Machine Learning and AI – Comparison

Key Takeaways

• AI is the broader goal — building systems that act intelligently. Machine learning is one approach to AI, where systems learn from data rather than follow hand-written rules.
• Every machine learning system is AI, but not every AI system uses machine learning (rule-based AI still exists).
• Deep learning is a subset of machine learning based on neural networks; generative AI and LLMs are a subset of deep learning focused on producing new content.
• AI ranges from weak (narrow) AI — single-task systems like LLMs and recommender engines — to theoretical strong and super AI, which don’t exist today.
• In business, the right question is rarely “AI or ML?” but “which technique fits this problem, data, and risk profile?” — most production systems combine classical ML and modern generative AI.

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!

“AI” doesn’t always mean AI

Even today, “AI” is often used as a marketing label rather than a technical claim. A widely cited 2019 MMC report found that around 40% of European companies classified as “AI startups” showed no real evidence of using AI — and while the AI landscape has changed dramatically since then, the underlying problem hasn’t gone away.

Today the same dynamic plays out around the words “agentic,” “generative” or “AI-native.” Whether a product truly uses AI in any meaningful way depends not on its branding, but on what’s actually happening under the hood — whether it learns from data, adapts over time, or simply runs a deterministic script with an LLM wrapper.

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%)

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)
• Visual search (Google Lens, Pinterest Lens, e-commerce image search)
• 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.

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 intrinsic structures in data. In addition, these techniques can be used for, e.g., customer 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
• Image classification
• Business/market forecasts and predictions
• Data analysis
• Fraud detection
• Video surveillance

Where does deep learning fit?

The same way machine learning is a subset of AI, deep learning is a subset of machine learning. It’s based on artificial neural networks with multiple hidden layers — hence the word “deep.”

What sets deep learning apart is that, instead of relying on human-designed features, it learns useful representations directly from raw data. This makes it the dominant approach for problems where humans struggle to define rules explicitly: image recognition, speech-to-text, machine translation, and — most recently — generative AI.

A simple mental model:

• AI → the goal: machines that act intelligently
• Machine learning → one way to get there: learn from data
• Deep learning → one type of ML: learn through deep neural networks
• Generative AI / LLMs → one application of deep learning: generate new content

Where Generative AI and LLMs Fit In?

Since this article was originally written, one branch of AI has reshaped public perception of what artificial intelligence can do: generative AI. Built on a class of deep learning models called transformers (introduced in 2017), generative AI produces new content — text, images, code, audio — rather than only classifying or predicting.

The most visible examples are large language models (LLMs) like GPT, Claude, Gemini, and open-source alternatives such as Llama and Mistral. They power tools millions of people now use daily: ChatGPT, Microsoft Copilot, Google’s Gemini, and countless enterprise applications.
So how does this fit our AI vs ML picture?

• AI is still the overall umbrella.
• Machine learning is still the subset of AI that learns from data.
• Deep learning is a further subset of ML based on neural networks.
• Generative AI and LLMs sit inside deep learning — they’re a specific application of ML that focuses on generating new content rather than classifying or predicting.

In short, generative AI hasn’t replaced traditional ML — it has expanded what ML-based AI can do. Most real-world enterprise AI systems in 2026 combine both: classical ML for forecasting, fraud detection and recommendations, plus LLMs for natural-language interfaces, document understanding and content generation.

Machine learning and AI: summary

If someone asks you “What’s the difference between artificial intelligence and machine learning?” — here’s the short answer:

1. Artificial intelligence is the broad goal of building systems that simulate human intelligence — they reason, perceive, decide or act in ways that previously required a person.
2. Machine learning is one way to achieve AI: instead of writing rules, you let the system learn patterns from data. It powers most of today’s predictive AI — fraud detection, recommendations, demand forecasting.
3. Deep learning is a subset of machine learning based on neural networks. It dominates image, speech and language tasks.
4. Generative AI and LLMs are a recent branch of deep learning focused on producing new content — and the part of AI that most people now experience daily through tools like ChatGPT.

Different problems call for different tools. A spam filter doesn’t need an LLM; a customer-support chatbot probably does; a fraud-detection system likely combines both. The right question is rarely “AI or ML?” — it’s “which technique fits this specific problem, this data, and this risk profile?”

If you’d like help mapping a real business problem to the right approach — classical ML, deep learning, generative AI, or a combination — book a 30-minute call with our team and we’ll talk through it.

References

[1] Roberto Iriondo. Machine Learning (ML) vs. Artificial Intelligence (AI) — Crucial Differences. Oct 16, 2018. URL: https://pub.towardsai.net/differences-between-ai-and-machine-learning-and-why-it-matters-1255b182fc6. 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: https://www.theverge.com/2019/3/5/18251326/ai-startups-europe-fake-40-percent-mmc-report. Accessed May 26, 2020.

[3] Thinkautomation. Types of AI: distinguishing between weak, strong, and super AI. URL: https://www.thinkautomation.com/bots-and-ai/types-of-ai-distinguishing-between-weak-strong-and-super-ai/. Accessed May 26, 2020.