Artificial Intelligence vs Machine Learning vs Deep Learning
Artificial Intelligence vs. Machine Learning vs. Deep Learning: What’s the Difference?
The terms “machine learning”, “deep learning” and “artificial intelligence” are buzzwords that everyone seems to be talking about these days. What do these terms mean? They are often used interchangeably, which isn’t right. This article will help you understand what these terms mean and how they are different.
Evolution of Artificial Intelligence
The birth of the term “Artificial Intelligence” can be traced back to 1956 when it was formally coined by John McCarthy. Back then, the term was used to describe machines that would possess the same characteristics of human intelligence. This concept of AI, also known as “General AI”, is still a “concept” and not something we’ve been able to practically pull off — at least not yet.
Today, with AI technologies, we can perform specific tasks or solve certain problems as well as or better than, what we humans can. Recent advancements in algorithm, high-end computing power and storage facilities are enabling the creation of machines that can do more than we’ve ever imagined. The exponential increase in the size of the data that’s generated today is one of the reasons too.
Now that we have more data than our human brains can handle, AI systems are being built that can solve more complex problems and make more accurate predictions. Examples include virtual assistants like Siri and Cortana, voice-based personal assistants like Amazon Echo and Google Home, and many more.
Artificial Intelligence is the Umbrella Term
When we come to understand how AI, machine learning (ML), and deep learning (DL) are related, the easiest way is to visualize them in the form of concentric circles where, AI — the very first concept or idea — is the largest circle; the second circle is machine learning, and finally, the third circle — which fits inside both the circles — is deep learning.
In short, AI is the broader concept of machines mimicking human capabilities, while machine learning is a subset. Now, the question is what led to the creation of machine learning?
In order for AI systems to improve into more robust versions, researchers started exploring if these systems could learn from data and get better with experience. Thus, machine learning was born. These new systems differed from older AI systems in their ability to learn and improve over time when exposed to new data. One of the major application of machine learning has been to improve computer vision, which is the ability of a machine to recognize an object in an image or video.