What is Artificial Intelligence? What is Machine Learning?

by: Peyton Panik : Fleetio

Artificial Intelligence (AI)

(1) Artificial intelligence is the development and use of computer systems and algorithms to perform tasks that typically require human intelligence, such as perceiving, learning, problem-solving and decision-making. At Geotab, AI is used to augment fleet managers’ decision-making capabilities by leveraging predictions to improve efficiency, safety and the overall performance of fleets.


There are many different subfields of AI, with each managing different problems and/or different types of data. The two most common subfields leveraged at Geotab are machine learning and computer vision.

Machine Learning

(2) Machine Learning is a subfield of AI that uses algorithms and statistical models to analyze data and make predictions. These algorithms are designed such that the computer system learns and improves their performance without explicit programming. In turn, these predictions can be used to inform decision-making.


One of the defining features of machine learning is the ability to learn using training data and feedback. Without training data, machine learning models can’t learn and a lack of feedback makes it impossible for such models to improve themselves.


For example, if a fleet manager has access to data on vehicle utilization, maintenance needs and fuel efficiency, they can use this data as training for a machine learning model. This model may then be used to forecast future demand and to optimize routes while minimizing costs. Feeding the actual demand back into the model helps it learn further, allowing it to make better predictions in the future.


Below are some examples of the use of machine learning in telematics:

Vehicle routing and scheduling: Optimize routes and schedules using data on customer demand, vehicle utilization and fuel efficiency.
Predictive maintenance: Predict when maintenance will be needed based on vehicle data and maintenance history. Can also proactively schedule maintenance tasks to reduce downtime.
Fuel efficiency: Identify opportunities to improve fuel efficiency and reduce costs by analyzing data on fuel consumption and driving patterns.
Safety: Improve safety by analyzing data from sensors and vehicle cameras to identify unsafe driving behaviors.

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