AI is a complex field of study that continually evolves as time progresses. Over the past year alone, we’ve seen AI assistants take over the majority of online searches, and facial recognition make waves. That said, it’s understandable for some readers who encounter AI jargon like Machine Learning and Deep Learning to get confused about what they truly mean. This post aims to clarify how these AI concepts differ through their definitions and examples.
Machine Learning vs. Deep Learning vs. Artificial Intelligence
Artificial Intelligence (also known as “machine intelligence”) is an umbrella term for an emerging type of technology or computer that can learn independently, reason out, and make decisions just like a human being. AI’s reach cuts across several industries, including manufacturing, healthcare, design, and marketing. AI is differentiated by approach and applications, which explains why you sometimes get different sub-branches per article or publication.
Machine Learning (ML) is the most common subset of AI by design or use case, and it has much broader applications than AI. That is the reason why you read more about ML than, let’s say, symbolic AI.
Deep Learning is a machine learning technique that teaches computers to imitate what the human brain does. It is another popular buzzword recently in the AI segment and is essentially an enhanced version of ML.
What are Some Machine Learning and Deep Learning Differences?
Let’s take a closer look at their definitions and distinguishing characteristics.
Machine Learning Characteristics
This is an AI application where the system is pre-trained using structured or labeled data. As a result, the system learns to accomplish tasks on its own and improves itself based on experience over time.
If you teach an AI system to recognize a voice command, for example, its agent may learn to respond similarly to semantically related commands. One thing to bear in mind, though, when it comes to ML is that, with it, the desired outcome is predictable, whereas, in deep learning, the result leans on the more unpredictable side.
Deep Learning Characteristics
Deep learning is a type of ML that relies on artificial neural networks (ANNs) or connectionist systems. ANNs are akin to the neurons inside the human brain and their learning mechanism. The algorithms in ANNs are structured in layers (i.e., input and output layers) to process information and learn.
ANNs learn by analyzing examples to accomplish tasks, rather than following a linear set of instructions. However, vast volumes of data (i.e., big data), acquired through data mining, are needed to train ANNs to develop efficiency and minimize errors. Because ANNs require large quantities of data, they also require more computational power than ML. Deep learning uses graphic processing units (GPUs) with multiple cores rather than central processing units (CPUs). CPUs are usually enough for most ML tasks.
What are Some Examples of Machine Learning vs. Deep Learning?
Famous examples of ML are the algorithms used by popular media streaming services like Spotify and Netflix. These algorithms comb through countless user profiles and preferences to serve new content recommendations. New lending models in finance also use ML to effectively evaluate loan applicants based on their credit history, type of loan, and borrower profile.
With deep learning involved, the lending model would be able to derive predictions based on seemingly irrelevant data on the loan applicant. The deep learning agent would mine data from various sources and databases to spot associations between loaning times, seasons, trends, and so forth.
Google’s AlphaGo is a famous example of a deep learning application that can predict a player’s moves. Its training involved an analysis of a database of 60 million moves for the game “Go,” along with expert players’ techniques.