become more adept at executing targeted applications, the need for a human touch in training them may lessen, and machine learning — the concept of the machine teaching itself —can take over. For now, some transportation and logistics companies may be uncomfortable with the notion of their AI-enabled systems learning on their own, and eventually making their own decisions, but doing so may help maximize their value to a particular operation.