Unless you’ve been living under a rock for the past decades, you’ve probably heard about Artificial Intelligence, Machine Learning, and the Internet of Things before. These three are the next big thing in technology, but they’re often misunderstood as interchangeable terms. It’s worth noting that these three are unique in each of their own ways, though the distinction between them can also be quite confusing.
What is the difference between Artificial Intelligence, Machine Learning, and the Internet of Things? How do they relate to one another? Here’s a quick and dirty guide for beginners on the delineation of the said terms.
Artificial Intelligence (AI) is an umbrella term for machines that are able to carry out tasks by themselves with human-level intelligence. AI is designed to mimic human-like behavior and to automate routine processes. The concept has been in use for quite a while now, starting with the early machines such as calculators and cash registers.
Put simply, machines or computers with AI capabilities are explicitly taught what to do and how to do them, just like how dogs are trained to sit and heel by command. Some notable examples of AI in today’s world are smart cars, chatbots, smart home devices, and digital personal assistants like Siri and Cortana.
As more and more technological advancements are introduced, and with the advent of the Internet, the idea of teaching machines to learn for themselves emerged; hence, the birth of Machine Learning.
Machine Learning (ML) is a subset of AI that provides machines and computers the ability to learn by itself without being explicitly programmed. Rather than teaching a machine exactly what to do, ML allows a machine to think for itself and have a judgement call on its actions based on sample data provided to it via the Internet.
ML essentially works on a system of probability, paired with feedback loops, to enable the learning mechanism. When data is fed to an ML-capable device through the Internet, the device classifies the information based on a series of algorithms in order to carry out a task or predict actions.
In simple terms, ML allows a machine to make subjective and intelligent human-like decisions based on the information it gathers from a data source, or Big Data. Think of the difference between commanding a dog to eat at a certain time of the day, and with allowing a dog to eat when he or she is hungry regardless of what time it is. Examples of ML in our everyday lives include stock market predictor apps, ridesharing apps like Uber, and the spam filters in our email.
Machine Learning is the mechanism behind most of the exciting advances in technology nowadays, and this was all made possible through the Internet.
The Internet of Things (IoT) is the interconnection of devices through the Internet. It is the technology that enables smart devices to communicate with one another by sending and receiving data. The IoT does not limit itself to smart devices as well; it also connects individual components of a machine, such as the ink level indicator of a printer and the jet engine of a plane.
IoT is the string that connects AI and ML together, in that it serves as a treasure trove of data with which AI and ML can use in order for their program to work. AI, ML, and IoT thrives in a symbiotic relationship wherein one cannot properly work without the other.
To wrap things up, Artificial Intelligence is the broader term of the three, where Machine Learning is a subset. Hence, all types of ML are considered AI, but not all AI is considered ML. The Internet provides the data for ML to work, while the Internet of Things is a network of physical smart devices that contain embedded technology (that is, AI and ML) which enables smart devices to communicate with one another.
tags: ai, artificial intelligence, internet of things, iot, latest tech, machine learning, ml
article written by Kristenne Q.