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Distinguishing Between AI, Machine Learning, and Natural Language Processing

August 20, 2019 by Bisma Hashmi

As the reality of a fourth industrial revolution becomes more certain, you probably come across the terms “artificial intelligence” and “machine learning” more often than you would expect to, however, surprisingly enough, not many of us really understand the difference between them and their relation to one another. When you understand fully what these terms mean, it becomes easier to appreciate their practical implications.

AI is helping to improve the electrical engineering industry in various ways. Interestingly enough, there are many uses for AI and ML in power systems, primarily for the distribution of electric power system automation and manual control devices. These include devices such as relays, circuit breakers, and switches to name a few. 

However, before we dig deeper into the applications of artificial intelligence, machine learning, and natural language processing, let’s first look at exactly what these are. 

Artificial Intelligence 

In general terms, artificial intelligence is the wider concept of programming machines to perform tasks in an intelligent manner. When a machine carries out a set of tasks that a human assigns to it, based on specific rules that solve problems (algorithms), it is known as ‘intelligent’ behavior. Thus, referred to as artificial intelligence’. 

Artificial intelligence has several branches or subfields, such as machine learning, natural language processing, neural networks, and deep learning, etc. 


Applications of AI in the Electrical Engineering Industry

Artificial intelligence is being used in almost every sector, whether it’s entertainment, technology or the EE industry. There are numerous uses of AI for electrical engineers. 


Fuzzy Logic

Fuzzy logic is an approach to computing, based on the idea that there are ‘degrees of truth’ instead of the conventional boolean logic of 1 (true) and 0 (false). 

A common use of AI is for systems that use fuzzy logic. It facilitates the modern industrial control automation systems. An example of this is AI-based fuzzy logic that is used in automotive systems for traffic and speed control. 


graphic showing the relation between Artificial Intelligence, Machine Learning, and Natural Language Processing

A pictorial depiction of the relation between AI and its two prominent subfields, machine learning, and natural language processing. 


Power Systems

A network of electrical components used to supply, transfer, and use electrical energy are categorized as power systems. Artificial intelligence techniques have various applications in power systems, such as assisting in fault detection and frequency monitoring.  This means that a power plant is controlled and maintained from a distance by deploying various artificial intelligence methods such as natural language processing and machine learning. 


We’ll go in more detail about how they are used in a while. But first, we’ll take a look at the relation between machine learning and natural language processing.

Machine Learning

As we already know, machine learning is a sub-field of AI. It is when you give machines access to data and let them learn on their own so they can perform intelligent tasks. 


How Does It Work? 

Machine learning is probably already a part of your life in more ways than you know of. For instance, when you watch a couple of shows on Netflix, it keeps track of them. Netflix then uses machine learning to suggest new shows based on your previous choices. 


How Can Electrical Engineers Use Machine Learning?

Machine learning is used in several EE disciplines. One of the more prominent applications of machine learning for EE is for turbo decoding. There is a sub-field of machine learning called deep learning–it learns from large amounts of data and performs a task repeatedly while improving results each time. This works wonderfully for deep learning-based channel decoding. A decoding algorithm is fed to the decoders. Machine learning is used to perform the same task repeatedly and eventually improve results. 

Another very useful application of machine learning is for the discipline of statistical signal processing. Background noise disrupts communication and hinders understanding and recognition of speech. To enhance the quality of speech, speech enhancement algorithms are used. Deep neural networks, which are a branch of machine learning, are employed in statistical signal processing


Image courtesy of Pixabay.

A graphic that depicts a human mind being mapped through machine learning applications. Image courtesy of Pixabay.


Natural Language Processing (NLP)

Natural language processing is a branch of artificial intelligence that deals with communication between computers and humans. 

If AI is a building system that can perform intelligent things, natural language processing is a building system that understands human language. It is related to machine learning because natural language processing uses machine learning to make sense of human language. 


How does NLP work?

Natural language processing takes large amounts of unstructured data and obtains meaningful information from it to give results. Unstructured data can be human speech, social media posts, text messages, etc. 


What Are the Applications of NLP?

Some of the everyday uses of NLP include:

  • Applications such as Google Translate that use text analytics to offer language translation.

  • Call centers where interactive voice response (IVR) technology is used to respond to callers’ requests.

  • Social media monitoring.

  • Applications such as Google Assistant. 


AI is growing fast. With new research efforts being carried out every day, the applications of AI for electrical engineering are also increasing. Let us know, are there other ways in which electrical engineers can benefit from AI? 

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