Machine learning, artificial intelligence. AI/ML are the terms people use to describe “smart machines.”
But are AI and ML the same? We will provide the answer along with possible use cases for the trendy technology.
Article Contents
What is AI?
AI, or Artificial intelligence, is a branch of computer science. As the name suggests, it seeks to build solutions that mimic human intelligence.
Such systems seek to cover activities like understanding natural language, reading patterns, solving problems, and even creative ones — writing stories or composing music.
The main features of AI are:
- Perception: Interpreting sensory inputs — like vision or sound.
- Reasoning: Making decisions based on the given data.
- Learning: Improving over time by analyzing feedback.
- Autonomy: Completing tasks with no-to-minimal human oversight.
What is ML?
Machine learning is a part of AI. It helps machines learn from data. Thus, they can make decisions on their own and don’t need to be programmed for every step.
Think of it like showing computer examples, much like how people learn from experience.
The main categories of ML are as follows:
- Supervised learning: training an ML model with clean, labeled data.
- Unsupervised learning: training with unlabeled data.
- Reinforcement learning: the model learns through trial and error.
AI vs. ML: Similarities and Differences
As you’ve already understood, AI and ML overlap. Here’s what makes them similar:
- Purpose: Both aim to make machines smarter.
- Data-driven: Both rely on data to learn, improve, and function.
- Automation: Both strive to automate tasks that would require human involvement.
- Pattern recognition: Both excel in identifying patterns in large datasets.
- Efficiency: Both seek to enhance efficiency and reduce human error in tasks.
Let’s now take a look at where these two differ:
- Scope: AI covers a broader scope, namely all forms of intelligence: language recognition, robotics, problem-solving, and so on. ML, in turn, is used to develop algorithms that learn from data.
- Focus: AI app development aims for general intelligence. ML, in turn, focuses on specific tasks — for example, image recognition, fraud detection, or trend analysis.
- Complexity: AI is highly complex as it often combines multiple AI techniques to work properly. ML’s complexity depends on the task, which can be either simple or complex.
- Learning: While AI depends on data, it doesn’t always need to learn from it — it can use a rule-based system instead. In contrast, ML is all about learning from data.
- Flexibility: AI can include both ML and rule-based models, while ML is solely focused on data-driven learning.
- Autonomy: AI can simulate higher levels of autonomy and reasoning, while ML applications are typically narrower in scope, with limited decision-making autonomy
ML vs. AI: When to Use
If you’re planning a project requiring advanced features like AI or ML, you need to decide which tech will suit you most.
Here are the examples of tasks and the tools best fitted for them:
| Use Case | Best Suited for AI | Best Suited for ML |
| Building a virtual assistant (like Siri or Alexa) | Yes | Maybe |
| Predicting stock prices | No | Yes |
| Automating customer support | Yes | Maybe |
| Detecting credit card fraud | No | Yes |
| Image recognition | Yes | Yes |
| Recommending products | No | Yes |
| Sentiment analysis for social media | Maybe | Yes |
| Autonomous driving | Yes | Maybe |
| Personalizing marketing campaigns | Maybe | Yes |
| Optimizing supply chain logistics | Yes | Yes |
| Predictive maintenance for machinery | No | Yes |
| Spam email filtering | No | Yes |
To Conclude
Indeed, AI and ML are two sides of the same coin — yet with a subtle but important distinction between them.
Artificial intelligence is the general concept of creating intelligent systems, while ML is more of a tool within AI that allows these very systems to learn and advance.
If you’re still unsure which tech matches your project or simply have questions left, you can contact our team at S-PRO.
With profound expertise in both AI and ML, we can help you build and launch your MVP, test its viability, and move on with a full-scale intelligent solution.
