Pandata Blog

AI design and development for high risk industries

Artificial Intelligence vs. Machine Learning vs. Deep Learning: What’s the Difference? 

What is the difference between artificial intelligence, machine learning, and deep learning? How are they different? How are they similar?

There are a slew of terms and jargon within the AI and data science industry. And this evolving language can get overwhelming—especially when some sources use the words interchangeably. So let’s break this down…

What Is Artificial Intelligence? 

Artificial intelligence (AI) is software that can learn, reason, and adapt based on a large variety of data types. It learns to recognize and react to patterns, emulating traditionally human tasks, such as understanding language, recommending business actions, and synthesizing large amounts of information. AI helps humans best by learning and then performing repetitive tasks that depend on large amounts of information.

Some examples of AI include social media monitoring tools, Netflix show recommendations, customer chatbots, and self-driving cars, however there are examples of AI in nearly every industry today.  

What Is Machine Learning?

Machine learning (ML) is learning performed by a computer on its own without explicit instructions or programming from a human. Machine learning algorithms allow computers to learn automatically and autonomously from data in order to perform tasks, and improve at those tasks, after they’ve been trained by a human. Machine learning is most commonly used for prediction or optimization. It can also be used to mine data to recognize patterns.

Machine learning may be used in tasks like image recognition, speech recognition, and identifying patterns in medical scans. 

What Is Deep Learning? 

Deep learning is a type of machine learning based on artificial neural networks that mimic the architecture of the human brain. Deep learning uses multiple layers of computer processing to extract progressively higher-level features from data. If a situation calls for it, deep learning can also outperform classical AI methods and provide state-of-the-art performance. Deep learning is most useful with sequential data, image data, and learning from simulated environments.

Some of the most popular deep learning terms are recurrent neural networks (RNNs), convolutional neural networks (CNNs), and long short term memorial networks (LSTMs). 

The Difference Between AI, ML, and Deep Learning

AI, ML, and deep learning are three terms that go from broad and basic, to narrow and advanced.

AI is the broadest term and indicates we’re giving machines the ability to perform intelligent tasks typically reserved for humans.

ML is a subset of AI that enables machines to learn and improve on their own without explicit human programming.

Deep learning is a very advanced type of ML that seeks to teach machines how to think and learn even better than standard ML by mimicking the structures of the human brain.Whether you are talking about AI, ML or deep learning, the one thing they all have in common is that they should be designed, developed and used ethically (AI without human bias, for example). 

Gain More Expert Insight

Stay up-to-date on the latest in trusted AI and data science by subscribing to our Voices of Trusted AI monthly digest. It’s a once-per-month email that contains helpful trusted AI resources, reputable information and actionable tips straight from data science experts themselves.

voices of trusted AI email digest

Nicole Ponstingle McCaffrey is the COO and AI Translator at Pandata.