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Difference between Artificial Intelligence, Machine Learning and Deep Learning

Difference between Artificial Intelligence, Machine Learning and Deep Learning

The rise of autonomous vehicles, natural language processing, predictive maintenance, and even chess robots has come with its own jargon. The terms artificial intelligence, machine learning, and deep learning are fairly familiar to people working in the field services sector. These technologies make field service automation possible. They power field service software. But what exactly distinguishes one from the other?

The Sky is the Limit

Think of the evolution of artificial intelligence as an umbrella. Machine learning and deep learning both fall under the umbrella of artificial intelligence. And in that order. Without AI there would be no machine learning. And machine learning has given birth to deep learning. However, it might be most logical to turn that umbrella upside down, because with deep learning, the sky is the limit.

One easy way to understand the difference between these three types of intelligence and learning is to draw a parallel to the age-old and very familiar analog training and education process.

➔ Artificial intelligence is like teaching a student directly the information that you want them to learn.
➔ Machine intelligence is like giving a student a book and allowing them to learn and process the information on their own.
➔ The process of deep learning is the same as machine learning, except in this case the student is capable of learning from mistakes made and constantly improving.

The students in the case of AI, ML, and DL are machines. And the books are data. An endless flow of data that is either fed to the machine, in the case of AI, or that the machine retrieves from external sources like the Internet, sensors, etc. in the case of ML and DL. Here is a more concrete explanation of each of the three.


Artificial Intelligence (AI)

AI is used to denote machines that imitate human cognitive abilities like problem solving and learning or other skills that necessitate language and speech and strategic thinking. AI applications make it possible for machines to perform certain human tasks with the same skill level or better. And the era of big data is making AI ever more crucial. With an infinite number of data points and constant generation of new data, it will soon be impossible for the human mind to sift, sort, analyze, assess and arrive at a logical conclusion. And this is in regards to mundane tasks, like scheduling appointments, detecting software errors or machine malfunctions, and managing a gig economy workforce. In fact, many successful field service providers are already relying heavily on AI to effectively navigate these tasks. 


Machine Learning

Machine learning is the next logical step in AI. Two realizations propelled ML forward: the idea that machines could learn how to learn and the Internet. Teaching machines is a cumbersome job. However, providing them access to the endless source of data that is the Internet so they can learn for themselves has opened up immeasurable opportunities. Like deep learning.


Deep Learning

Deep learning differs from machine learning in that machines are capable of learning beyond the data that is available to them. It involves the ability to analyze and assess information to make logical conclusions, determine solutions, and learn from errors. So the more data a machines receives, the more it is capable of learning and the smarter it gets. And though the artificial neural networks responsible for this technology have been around since the 1950s, extensive developments in the last decade have starkly improved the learning curve. The most common current applications are voice and image recognition. However, the level of data analysis possible will make many predictive applications possible. This includes anything from massive improvements in predictive maintenance and safer autonomous vehicles, to predicting illnesses or recidivism.


Where to Now?

Looking at the strides made from the inception of AI in the 1950s to today, we can track an obvious surge in developments and applications. More has occurred in the past ten years than in the 50 preceding them. As more and more businesses embrace the digital transformation and switch to automated processes, we will see even more innovation in this field to meet a growing demand. The possibilities are endless.

For more information on current Artificial Intelligence applications in field services, read our free white paper:

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Author: Leo Andrade, Product Owner, Coresystems

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