The terms AI and Machine Learning often get used interchangeably – is this correct or is there a distinction between the two?

Mark Tatarsky, SVP Marketing, FieldAware

It is understandable to interchange these terms because they are very closely related. However, there is a difference between them. Machine Learning is a more complex subset of Artificial Intelligence.

Machine Learning refers to the process of using computer algorithms and programs to decipher and interpret patterns in data that lead to typical/predicted results and improves the accuracy of these “answers” over

time. Artificial Intelligence is the broader process of gathering inputs from various sources and learnings (in some cases using ML) and automatically applying a decision framework around these data inputs to create specific recommendations or decisions that mimic human interactions and decisions.

So, AI is the intelligent framework and “rules” that allow computers to interpret data and take action based on that data, while Machine Learning is the interactive intelligence that helps it make the decision and get better over time. In a basic consumer example, Siri is the AI that interprets voice-commands, translates them into something a computer can understand, and provide answers to the user’s questions.

ML is the complex data analysis and programming algorithms that run in the background, instantaneously scouring massive databases of potential interpretations and extrapolations to the problem and searches for appropriate responses. Based on iterative learning, ML then serves up the most likely answer for the AI program to present back to the user in a human-like manner – in this case, a Siri voice response.

Amit Jain, Senior VP of Product, ServiceMax. 

No, although the terms are increasingly used interchangeably this is not actually correct. Essentially, Machine Learning could be viewed as a subset of Artificial Intelligence – or even the latest iterative of what is in fact a long journey for AI.

Artificial Intelligence is a wider technology that covers the whole gamut of machines that can undertake activities humans would normally do – a really early example of this would be the autopilot on a plane. The autopilot has been given a series of programmed instructions on how to deal with specific scenarios and can hold a steady course by ‘Intelligently’ making adjustments based on the data it is receiving. However, it is still reliant on human programmers to outline the parameters for which is it is to operate within and how it is to behave.

Machine Learning is another application of AI that is centred around the idea that the programming we give the machine allows it to interpret the data and learn for itself.

For example, in the more advanced field service scheduling tools, the algorithm powering the scheduling system will learn from the various data it is fed to constantly improve the way it schedules your field service workforce and as such it actually increases its own efficiency over time.

The third part of this Big Discussion will be published next week, when the pair give advice for businesses who are looking to adopt AI. You can read the first part of the big discussion here.