This article was originally published by Datamation.

Technology solution providers frequently list artificial intelligence (AI) and machine learning (ML) products under one category of AI/ML. 

The combination of these two tech trends has gained significant buzz in the business world, but AI and ML aren’t the same thing: machine learning is a subset of artificial intelligence. 

Bartowsz Wojtowicz, a machine learning engineer at Netguru, a machine learning and digital transformation company, offered further distinction:

“AI describes a general concept of creating machines, simulating human cognitive capabilities — such as learning and problem solving,” Wojtowicz said. “Whereas, machine learning is currently the most promising and most widely used application of AI, which allows computers to learn and improve based on experience.”

Read on to learn about artificial intelligence vs. machine learning and how these technologies work together to create business innovations:

Machine Learning Today

Machine learning, or training computer algorithms to recognize data sets and perform certain tasks based on that data, has become an integral part of new technology developments. 

Machine learning is a part of AI that has many practical applications for using data at scale. Because machine learning involves developing an algorithm that focuses on completing one data-based task at a time, data scientists and ML specialists can program and perfect these solutions for a singular task over time.

Machine learning, on its own, commands a growing global market, as it reached an estimated value of $1.41 billion in 2020 and is projected to hit $8.81 billion by 2025, according to 360 Research Reports

Applications Of Machine Learning

  • Deep learning: This type of ML includes neural networks that cause it to function like a human brain, which makes it possible for deep learning models to closely copy human behaviors for assigned tasks. Some common examples include chatbots and virtual assistants.
  • MLOps and automation: Machine learning models are often trained to automate back-office tasks that require less specialized human skills or can benefit from machine support. This process, most commonly known as MLOps or AIOps, automates functions like security monitoring, network audits, and network self-healing efforts.
  • Smarter data analytics: The most widely used application of ML is found in data mining and data analytics. Once ML models are trained to comb through big data sets, they can not only move through data at a faster rate than humans, they can also deliver deeper insights and typically avoid user error problems.

More on machine learning: Machine Learning Market

Artificial Intelligence Today

Artificial intelligence is the greater umbrella term for algorithm-driven technology designed to simulate human actions and intelligence. 

Machine learning, deep learning, and natural language processing (NLP) are subcategories that fall under AI. 

The AI software market is one of the fastest-growing tech markets in the world, reaching an estimated value of about $62.3 billion in 2020 and predicted to reach around $997.8 billion by 2028, according to Grand View Research

The majority of AI today is considered weak AI or artificial intelligence that can simulate aspects of human behavior and intelligence. Some of the more specialized tech firms are working on strong AI development or robotic solutions that can think and act independently once programmed.

Applications Of Artificial Intelligence

  • Natural language processing: A commonly used form of AI, NLP analyzes text, voice, and other communication data in order to make decisions and communicate with users.
  • Computer vision: This type of AI focuses on image analysis, finding meaning, identifying people, and pointing to potential dangers in a given environment. Computer vision is increasingly being used by law enforcement to supplement criminal investigations.
  • Accessibility tools: Most of today’s AI innovations focus on improving accessibility and simplifying daily routines. Some common accessibility improvements that come from AI include autonomous vehicles, medical diagnostic tools, and virtual home assistants.

Learn about other AI use cases here: Artificial Intelligence Market

ML And AI Use Cases

Some of the most powerful applications of smart tools happen when big data-based machine learning insights and task-driven functionality are layered with human-emulating AI development. 

See the following use cases to learn about the advantages of using ML and AI together:

Conversational AI And ML

“Powered by ML and AI, the telecommunications and telephony market has evolved to offer voice, audio, and video-driven networks with capabilities to analyze conversations in real-time and give automated feedback. ML is the engine that dynamically provides insights based on learning/training from massive data input. AI is the intelligence that is provided as a result of the findings from ML. Without ML, there is no AI. Together, both AI and ML technologies work seamlessly to provide stronger, natural experiences between representatives and customers, resulting in more confidence from both parties.” -Greg Armor, EVP at, an AI-powered sales acceleration platform

More on AI and communication: Conversational Artificial Intelligence (AI) Market

Better Business Intelligence (BI)

“In BI tools, you can see AI-driven features as helpers to make humans better at seeing patterns in their data and ML as the technology that finds and surfaces those patterns. In the AutoML tooling market, you are seeing AI-driven features being used to help the citizen data scientists automate data wrangling and select the best model for the task at hand.” -David P. Mariani, founder and CTO at AtScale, a BI and data science software provider  

Fraud And Transaction Monitoring

“Some people think that AI and machine learning are the same things — that cannot be further from the truth. Machine learning is about teaching machines to learn without being explicitly programmed. It allows for automation of repetitive tasks. AI is about providing machines the ability to complete tasks that ordinarily require human insight, such as assessing the nuance of a particular transaction. An effective fraud prevention solution makes accurate real-time decisions about transactions and user behavior — and it cannot do that without machine learning and AI.” -Liron Damri, President and Co-Founder, Forter, a fraud prevention platform.

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