Machine Learning vs Deep Learning: What’s The Difference?

In recent years, artificial intelligence has evolved from a niche field of computer science to powering tools and services that most of us interact with every day. Looking around the room in which I am writing, I see a phone that uses AI for many of its applications and a smart speaker that can only be interacted with via an AI-powered voice interface. The impact on industry has been even more pervasive; manufacturing, logistics, healthcare, finance, and education are deploying AI technologies in many areas.

But what exactly do we mean by AI and the related terms machine learning and deep learning? They are often used interchangeably but they are not the same. Understanding the difference is useful for executives and HR professionals looking to take advantage of these technologies in their business. A basic understanding of the way AI is used will also help businesses to recruit executives with the expertise to implement AI initiatives. 

How Do We Define Artificial Intelligence?

Artificial Intelligence is a broad field that includes any software that gives computers human (or animal)-like intelligence. Those capabilities are constrained within narrow boundaries: there’s no such thing as a general AI yet. In essence, AI gives machines the ability to spot patterns in data and react according to predetermined goals with varying levels of flexibility.

What is Machine Learning

Machine learning is a subset of AI that focuses on creating algorithms that can ingest data, learn from it, and make decisions based on what has been learned. One of the most common examples of machine learning is recommendation engines. Netflix knows what you have watched and what other people have watched. Its recommendation engine can ingest that data, use it to learn your preferences, and make suggestions based on those preferences. 

Machine learning algorithms are complex and difficult to code because the developer has to tell the algorithm how the data it ingests relates to the decisions it is supposed to make. Traditional machine learning is like complex clockwork, every cog and spring has to be designed in advance and carefully combined to produce the desired outcome.

What is Deep Learning

Deep learning is a subfield of machine learning that, instead of relying on traditional algorithms, uses deep neural nets that can learn on their own. A neural net works similarly to the neurons in the human brain; each neuron responds to signals, which can cause other neurons to fire depending on their characteristics. Early neural nets used thin layers of neurons, but deep neural nets use many layers.

Machine Learning Vs. Deep Learning

The key difference between traditional machine learning and deep learning is that neural nets don’t have to be told which features they should use to classify data. Instead, they’re given huge sets of training data and the neurons react until the whole network is aligned such that the inputs provoke the desired output. Google is a leader in this field. One of the most impressive early demonstrations of deep learning was Google’s ability to automatically identify cats in photos. To train it, Google had to show the neural network over ten million pictures of cats.

A more impressive achievement is AlphaGo, which learned how to play Go so well that it beats the best humans. It’s important to note that no one taught AlphaGo how to play Go. It was not programmed with the rules of Go in the way a traditional chess computer is programmed with the rules of chess. It learned by itself by ingesting data from a massive number of games. AlphaGo Zero, a more recent integration, learned to play Go by playing games against itself, and the results were even more impressive. A consequence of this method is that no one really knows how a deep learning neural network does what it does.

Deep learning is a significant step forward in the state of the art for artificial intelligence, and it is already being widely deployed throughout industry in areas as diverse as image recognition, signal processing, AR and VR, network optimization, cyber security, and more. In the coming years, it will be applied to a widening number of applications. Businesses that proactively recruit executives with AI expertise and leadership skills will be in a strong position to exploit the competitive benefits of AI, machine learning, and deep learning.