Artificial intelligence (AI) and machine learning (ML) are constantly being used interchangeably in the tech landscape. But are they the same thing?
The answer is No. They both combine machines with the study of cognitive science in an attempt to mimick how the human brain performs thought, learning, and mental calculations. However, they have distinct qualities that invariably make them two separate concepts. Let’s take a look.
AI is the field of computer science that aims to study and create machines that can exhibit human behavior independently, without human control.
ML is a process that allows machines to learn from data without explicit directions, using algorithms. It essentially involves an algorithm—a formula representing the relationship between certain variables—and a model, which is a program that can find patterns or make predictions on new, incoming data.
AI is a field of computer science, the same way cybersecurity, software engineering, human-computer interaction, data science, and many more are fields of computer science. ML, on the other hand, is a process in which computers learn, and is a subset of the AI umbrella.
“Weak AI” is AI trained to perform very specific tasks, and is the most commonly used AI in our everyday products. Amazon Alexa, IBM Watson, and Tesla Autonomous Driving Software all fall under this type.
“Strong AI” is mostly theoretical. It obtains the same (or greater) computational capabilities as a human, with self-awareness and the ability to plan for the future. An example of strong AI would be the fictional program Jarvis in Marvel’s Iron Man.
There are many different learning methods for ML algorithms, but they all boil down to three overarching methods.
Supervised learning is when models are trained on labeled input data, and the outputs are compared to the target (or desired) outcome, and used to determine how well the model performed.
Supervised learning has been used to detect spam. Given emails labeled as “spam” or “not spam,” it learns what would be considered spam. When given new emails, the model will then label them as spam or not and will be scored based on accuracy.
Unsupervised learning is used to describe a relationship within data. Instead of aiming for a target, unsupervised learning intakes unlabeled data and determines what relationship all the data elements may have.
Clustering is a form of unsupervised learning. When given data, the model will cluster or group data based on similarities it determines. An example of clustering is its use in grouping retail products. Given product description data, the unsupervised learning model will group the data based on features it determines on its own. It could end up grouping products by color or size or some other feature entirely.
Reinforcement learning doesn’t train on input data at all. Instead, it’s given a goal to achieve and actions it’s allowed to perform. The feedback received will tell the algorithm if it has reached the goal or not.
You can see an example of this in a game where a computer agent is given the rules of a game, a point system, and a goal to get the highest score. Through its trial and error of moves, it will eventually learn how to achieve the highest score possible.
Within ML, specific subsets focus on different learning designs. The two most well-known subsets are neural networks and deep learning.
Neural networks is a branch of ML whose design mimics how human brain neurons process and pass on information. They’re made up of layers of nodes in which each node holds weight or value and passes data to other nodes in the next layer with little human interference.
They’re usually used to solve problems and detect patterns on unstructured, non-linear, or otherwise complex data.
Deep learning is what we call the group of algorithms that use neural networks to solve problems. Specifically, it uses a neural network with at least three layers. In basic ML, humans prepare the data in some way before feeding it to the model.
Deep learning, however, takes in mostly unprocessed data and learns for itself which features are important in making correct predictions.
An amazing example of deep learning is its use in AI-powered chatbots. These chatbots use a combination of natural language processing (NLP) and deep learning to understand human language.
NLP is a branch of AI devoted to teaching machines how to understand text and voice, and to respond on their own, ultimately mimicking human conversation. For AI-powered chatbots, deep learning can be used to assist in NLP goals by helping NLP systems learn the meaning of words in real time.
Breaking It Down
The main but subtle difference between AI and ML is mostly hierarchy. AI is a study and field of computer science that encompasses any technology that attempts to mimic or surpass human intelligence. ML is a subset of the AI field, and describes a process of how machines can learn patterns and make predictions on data.