Machine Learning vs AI: What’s the Difference?

You may have heard the terms AI and machine learning thrown around a lot recently in digital transformation conversations. As closely related words, they’re often used interchangeably, which can be confusing when considering how they actually differ.

Don’t worry if you’re still wondering, ‘What is the difference between AI and machine learning?’ In this post, we’ll cover the machine learning vs AI debate and discuss some use cases of each. 

Machine Learning vs AI: Definitions

Artificial Intelligence

Artificial intelligence, or AI for short, is the simulation of human intelligence processes by machine and computer systems. It can copy cognitive functions and tackle tasks like problem-solving, data analysis, understanding and responding to language and making decisions or recommendations.

AI involves several approaches, including machine learning, neural networks, deep learning, and rule-based AI. Our team at McKenna Consultants can implement these technologies across a wide range of industries and applications, adapting to advancements as they become more popular.

Machine Learning

As a subset of AI, machine learning concentrates on developing algorithms and statistical models that allow computers to learn from, predict from, and make decisions based on data analysis.

Essentially, machine learning allows the machine or system to learn from experience, train on data and improve over time instead of being explicitly programmed (which saves us a job!).

What are the Similarities Between AI and Machine Learning?

Whilst machine learning and AI are not the same, they certainly have a few similarities.

Both AI and machine learning:

  • Mimic human intelligence

Both artificial intelligence and machine learning intend to develop systems that copy human intelligence, including capabilities like problem-solving, making decisions, and learning from data.

  • Apply data-driven approaches

Machine learning concentrates on developing algorithms and trains itself from data. Similarly, many AI applications utilise data-driven approaches to help make predictions and automate tasks based on insightful trends and patterns.

  • Undergo iterative improvement

AI and machine learning algorithms undergo iterative improvement by continuously training themselves on new data and refining their performance.

  • Can be applied across industries

Both AI and machine learning have applications in various industries, including healthcare, transportation, and manufacturing. We will explore these in more detail later in the article.

What is the Difference Between AI and Machine Learning?

Now that you know the similarities and interconnectedness between AI and machine learning, let’s highlight the main differences.

  • Objectives

AI aims to generate systems capable of performing tasks that would usually require human intelligence, from decision-making to language understanding. It strives to copy cognitive and human-like behaviours in machines.

On the other hand, machine learning specifically concentrates on enabling machines to train themselves from data, improving themselves from experience by making better decisions or predictions based on data.

  • Methods

AI uses various methods, from logical reasoning to rules-based systems and machine learning, meaning not all AI is machine learning.

However, all machine learning is AI. It predominantly focuses on statistical procedures to develop accurate predictive models. These models are generated using algorithms that adjust their parameters based on data feedback. Common methods include reinforcement learning and supervised learning.

  • Requirements

AI systems typically require complex rule sets and logical frameworks that copy human behaviours.

In contrast, machine learning mainly relies on large volumes of quality data for accurately training the algorithms. Machine learning also requires considerable computational power for data processing and model training.

Machine Learning vs AI: A Beneficial Collaboration

An intelligent computer utilises artificial intelligence to copy humans and execute tasks by itself. Going further, machine learning is how the computer system trains itself on data to expand its intelligence. When considering the machine learning vs AI difference more, it’s worth seeing how they interact:

  • AI systems are developed utilising machine learning and other techniques
  • Machine learning systems are developed by studying data patterns
  • The machine learning systems are optimised based on data patterns
  • This training is repeated until the machine learning model’s accuracy is adequate for the tasks it is being trained to perform

A machine learning and AI collaboration comes with various benefits and possibilities. This includes:

  • Increased operational efficiency and decreased costs
  • Broader data ranges by analysing more unstructured and structured data sources
  • More informed, quicker decision-making by enhancing data processing, data integrity, and reducing human errors

Benefits of Using Machine Learning and AI

Organisations can use AI and ML in various ways. Below are some capabilities your company could benefit from.

  1. Language understanding

By recognising speech and adopting natural language understanding, computer systems can detect words in spoken natural language and find meaning in them.

This is integral for your company to facilitate digital communications with consumers.

  • Predictions and recommendations

The intelligence capability of predictive analytics can allow your business to accurately identify behavioural patterns, predict trends, and make recommendations based on data.

This can drive better decision-making based on what your company predicts your consumers will want. Don’t forget that quality recommendations can also add value to the customer experience.

  • Media processing

AI makes it possible for machines to recognise components of images and videos, from human faces to specific objects. This means it can also use functionalities such as visual search.

  • Analysing sentiment

We can use AI computer systems for sentiment analysis to gather and categorise positive and negative opinions and neutral ones displayed in textual language.

This can help your company enhance its product offerings by evaluating what does and doesn’t work.

Machine Learning and AI Examples in Industry

These components can be used widely across various industries considering the vast range of machine learning vs AI examples. Incorporating both AI and machine learning capabilities into industry strategies can help your company leverage its time and money more effectively.

Here are some of the most common AI and machine learning examples applied:

  • Manufacturing

We can employ AI and machine learning to streamline productive processes by predicting equipment failures and reducing downtime.

  • Healthcare

Machine learning algorithms can analyse data in the healthcare industry to identify patterns and streamline administrative tasks. At the same time, AI can assist in personalising treatment plans and customer service with patients.

  • Retail

With AI and machine learning, retailers can offer consumers useful product or service recommendations, gain insights into what products work best, and boost customer service communications through chatbots.

  • Sales

In sales and marketing, our team can leverage AI and machine learning in sentiment and predictive analysis to optimise campaigns and provide personalised offers that appeal to the target audience.

  • Transportation

In transportation, AI and machine learning are valuable tools in streamlining companies’ routes and helping in traffic forecasting.

Employ the Benefits of Machine Learning and AI with McKenna Consultants

After reading this post on AI vs machine learning, hopefully, you will have a better idea of the difference between AI and machine learning.

At McKenna Consultants, we can help you use machine learning and AI to improve your current services. We’ve even developed our very own artificial intelligence assistant (also known as Ziggy). If you would like us to create an AI assistant for your platform, help you choose between AI and machine learning, or assist in other web or app development services, please get in touch with us today. Or, take a look at our blog to learn about our other areas of expertise.

Nick McKenna
Since 2004, Nick McKenna, BSc, MBCS Biography has been the CEO of McKenna Consultants. McKenna Consultants is a bespoke software development based in North Yorkshire, specialising in Cloud development, mobile App development, progressive web App development, systems integration and the Internet of Things development. Nick also holds a First Class Degree in Computer Science (BSc) and wrote his first computer program at the age of nine, on a BBC Micro Model B computer. For the last 21 years, Nick has been a professional computer programmer and software architecture. Nick’s technical expertise includes; Net Core, C#, Microsoft Azure, Asp.Net, RESTful web services, eProcurement, Swift, iOS mobile development, Java, Android mobile development, C++, Internet Of Things and more. In addition, Nick is experienced in Agile coaching, training and consultancy, applying modern Agile management techniques to marketing and running McKenna Consultants, as well as the development of software for clients. Nick is a Certified Enterprise Coach (Scrum Alliance), SAFe Program Consultant (SAI), Certified LeSS Practitioner (LeSS) and Certified Scrum@Scale Practitioner. Outside the office, Nick is a professional scuba diver and he holds the rank of Black Belt 5th Dan in Karate.

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