Most ML algorithms are trained on static datasets to generate predictive models, thus ML algorithms only contribute to the development of a certain part of artificial intelligence.
Today’s hottest disruptive technologies are changing the business landscape: machine learning (ML) and artificial intelligence (AI).
Almost all of us have heard of or read about them, but do we really know what they are all about ?
These companies are trying to leverage advanced algorithms and the explosive growth in digital data and computing power to enable collaboration and natural interaction between humans and machines.
However, the public and the media still have a lot of confusion about what machine learning (ML) and artificial intelligence (AI) are.
Machine learning is a term that emerged after the birth of artificial intelligence. The two terms are often used synonymously, and in some cases are even regarded as discrete, parallel developments.
In fact, machine learning is to artificial intelligence what neurons are to the human brain.
Let's start with machine learning.
Roberto Iriondo, editor of the Machine Learning Department at Carnegie Mellon University in Pennsylvania, says that machine learning is a branch of artificial intelligence.
As computer scientist and machine learning pioneer Tom M. Mitchell coined, “machine learning is the study of computer algorithms that allow computer programs to improve automatically through experience.”
For example, if you feed a machine learning model your favorite songs, along with the corresponding audio data (instruments, beats, genres, etc.), it will be able to automatically generate a system that suggests music you might like—just like Netflix, Spotify, and other companies do.
If a digital payment company wants to detect fraud or potential fraud in its system, it can use machine learning tools for this purpose. Computational algorithms built into computer models process all transactions occurring on the digital platform, discover patterns in the dataset, and point out any anomalies detected within those patterns.
Iriondo said : "In a simple example, if you load a machine learning program that contains a fairly large dataset of X-ray images along with descriptions (such as symptoms), it will be able to (or possibly automatically) assist with subsequent data analysis on the X-ray images."
Machine learning models examine each image in a dataset and find common patterns in images labeled with similar indicators. Deep learning is a subset of machine learning.
On the other hand, artificial intelligence is a broad and general term that refers to the attempt to enable computers to think like humans, to simulate various things that humans do, and ultimately to solve problems in a better and faster way than we do.
Artificial intelligence encompasses a wide range of tasks; it is a system in itself, not just a standalone data model. It includes a diverse array of tasks such as creative work, planning, walking, speaking, recognizing objects and sounds, and executing business transactions.
However, Theovan Kraay, cloud solutions architect (Advanced Analytics & AI) at Microsoft's Customer Success Group, says any attempt to define artificial intelligence is somewhat futile because we must first properly define "intelligence," a word with a wide variety of meanings.
“First of all, it’s interesting and important that the technology that was called artificial intelligence more than 20 years ago was almost zero different from traditional computer systems,” Van Clay said.
Today's artificial intelligence systems reflect a key difference between humans and traditional computer systems—humans are predictive machines.
Many of today's artificial intelligence systems, much like humans, are complex predictive machines.
“The more complex a machine is, the more accurate its predictions will be. Based on a complex set of data used to train various (ML) models and state-of-the-art artificial intelligence systems, they will be able to continuously learn from mistakes to improve the accuracy of their predictions , thus exhibiting something close to human intelligence,” he said.
Most ML algorithms are trained on static datasets to generate predictive models, thus ML algorithms only contribute to the development of a certain part of artificial intelligence.
Fifty years ago, chess programs were considered a form of artificial intelligence.
But today, AI in chess games is considered boring and outdated because it can be found on almost every computer.
Iriondo stated : "Today, the symbols of artificial intelligence are AI-powered interactive devices such as Google Home, Apple's Siri, and Amazon's Alexa, or multimedia video prediction systems that power Netflix, Amazon, and YouTube."
Compared to machine learning, artificial intelligence is a constantly evolving goal, and its definition changes as related technologies develop.
Iriondo joked : "It's possible that in a few decades, current innovative advances in artificial intelligence will be considered as boring as today's flip phones."
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