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May 26, 2020

Machine Learning and AI – Comparison


Edwin Lisowski

CSO & Co-Founder

Reading time:

9 minutes

Last time, we talked about the difference between machine learning and deep learning. Today, we want to tell the difference between machine learning and artificial intelligence. Although to someone who’s in the IT industry, this difference should be pretty obvious, there are many misleading conceptions about machine learning and artificial intelligence going around the Internet. That’s why we decided to tackle this issue and explain what’s the difference between machine learning and AI.

Today, artificial intelligence is one of the many buzzwords that describe the modern IT industry, and, in fact, our whole world. We can see AI almost everywhere:

  • It’s in our mobile devices (Google Assistant, Siri, Alexa)
  • On our TV (Netflix recommendations)
  • And on the Internet (chatbots, product recommendations)

And in the companies as well! Nowadays, intelligent assistants enhance our abilities as humans and professionals. They make us more and more productive, allow us to work in a smarter and faster way–they take the repetitive and administrative tasks off our hands. But what is artificial intelligence all about? And what’s the difference between machine learning and AI?

Actually, if you asked a randomly selected Internet user to explain what AI is, the level of complexity of this question could easily compete with quantum physics. It’s like a black hole–we all think that we understand the concept, but it takes a lot of knowledge to explain them correctly.

So, this is exactly what we are going to do today! We are going to explain what is the difference between machine learning and AI.

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Machine Learning and AI: An Introduction to Artificial Intelligence

First of all, we need to understand what AI is. In fact, it’s an extensive and complex term. In our last article, we told you that it is “commonly used to describe all the technologies, algorithms, and programs capable of working without human assistance.” And it’s a 100% accurate definition. Today, however, let’s take one step forward and say that

AI is a concept to create intelligent machines, algorithms, and applications that can simulate human thinking capability.

As Andrew Moore, Former-Dean of the School of Computer Science at Carnegie Mellon University once said, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.”[1]

As a result, such computers or machines can move and manipulate objects, recognize people, and track their movement, or solve repetitive problems. Sounds simple? Well, it’s not. And, oddly enough, even for companies that claim to work with artificial intelligence!

technology, Machine Learning and AI

Do the startups work or don’t work with AI?

According to the survey conducted by the MMC company, a whopping 40% of the European start-ups that are, in theory, classified as the ‘AI companies’ don’t actually use artificial intelligence! As the report states:

“In 40 percent of cases, we could find no mention of evidence of AI.”

David Kelnar, Head of research, MMC[2]

In other words, companies that people assume are AI companies are, in many, instances not. You might think that’s it’s a minor problem, or that study is outdated. MMC studied over 2,800 AI start-ups in 13 European countries, and the results of this research were revealed in the 1Q of 2019.

No wonder that people misunderstand the AI concept if even companies that claim to deal with AI have difficulties with it!

What is artificial intelligence really?

We ought to begin by saying that AI is a part of computer science. It’s a technology using which we can create intelligent systems that can simulate human intelligence. As a result, these systems can perform many tasks that in the not-too-distant past required human presence.

Based on AI capabilities, this technology can be classified into three types:

  • Weak AI
  • Strong AI
  • Super AI

Currently, we are working with weak AI and strong AI. In the near future, we can expect to see the super AI, for which it is said that it will be more intelligent than humans. It’s also known as artificial superintelligence (ASI) or just superintelligence. The predictions are that even the brightest human minds won’t even come close to the abilities of super AI[3]. But, as we already said, it’s still a thing of the future. The fact is, however, that AI is changing faster than its history can be written, so predictions about its future are unreliable.

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AI use cases

In the previously-mentioned MMC report, we can find out that when companies work with artificial intelligence, they primarily focus on two applications:

  • Chatbots (26% of the verified companies)
  • Fraud detection (21%)

chatbot, phone

Today, AI is used in a multitude of fields and industries. Let’s take a look at some of the most common modern AI applications:

  • Mobile assistants (Siri, Alexa, Google Assistant)
  • Driving assistants (autonomous vehicles, pedestrian detection)
  • Online communication (voice bots, chatbots)
  • Intelligent product recommendations (Netflix, Amazon)
  • E-mail communication (SPAM detection)
  • Search by image (Google Images, Find It On eBay)
  • Augmented Reality (computer-generated images put into a real image)

These are B2C AI use cases, but this technology in more and more common in the B2B environment, especially in:

  • Human Resources (resume scanning)
  • Marketing (ads optimization and web analytics)
  • Data science and business intelligence (enhancement of the decision-making process)
  • Healthcare (medical image analysis, drug development process)
  • Manufacturing (production process optimization)

Before we switch to machine learning, it’s vital, to sum up quickly what we already know about AI:

  1. Artificial intelligence is a technology that allows algorithms and programs to simulate human thinking and perform tasks just like humans.
  2. AI has a very extensive range of possible applications, from internet shopping to manufacturing.
  3. The goal of AI is to improve our work and lives and take simple repetitive tasks off our heads.
  4. On the basis of capabilities, AI can be divided into three types: Weak AI, Strong AI, and Super AI.

The difference between machine learning and AI

Now, you know a lot about artificial intelligence, much more than a typical internet user. Let’s now think about the difference between machine learning and AI. The first thing you need to know is that machine learning is not an entirely separate technology. Actually, it’s a subset of artificial intelligence.

Machine learning enables programs and algorithms to learn from data and experience without human assistance. Hence the name – it’s all about machine(s) learning (themselves)!

Machine learning is used to make predictions or decisions using historical data. This technology uses a massive amount of data so that the machine learning model can learn from it and generate accurate results. ML relies on working with specific datasets by examining and comparing data in order to:

  • Find common patterns
  • Explore correlations
  • Classify elements
  • Make predictions

At this point, we have to state that Machine learning has a limited scope of possible applications. Machine learning is working to create machines and algorithms that can perform only those specific tasks for which they are trained. They are concentrated almost exclusively on predictions and finding patterns. When it comes to other applications, like, for instance, online communication with the customer–you have to opt for other solutions.

So, to make this machine learning question more understandable, let’s use a healthcare example to show you how it works:

If you load a machine learning program with a dataset of medical pictures, let’s say presenting cancer cells, along with their description (you have to indicate which tissue is attacked by cancer and which one is healthy), it will have the capacity to assist in (or even fully optimize!) the data analysis of these pictures. As a result, human physicians can spend much less time on examining medical images, which, in turn, allows them to start treatment significantly quicker.

medical pictures, people

What is machine learning used for?

As you know from our previous text, the machine learning algorithms parse data, learn from it in a similar way humans learn–based on experience (previous knowledge), and then apply what they’ve learned to produce the desired outcome.

Machine learning can be divided into two main categories:

  • Supervised learning
  • Unsupervised learning

Both of these categories are used for different purposes. Supervised machine learning is used when you want to predict or explain the data you possess. The unsupervised machine learning methods find hidden patterns or intestine structures in data. In addition, these techniques can be used for, i.a. customers’ segmentation or product structurization. There’s also reinforcement learning. RL makes a case for itself when you have little or no historical data. The RL algorithms don’t need any information in advance, ergo they learn from data during the process. Reinforcement learning can be used in robotics for industrial automation.

If you’d like to read more about machine learning techniques and methods, that’s the article for you!

Today, machine learning is being used in various places and industries, such as:

  • Online recommendations
  • Search algorithms
  • E-mail spam filtering
  • Facebook auto friend tagging suggestions
  • Image classification
  • Business/market forecasts and predictions
  • Data analysis
  • Fraud detection
  • Video surveillance

Data analysis

Machine learning and AI: summary

It’s time to sum up our today’s comparison. If someone asked you, “What’s the difference between artificial intelligence and machine learning?”

What should your answer be? You ought to say:

  1. Artificial intelligence is a broad subject that comprises all the technologies, algorithms, and programs that are designed to work without human assistance and perform tasks that until recently required human intelligence.
  2. Machine learning is a subset of artificial intelligence. Its scope is limited, as this technology is concentrated almost solely on delivering precise predictions, classifications, patterns, and correlations within data.

If you are interested in deep learning technology as well, we have another fascinating article for you.

Addepto is your best bet when it comes to implementing AI/ML in your company. Drop us a line and let’s chat! We are always happy to guide you through this amazing AI world and show you what great opportunities await you. You’re just a step away from working in a faster, smarter, and more efficient way. Find out how today, with us!

Also check out our machine learning solutions to learn more.


[1] Roberto Iriondo. Machine Learning (ML) vs. Artificial Intelligence (AI) — Crucial Differences. Oct 16, 2018. URL: Accessed May 26, 2020.

[2] James Vincent. Forty percent of ‘AI startups’ in Europe don’t actually use AI, claims report. Mar 5, 2019. URL: Accessed May 26, 2020.

[3] Thinkautomation. Types of AI: distinguishing between weak, strong, and super AI. URL: Accessed May 26, 2020.



Machine Learning

Artificial Intelligence