There is a lot of writing and talking about artificial intelligence (AI) now. Along with other related concepts such as machine learning and deep learning. Many people tend to misunderstand the terms used in the context of AI, so this article is intended to help you differentiate and better understand the roles that these capabilities can play in IT service management (ITSM).
What is AI?
As the name suggests, it is a human-made intelligence system, the ability of a machine to work and reason like a human.
AI works and behaves like a human. Examples of AI applications include facial recognition, retinal scanning, text translation, and digital biometrics. Now you can see it more and more often – and often not even see it, because it all just works in the background – in our daily life.
What is Machine Learning?
Machine learning is a subset of artificial intelligence. It is the ability of a machine to gain knowledge through data analysis, allowing it to become artificially intelligent through learning.
Machine learning involves the use of algorithms for data collection and training. At the end of the learning phase, the machine analyzes the data and creates a model from which decisions or predictions can be made. For example, you may have received recommendations from Facebook regarding e-commerce sites – this is due to the fact that the data about the sites you visit is analyzed by Facebook in order to understand what kind of advertising you will be interested in.
What is Deep Learning?
Deep learning is a subsection of machine learning, that is, it is also a subsection of AI. Deep learning has similarities to machine learning, but differs in that machine learning needs some supervision when performing its learning tasks, while a deep learning model will perform its task efficiently even without human guidance (deep learning can be supervised, partially supervised, or performed without human intervention).
Differences between artificial intelligence, machine learning and deep learning
Given that these terms and practices are closely related and sometimes difficult to differentiate, I believe that a practical example is the best way to describe the differences. I will use traffic planning as an example.
“If you’ve built an artificial intelligence app that notifies traffic engineers and planners of where major congestion points are in a city, this can help them plan road maintenance, traffic lights, and other factors that will reduce congestion.
The second step will be programming the AI to find and identify certain patterns (patterns) in the data. For example, traffic in some parts of the city is not difficult between 2 pm and 4 pm and is congested in the evening (possibly due to overlapping rush hour and football match). The model generated from this data will provide more information and thus help designers and engineers make plans to avoid traffic congestion on football days. This is a machine learning application.”
Deep learning technologies go beyond simply reading raw data and recognizing patterns. It uses several layers to gradually extract more information from the data, possibly without human intervention.
Why is it important to distinguish between types of AI
The ability to classify the differences between AI, machine learning, and deep learning is critical because it helps to intelligently apply AI solutions at different levels of government and drive business growth.
“For example, if a city is able to conduct a comprehensive analysis of its roads in the next year to find out where the most congestion is occurring and which roads need maintenance, next year the city will be able to predict congestion during peak hours and in other situations. After that, the city will have the opportunity to inform passengers about the places of congestion and offer them alternative routes. By the third year, the city will have enough understanding of the situation to make plans for the future, taking into account the size of the population and the increase in traffic volume, infrastructure maintenance plans, and also consider the impact of other factors on road traffic, such as climate change.”