Simple descriptions of how Artificial Intelligence, Machine Learning, and Deep Learning differ from one another.
The phrase “artificial intelligence” is one that we are all familiar with. After all, films like The Terminator, The Matrix, and Ex Machina have made it a popular topic of discussion (a personal favourite of mine). However, you might have recently become aware of other terms like “Deep Learning” and “Machine Learning,” which are occasionally used in the same sentence as artificial intelligence. As a result, it may be difficult to tell the difference between artificial intelligence, machine learning, and deep learning.
Let me first quickly define artificial intelligence (AI), machine learning (ML), and deep learning (DL), then discuss how they differ. Following that, I’ll go over how the Internet of Things and artificial intelligence are inextricably linked, with a number of technological advancements coming together at once to pave the way for a boom in both fields.
How do Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) differ from one another?
John McCarthy first used the term artificial intelligence (AI) in 1956 to describe machines that can carry out tasks resembling those of human intelligence. This may sound broad, but it encompasses skills like organizing, comprehending language, identifying objects and sounds, learning, and problem-solving.
AI can be divided into two groups:
- General Artificial Intelligence
- Narrow Artificial Intelligence
1. General Artificial Intelligence
All of the traits of human intelligence, including thinking, acting, and learning like humans, would be present in general AI.
2. Narrow Artificial Intelligence
Narrow AI mimics some aspects of human intelligence and excels at them, but it falls short in other areas. A narrow AI system would be one that does well at image recognition but does nothing else.
The term “ability to learn without being explicitly programmed” was first used by Arthur Samuel in 1959, not long after AI. You see, artificial intelligence (AI) can be created without the use of machine learning, but doing so would require coding millions of lines of code with intricate rules and decision-trees.
Therefore, machine learning is a way of “training” an algorithm so that it can figure out how, as opposed to hard-coding software routines with specific instructions to complete a particular task. Massive amounts of data are fed to the algorithm during “training,” allowing it to make adjustments and advance.
To give an example, machine learning has been used to make drastic improvements to computer vision (the ability of a machine to recognize an object in an image or video). You gather hundreds of thousands or even millions of pictures and then have humans tag them. For example, the humans might tag pictures that have a cat in them versus those that do not. Then, the algorithm tries to build a model that can accurately tag a picture as containing a cat or not as well as a human. Once the accuracy level is high enough, the machine has now “learned” what a cat looks like.
Among the many methods for machine learning, deep learning is one. Inductive logic programming, clustering, reinforcement learning, Bayesian networks, and other techniques are examples of additional methods.
The brain’s structure and operation, specifically the interconnection of numerous neurons, served as inspiration for deep learning. Algorithms that resemble the biological makeup of the brain are known as artificial neural networks (ANNs).
In ANNs, there are “neurons” which have discrete layers and connections to other “neurons”. Each layer picks out a specific feature to learn, such as curves/edges in image recognition. It’s this layering that gives deep learning its name, depth is created by using multiple layers as opposed to a single layer.