AI vs ML: What's the Difference?

Learn how to make AI work for you

AI won’t take your job, but a person using AI might. That’s why 1,000,000+ professionals read The Rundown AI – the free newsletter that keeps you updated on the latest AI news and teaches you how to use it in just 5 minutes a day.

Sign up to start learning.

Artificial intelligence (AI) and machine learning (ML) are buzzwords that are often used interchangeably, but are they the same? To understand the relationship between AI and ML, let’s break it down step by step.

What is Artificial Intelligence (AI)?

AI refers to systems that mimic or exceed human capabilities such as reasoning, discovering, and inferring. Essentially, AI aims to replicate human intelligence, enabling machines to perform tasks like problem-solving, learning, and understanding language.

AI covers a broad spectrum of fields, including:

• Natural Language Processing (NLP): Understanding and generating human language.

• Computer Vision: Interpreting and analyzing visual data.

• Robotics: Enabling motion and physical interaction, such as walking, lifting, or assembling objects.

• Speech and Audio Processing: Converting text to speech and recognizing sounds.

What is Machine Learning (ML)?

ML is a subset of AI focused on enabling systems to learn from data without explicit programming. Instead of hard-coding instructions, ML models are trained to make predictions or decisions by analyzing patterns in data.

Key types of ML:

1. Supervised Learning: Learning with labeled data where the outcomes are predefined.

2. Unsupervised Learning: Exploring patterns in unlabeled data to find hidden structures.

3. Deep Learning (DL): A specialized subset of ML that uses multi-layered neural networks to process complex data.

Deep learning, while highly powerful, can sometimes operate as a "black box," meaning its decision-making process isn't always transparent.

The Venn Diagram of AI, ML, and DL

If we think of this relationship as a Venn diagram:

• AI is the largest set, encompassing all efforts to simulate human intelligence.

• ML is a subset of AI, representing techniques that allow systems to learn from data.

• DL is a smaller subset within ML, focused on neural networks and deep architectures.

In short, AI includes ML, but ML is not the entirety of AI.

AI vs ML vs DL: The Right Perspective

It’s not "AI vs ML" or "AI = ML." Instead, ML is one approach within the broader field of AI. Similarly, deep learning, while groundbreaking, is just one piece of the puzzle.

AI is about creating systems that can perform human-like tasks. ML provides the tools for systems to improve themselves using data. Together, they are transforming industries and reshaping how we live and work.

So next time you hear these terms, remember: ML is a way to achieve AI, and DL is a deeper dive into ML!