Artificial Intelligence vs. Machine Learning vs. Deep Learning
Machine learning and artificial intelligence (AI) are all the rage these days — but with all the buzzwords swirling around them, it's easy to get lost and not see the difference between hype and reality. For example, just because an algorithm is used to calculate information doesn’t mean the label "machine learning" or "artificial intelligence" should be applied.
Before we can even define AI or machine learning, though, I want to take a step back and define a concept that is at the core of both AI and machine learning: algorithm.
What Is an Algorithm?
An algorithm is a set of rules to be followed when solving problems. In machine learning, algorithms take in data and perform calculations to find an answer. The calculations can be very simple or they can be more on the complex side. Algorithms should deliver the correct answer in the most efficient manner. What good is an algorithm if it takes longer than a human would to analyze the data? What good is it if it provides incorrect information?
Algorithms need to be trained to learn how to classify and process information. The efficiency and accuracy of the algorithm are dependent on how well the algorithm was trained. Using an algorithm to calculate something does not automatically mean machine learning or AI was being used. All squares are rectangles, but not all rectangles are squares.
Unfortunately, today, we often see the machine learning and AI buzzwords being thrown around to indicate that an algorithm was used to analyze data and make a prediction. Using an algorithm to predict an outcome of an event is not machine learning. Using the outcome of your prediction to improve future predictions is.
Artificial Intelligence vs. Machine Learning vs. Deep Learning
AI and machine learning are often used interchangeably, especially in the realm of big data. But these aren’t the same thing, and it is important to understand how these can be applied differently.
Artificial intelligence is a broader concept than machine learning, which addresses the use of computers to mimic the cognitive functions of humans. When machines carry out tasks based on algorithms in an “intelligent” manner, that is AI. Machine learning is a subset of AI and focuses on the ability of machines to receive a set of data and learn for themselves, changing algorithms as they learn more about the information they are processing.
Training computers to think like humans is achieved partly through the use of neural networks. Neural networks are a series of algorithms modeled after the human brain. Just as the brain can recognize patterns and help us categorize and classify information, neural networks do the same for computers. The brain is constantly trying to make sense of the information it is processing, and to do this, it labels and assigns items to categories. When we encounter something new, we try to compare it to a known item to help us understand and make sense of it. Neural networks do the same for computers.
Benefits of neural networks:
· Extract meaning from complicated data
· Detect trends and identify patterns too complex for humans to notice
· Learn by example
· Speed advantages
Deep learning goes yet another level deeper and can be considered a subset of machine learning. The concept of deep learning is sometimes just referred to as "deep neural networks," referring to the many layers involved. A neural network may only have a single layer of data, while a deep neural network has two or more. The layers can be seen as a nested hierarchy of related concepts or decision trees. The answer to one question leads to a set of deeper related questions.
Deep learning networks need to see large quantities of items in order to be trained. Instead of being programmed with the edges that define items, the systems learn from exposure to millions of data points. An early example of this is the Google Brain learning to recognize cats after being shown over ten million images. Deep learning networks do not need to be programmed with the criteria that define items; they are able to identify edges through being exposed to large amounts of data.
Data Is at the Heart of the Matter
Whether you are using an algorithm, artificial intelligence, or machine learning, one thing is certain: if the data being used is flawed, then the insights and information extracted will be flawed. What is data cleansing?
“The process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect or irrelevant parts of the data and then replacing, modifying or deleting the dirty or coarse data.”
And according to the CrowdFlower Data Science report, data scientists spend the majority of their time cleansing data — and surprisingly this is also their least favorite part of their job. Despite this, it is also the most important part, as the output can’t be trusted if the data hasn’t been cleansed.
For AI and machine learning to continue to advance, the data driving the algorithms and decisions need to be high-quality. If the data can’t be trusted, how can the insights from the data be trusted?
Difference between Artificial Intelligence, Machine Learning
and Deep Learning
The rise of autonomous vehicles, natural language processing, predictive maintenance, and even chess robots has come with its own jargon. The terms artificial intelligence, machine learning, and deep learning are fairly familiar to people working in the field services sector. These technologies make field service automation possible. They power field service software. But what exactly distinguishes one from the other?
The Sky is the Limit
Think of the evolution of artificial intelligence as an umbrella. Machine learning and deep learning both fall under the umbrella of artificial intelligence. And in that order. Without AI there would be no machine learning. And machine learning has given birth to deep learning. However, it might be most logical to turn that umbrella upside down, because with deep learning, the sky is the limit.
One easy way to understand the difference between these three types of intelligence and learning is to draw a parallel to the age-old and very familiar analog training and education process.
➔ Artificial intelligence is like teaching a student directly the information that you want them to learn.
➔ Machine intelligence is like giving a student a book and allowing them to learn and process the information on their own.
➔ The process of deep learning is the same as machine learning, except in this case the student is capable of learning from mistakes made and constantly improving.
The students in the case of AI, ML, and DL are machines. And the books are data. An endless flow of data that is either fed to the machine, in the case of AI, or that the machine retrieves from external sources like the Internet, sensors, etc. in the case of ML and DL. Here is a more concrete explanation of each of the three.
Artificial Intelligence (AI)
AI is used to denote machines that imitate human cognitive abilities like problem solving and learning or other skills that necessitate language and speech and strategic thinking. AI applications make it possible for machines to perform certain human tasks with the same skill level or better. And the era of big data is making AI ever more crucial. With an infinite number of data points and constant generation of new data, it will soon be impossible for the human mind to sift, sort, analyze, assess and arrive at a logical conclusion. And this is in regards to mundane tasks, like scheduling appointments, detecting software errors or machine malfunctions, and managing a gig economy workforce. In fact, many successful field service providers are already relying heavily on AI to effectively navigate these tasks.
Machine learning is the next logical step in AI. Two realizations propelled ML forward: the idea that machines could learn how to learn and the Internet. Teaching machines is a cumbersome job. However, providing them access to the endless source of data that is the Internet so they can learn for themselves has opened up immeasurable opportunities. Like deep learning.
Deep learning differs from machine learning in that machines are capable of learning beyond the data that is available to them. It involves the ability to analyze and assess information to make logical conclusions, determine solutions, and learn from errors. So the more data a machines receives, the more it is capable of learning and the smarter it gets. And though the artificial neural networks responsible for this technology have been around since the 1950s, extensive developments in the last decade have starkly improved the learning curve. The most common current applications are voice and image recognition. However, the level of data analysis possible will make many predictive applications possible. This includes anything from massive improvements in predictive maintenance and safer autonomous vehicles, to predicting illnesses or recidivism.
Where to Now?
Looking at the strides made from the inception of AI in the 1950s to today, we can track an obvious surge in developments and applications. More has occurred in the past ten years than in the 50 preceding them. As more and more businesses embrace the digital transformation and switch to automated processes, we will see even more innovation in this field to meet a growing demand. The possibilities are endless.