How Fleets Are Succeeding with Artificial Intelligence (AI) Today
[This article is an excerpt from the Idelic whitepaper: How Fleets Are Succeeding with Artificial Intelligence (AI) Today.]
Technology in trucking is rapidly transforming every day. Each year, the number of hardware devices in trucks and the software systems that track them continue to grow. As the amount of technology on the road and inside the back office expands, new solutions are being put in place to bridge the gap between raw data and impactful insights. The latest revolution in fleet safety is Artificial Intelligence (AI) and Machine Learning (ML). So what does this mean for fleets?
Artificial Intelligence (AI) Terms
AI is an umbrella term for a computer software’s ability to perceive its environment, attempt to mimic human behavior and learn to accomplish a given goal. The idea of machines capable of thinking and learning for themselves?has been a popular notion amongst computer scientists since the 1950s, but the way that it has impacted the world has been more subtle.
Because AI is such a broad term,?many companies like to oversell their solution’s AI capability, when in reality their technology is little more than a particularly complicated algorithm. Sometimes, methods of AI are often no more than traditional systems that use simple equations, if-then rules, and decision trees.?
At its best, however, AI can be a highly technical and powerful tool for analyzing and gaining insights from vast amounts of data. The ways in which AI is being applied are abundant, but?ML, the foundation of advanced AI, is profoundly impacting the way that fleet professionals perform their day-to-day operations.
Machine Learning
In the last decade, enormous progress in AI has evolved into a more narrowly defined subfield called Machine Learning (ML).?ML is a type of analysis that machines with access to vast amounts of data conduct which enables them to learn and identify optimal outcomes, without human help.?While the standard approach to AI focuses on hard-coded algorithms or rule-based systems to mimic human behavior, ML provides a way to move beyond these methods by finding patterns that are difficult for humans to identify and then using those patterns to predict events that haven’t yet occurred.
The real magic behind this technology is the ‘training’ process, where the ML models improve their predictions over time. In the case of transportation safety, ML takes data on driver attributes, behaviors, and safety events and runs hundreds of thousands of simulations,?tweaking the potential impact of each data type in every simulation to find the relative importance for each data point or feature. In the end, the finely tuned and highly complex model can accurately identify at-risk drivers, drivers likely to leave a fleet, and much more. Trying to obtain this level of analysis and accuracy without the use of advanced computing is impossible, which speaks to the real power of ML.
Transfer Learning
Developing a robust ML algorithm is no simple task for data scientists without a reference point from a previous model. Meanwhile, safety managers handle?many?forms of data and require a variety of reports to do their job effectively, which in turn means that numerous different models and processes would be needed to reach an ideal outcome.
Transfer learning is a method used by data scientists to utilize previous sophisticated models as a starting point for new models.?Doing so jump-starts the development process on a new task or problem, which allows ML algorithms to grow exponentially.?
Deep Learning
In trucking, fleets can use data from various systems integrated over time to see what has happened in the past, identify patterns, and predict how and when the next preventable accident will occur. As you continue to add more data, systems, and features to your models, the potential for more accurate predictions increases. However, this will also increase the complexity of the problem and can quickly outpace the abilities of traditional ML. These cases require more robust and sophisticated techniques like deep learning.
One modern deep learning architecture, recurrent neural networks (RNNs), allows deep learning models to directly evaluate a driver’s history of accidents and other events without first requiring data scientists to choose a single way of summarizing that data, such as natural language processing or feature extraction. Another advantage of deep learning is the ability to learn mathematical representations of customer-specific terminology automatically, which are called embeddings.
Predict Accidents with Machine Learning
To learn how to use your fleet data combined with ML technology to see who is at risk for a crash, improve operations, consolidate systems & unlock a better understanding of your drivers, read our full whitepaper, "How Fleets Are Succeeding with Artificial Intelligence (AI) Today."