Artificial intelligence (AI) is often discussed as though it is a single technology. In reality, AI is an umbrella encompassing many approaches designed to enable computers to perform tasks that traditionally required human intelligence. Machine learning sits beneath that umbrella, and deep learning is a specialized and increasingly powerful branch of machine learning.

Understanding the distinction helps explain why deep learning has become so important to the rapid evolution of AI.

Machine learning enables computers to identify patterns in data and use those patterns to make predictions or decisions without being explicitly programmed for every possible scenario. A machine learning model might analyze historical sales data to forecast demand, identify suspicious financial transactions or recommend products based on previous purchases.

Traditional machine learning often relies heavily on human expertise to determine which characteristics, or features, within the data are most relevant. Data scientists select and prepare those features before training the model.

Deep learning takes the process further. It uses multilayered artificial neural networks, computational structures loosely inspired by the way neurons in the human brain process information. The “deep” in deep learning refers to the many layers through which data passes as the system learns increasingly complex patterns.

Instead of requiring humans to identify every important feature in advance, deep learning models can learn useful representations directly from large volumes of data. For example, rather than being explicitly told which visual characteristics distinguish a dog from a cat, a deep learning system can learn those distinctions by analyzing thousands or millions of labeled images.

That capability makes deep learning particularly effective for complex, unstructured information such as images, audio, video and natural language.

Deep learning is not new. Some of its underlying concepts date back decades. What has changed dramatically is the environment surrounding it.

The explosion of digital data has given deep learning models enormous amounts of information from which to learn. At the same time, advances in computing power, cloud infrastructure and specialized processors have made training increasingly large neural networks practical.

The recent surge in generative AI has pushed deep learning even further into everyday life.

Large language models, image generators, speech recognition systems and many other generative AI applications rely heavily on deep neural networks. Technologies that once belonged primarily in research laboratories are now embedded in search engines, smartphones, healthcare applications, creative tools and business systems.

AI has also helped democratize access. Organizations no longer necessarily need massive internal research teams to experiment with deep learning. Cloud services, pretrained models, APIs and open-source tools allow developers and businesses to build on existing models rather than starting from scratch.

That accessibility creates opportunities, but it also introduces risks. Deep learning models can reproduce biases found in their training data, generate inaccurate information and operate in ways that can be difficult to explain. Their appetite for data and computing resources also raises questions about privacy, intellectual property, energy consumption and responsible governance.

The distinction between machine learning and deep learning may sound technical, but its implications are increasingly practical. Machine learning taught computers to learn from data. Deep learning dramatically increased the complexity of what they could learn.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, where smarter AI starts with structured, meaningful, well-governed knowledge.