In recent years, the fields of artificial intelligence (AI), machine learning and deep learning have seen remarkable advancements, revolutionizing numerous industries. However, the terms machine learning and deep learning are often used interchangeably, which can lead to confusion. While they share similarities and are related, they represent different concepts and methodologies within the broader AI landscape.

Machine learning is a subset of AI that focuses on developing algorithms that allow computers to learn from and make predictions or decisions based on data. Unlike traditional programming, where explicit instructions are given to the computer, machine learning algorithms identify patterns and relationships within data to make informed predictions or decisions.

Deep learning is a specialized subset of machine learning that focuses on using neural networks with many layers to model complex patterns in data. While traditional machine learning algorithms often rely on feature engineering by humans, deep learning algorithms automatically learn hierarchical feature representations from raw data.

There are some key differences between machine learning and deep learning. To start, machine learning relies heavily on feature engineering, where domain experts manually select and design features from raw data to be used by the algorithms. Where deep learning automatically learns feature representations from raw data, reducing the need for manual feature engineering.

Machine learning can work effectively with smaller datasets, especially with well-engineered features. Where deep learning requires large amounts of data to train effectively, as it needs to learn complex patterns and representations.

While machine learning and deep learning are related fields within AI, they have distinct characteristics and applications. Machine learning encompasses a broad range of algorithms that learn from data, often relying on human-engineered features. In contrast, deep learning uses multi-layered neural networks to automatically learn complex representations from raw data, excelling at tasks involving unstructured data and requiring large datasets.

Understanding the differences between these two approaches is crucial for selecting the right method for a given problem. As technology advances, both machine learning and deep learning will continue to evolve, driving innovation across various industries and shaping the future of AI.

The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

Melody K. Smith

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

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.